id	sid	tid	token	lemma	pos
ajst-7806	1	1	academic	academic	ADJ
ajst-7806	1	2	journal	journal	NOUN
ajst-7806	1	3	of	of	ADP
ajst-7806	1	4	science	science	NOUN
ajst-7806	1	5	and	and	CCONJ
ajst-7806	1	6	technology	technology	NOUN
ajst-7806	1	7	issn	issn	NOUN
ajst-7806	1	8	:	:	PUNCT
ajst-7806	1	9	2771	2771	NUM
ajst-7806	1	10	-	-	SYM
ajst-7806	1	11	3032	3032	NUM
ajst-7806	1	12	|	|	NOUN
ajst-7806	1	13	vol	vol	NOUN
ajst-7806	1	14	.	.	PROPN
ajst-7806	2	1	5	5	NUM
ajst-7806	2	2	,	,	PUNCT
ajst-7806	2	3	no	no	INTJ
ajst-7806	2	4	.	.	NOUN
ajst-7806	2	5	3	3	NUM
ajst-7806	2	6	,	,	PUNCT
ajst-7806	2	7	2023	2023	NUM
ajst-7806	2	8	137	137	NUM
ajst-7806	2	9	improved	improve	VERB
ajst-7806	2	10	regional	regional	ADJ
ajst-7806	2	11	proposal	proposal	NOUN
ajst-7806	2	12	generation	generation	NOUN
ajst-7806	2	13	and	and	CCONJ
ajst-7806	2	14	proposal	proposal	NOUN
ajst-7806	2	15	selection	selection	NOUN
ajst-7806	2	16	method	method	NOUN
ajst-7806	2	17	for	for	ADP
ajst-7806	2	18	weakly	weakly	ADJ
ajst-7806	2	19	supervision	supervision	NOUN
ajst-7806	2	20	object	object	NOUN
ajst-7806	2	21	detection	detection	NOUN
ajst-7806	2	22	yujiao	yujiao	PROPN
ajst-7806	2	23	wang	wang	PROPN
ajst-7806	2	24	,	,	PUNCT
ajst-7806	2	25	hua	hua	PROPN
ajst-7806	2	26	huo	huo	PROPN
ajst-7806	2	27	school	school	PROPN
ajst-7806	2	28	of	of	ADP
ajst-7806	2	29	henan	henan	PROPN
ajst-7806	2	30	university	university	PROPN
ajst-7806	2	31	of	of	ADP
ajst-7806	2	32	science	science	NOUN
ajst-7806	2	33	and	and	CCONJ
ajst-7806	2	34	technology	technology	NOUN
ajst-7806	2	35	,	,	PUNCT
ajst-7806	2	36	luoyang	luoyang	PROPN
ajst-7806	2	37	471000	471000	NUM
ajst-7806	2	38	,	,	PUNCT
ajst-7806	2	39	china	china	PROPN
ajst-7806	2	40	abstract	abstract	PROPN
ajst-7806	2	41	:	:	PUNCT
ajst-7806	2	42	in	in	ADP
ajst-7806	2	43	recent	recent	ADJ
ajst-7806	2	44	years	year	NOUN
ajst-7806	2	45	,	,	PUNCT
ajst-7806	2	46	object	object	NOUN
ajst-7806	2	47	detection	detection	NOUN
ajst-7806	2	48	has	have	AUX
ajst-7806	2	49	made	make	VERB
ajst-7806	2	50	great	great	ADJ
ajst-7806	2	51	progress	progress	NOUN
ajst-7806	2	52	with	with	ADP
ajst-7806	2	53	the	the	DET
ajst-7806	2	54	continuous	continuous	ADJ
ajst-7806	2	55	development	development	NOUN
ajst-7806	2	56	of	of	ADP
ajst-7806	2	57	deep	deep	ADJ
ajst-7806	2	58	neural	neural	ADJ
ajst-7806	2	59	network	network	NOUN
ajst-7806	2	60	.	.	PUNCT
ajst-7806	3	1	at	at	ADP
ajst-7806	3	2	present	present	ADJ
ajst-7806	3	3	,	,	PUNCT
ajst-7806	3	4	there	there	PRON
ajst-7806	3	5	are	be	VERB
ajst-7806	3	6	many	many	ADJ
ajst-7806	3	7	different	different	ADJ
ajst-7806	3	8	fully	fully	ADV
ajst-7806	3	9	supervised	supervise	VERB
ajst-7806	3	10	object	object	NOUN
ajst-7806	3	11	detection	detection	NOUN
ajst-7806	3	12	algorithms	algorithm	NOUN
ajst-7806	3	13	in	in	ADP
ajst-7806	3	14	the	the	DET
ajst-7806	3	15	field	field	NOUN
ajst-7806	3	16	of	of	ADP
ajst-7806	3	17	computer	computer	NOUN
ajst-7806	3	18	vision	vision	NOUN
ajst-7806	3	19	,	,	PUNCT
ajst-7806	3	20	which	which	PRON
ajst-7806	3	21	are	be	AUX
ajst-7806	3	22	basically	basically	ADV
ajst-7806	3	23	saturated	saturate	VERB
ajst-7806	3	24	,	,	PUNCT
ajst-7806	3	25	while	while	SCONJ
ajst-7806	3	26	object	object	VERB
ajst-7806	3	27	detection	detection	NOUN
ajst-7806	3	28	in	in	ADP
ajst-7806	3	29	a	a	DET
ajst-7806	3	30	weakly	weakly	ADV
ajst-7806	3	31	supervised	supervised	ADJ
ajst-7806	3	32	manner	manner	NOUN
ajst-7806	3	33	is	be	AUX
ajst-7806	3	34	more	more	ADV
ajst-7806	3	35	challenging	challenging	ADJ
ajst-7806	3	36	than	than	ADP
ajst-7806	3	37	strongly	strongly	ADV
ajst-7806	3	38	supervised	supervise	VERB
ajst-7806	3	39	object	object	NOUN
ajst-7806	3	40	detection	detection	NOUN
ajst-7806	3	41	.	.	PUNCT
ajst-7806	4	1	since	since	SCONJ
ajst-7806	4	2	nowadays	nowadays	ADV
ajst-7806	4	3	mature	mature	ADJ
ajst-7806	4	4	object	object	NOUN
ajst-7806	4	5	detection	detection	NOUN
ajst-7806	4	6	algorithms	algorithm	NOUN
ajst-7806	4	7	rely	rely	VERB
ajst-7806	4	8	heavily	heavily	ADV
ajst-7806	4	9	on	on	ADP
ajst-7806	4	10	strongly	strongly	ADV
ajst-7806	4	11	labeled	label	VERB
ajst-7806	4	12	datasets	dataset	NOUN
ajst-7806	4	13	,	,	PUNCT
ajst-7806	4	14	but	but	CCONJ
ajst-7806	4	15	strong	strong	ADJ
ajst-7806	4	16	labeled	label	VERB
ajst-7806	4	17	datasets	dataset	NOUN
ajst-7806	4	18	are	be	AUX
ajst-7806	4	19	very	very	ADV
ajst-7806	4	20	expensive	expensive	ADJ
ajst-7806	4	21	and	and	CCONJ
ajst-7806	4	22	require	require	VERB
ajst-7806	4	23	huge	huge	ADJ
ajst-7806	4	24	datasets	dataset	NOUN
ajst-7806	4	25	to	to	PART
ajst-7806	4	26	support	support	VERB
ajst-7806	4	27	in	in	ADP
ajst-7806	4	28	order	order	NOUN
ajst-7806	4	29	to	to	PART
ajst-7806	4	30	train	train	VERB
ajst-7806	4	31	a	a	DET
ajst-7806	4	32	better	well	ADJ
ajst-7806	4	33	object	object	NOUN
ajst-7806	4	34	detection	detection	NOUN
ajst-7806	4	35	model	model	NOUN
ajst-7806	4	36	,	,	PUNCT
ajst-7806	4	37	weakly	weakly	ADV
ajst-7806	4	38	supervised	supervised	ADJ
ajst-7806	4	39	object	object	NOUN
ajst-7806	4	40	detection	detection	NOUN
ajst-7806	4	41	has	have	AUX
ajst-7806	4	42	received	receive	VERB
ajst-7806	4	43	more	more	ADJ
ajst-7806	4	44	and	and	CCONJ
ajst-7806	4	45	more	more	ADJ
ajst-7806	4	46	attention	attention	NOUN
ajst-7806	4	47	.	.	PUNCT
ajst-7806	5	1	in	in	ADP
ajst-7806	5	2	this	this	DET
ajst-7806	5	3	paper	paper	NOUN
ajst-7806	5	4	,	,	PUNCT
ajst-7806	5	5	a	a	DET
ajst-7806	5	6	new	new	ADJ
ajst-7806	5	7	module	module	NOUN
ajst-7806	5	8	can	can	AUX
ajst-7806	5	9	be	be	AUX
ajst-7806	5	10	embedded	embed	VERB
ajst-7806	5	11	in	in	ADP
ajst-7806	5	12	the	the	DET
ajst-7806	5	13	framework	framework	NOUN
ajst-7806	5	14	of	of	ADP
ajst-7806	5	15	weakly	weakly	ADJ
ajst-7806	5	16	supervised	supervised	ADJ
ajst-7806	5	17	object	object	NOUN
ajst-7806	5	18	detection	detection	NOUN
ajst-7806	5	19	,	,	PUNCT
ajst-7806	5	20	three	three	NUM
ajst-7806	5	21	modules	module	NOUN
ajst-7806	5	22	are	be	AUX
ajst-7806	5	23	introduced	introduce	VERB
ajst-7806	5	24	into	into	ADP
ajst-7806	5	25	the	the	DET
ajst-7806	5	26	weakly	weakly	ADJ
ajst-7806	5	27	supervised	supervised	ADJ
ajst-7806	5	28	object	object	NOUN
ajst-7806	5	29	detection	detection	NOUN
ajst-7806	5	30	framework	framework	NOUN
ajst-7806	5	31	,	,	PUNCT
ajst-7806	5	32	which	which	PRON
ajst-7806	5	33	is	be	AUX
ajst-7806	5	34	used	use	VERB
ajst-7806	5	35	to	to	PART
ajst-7806	5	36	generate	generate	VERB
ajst-7806	5	37	high	high	ADJ
ajst-7806	5	38	-	-	PUNCT
ajst-7806	5	39	quality	quality	NOUN
ajst-7806	5	40	proposals	proposal	NOUN
ajst-7806	5	41	and	and	CCONJ
ajst-7806	5	42	screen	screen	VERB
ajst-7806	5	43	these	these	DET
ajst-7806	5	44	proposals	proposal	NOUN
ajst-7806	5	45	,	,	PUNCT
ajst-7806	5	46	and	and	CCONJ
ajst-7806	5	47	finally	finally	ADV
ajst-7806	5	48	selecting	select	VERB
ajst-7806	5	49	more	more	ADV
ajst-7806	5	50	accurate	accurate	ADJ
ajst-7806	5	51	proposal	proposal	NOUN
ajst-7806	5	52	boxes	box	NOUN
ajst-7806	5	53	that	that	PRON
ajst-7806	5	54	are	be	AUX
ajst-7806	5	55	beneficial	beneficial	ADJ
ajst-7806	5	56	for	for	ADP
ajst-7806	5	57	subsequent	subsequent	ADJ
ajst-7806	5	58	training	training	NOUN
ajst-7806	5	59	,	,	PUNCT
ajst-7806	5	60	and	and	CCONJ
ajst-7806	5	61	demonstrate	demonstrate	VERB
ajst-7806	5	62	their	their	PRON
ajst-7806	5	63	effectiveness	effectiveness	NOUN
ajst-7806	5	64	on	on	ADP
ajst-7806	5	65	the	the	DET
ajst-7806	5	66	pascal	pascal	ADJ
ajst-7806	5	67	voc2007	voc2007	NOUN
ajst-7806	5	68	and	and	CCONJ
ajst-7806	5	69	pascal	pascal	ADJ
ajst-7806	5	70	voc2012	voc2012	NOUN
ajst-7806	5	71	datasets	dataset	NOUN
ajst-7806	5	72	,	,	PUNCT
ajst-7806	5	73	in	in	ADP
ajst-7806	5	74	which	which	PRON
ajst-7806	5	75	this	this	DET
ajst-7806	5	76	paper	paper	NOUN
ajst-7806	5	77	achieves	achieve	VERB
ajst-7806	5	78	a	a	DET
ajst-7806	5	79	significant	significant	ADJ
ajst-7806	5	80	improvement	improvement	NOUN
ajst-7806	5	81	over	over	ADP
ajst-7806	5	82	the	the	DET
ajst-7806	5	83	existing	exist	VERB
ajst-7806	5	84	classic	classic	ADJ
ajst-7806	5	85	weakly	weakly	ADJ
ajst-7806	5	86	supervised	supervised	ADJ
ajst-7806	5	87	object	object	NOUN
ajst-7806	5	88	detection	detection	NOUN
ajst-7806	5	89	algorithms	algorithm	NOUN
ajst-7806	5	90	with	with	ADP
ajst-7806	5	91	significant	significant	ADJ
ajst-7806	5	92	improvements	improvement	NOUN
ajst-7806	5	93	.	.	PUNCT
ajst-7806	6	1	keywords	keyword	NOUN
ajst-7806	6	2	:	:	PUNCT
ajst-7806	6	3	weakly	weakly	ADV
ajst-7806	6	4	supervised	supervised	ADJ
ajst-7806	6	5	,	,	PUNCT
ajst-7806	6	6	object	object	VERB
ajst-7806	6	7	detection	detection	NOUN
ajst-7806	6	8	,	,	PUNCT
ajst-7806	6	9	proposals	proposal	NOUN
ajst-7806	6	10	.	.	PUNCT
ajst-7806	7	1	1	1	X
ajst-7806	7	2	.	.	X
ajst-7806	7	3	introduction	introduction	NOUN
ajst-7806	7	4	one	one	NUM
ajst-7806	7	5	of	of	ADP
ajst-7806	7	6	the	the	DET
ajst-7806	7	7	most	most	ADV
ajst-7806	7	8	fundamental	fundamental	ADJ
ajst-7806	7	9	tasks	task	NOUN
ajst-7806	7	10	under	under	ADP
ajst-7806	7	11	the	the	DET
ajst-7806	7	12	direction	direction	NOUN
ajst-7806	7	13	of	of	ADP
ajst-7806	7	14	computer	computer	NOUN
ajst-7806	7	15	vision	vision	NOUN
ajst-7806	7	16	,	,	PUNCT
ajst-7806	7	17	object	object	VERB
ajst-7806	7	18	detection	detection	NOUN
ajst-7806	8	1	[	[	X
ajst-7806	8	2	3,4,8,9,17,18,19,21,27,35	3,4,8,9,17,18,19,21,27,35	NUM
ajst-7806	8	3	]	]	PUNCT
ajst-7806	8	4	,	,	PUNCT
ajst-7806	8	5	has	have	AUX
ajst-7806	8	6	made	make	VERB
ajst-7806	8	7	remarkable	remarkable	ADJ
ajst-7806	8	8	progress	progress	NOUN
ajst-7806	8	9	with	with	ADP
ajst-7806	8	10	the	the	DET
ajst-7806	8	11	continuous	continuous	ADJ
ajst-7806	8	12	development	development	NOUN
ajst-7806	8	13	of	of	ADP
ajst-7806	8	14	convolutional	convolutional	ADJ
ajst-7806	8	15	neural	neural	ADJ
ajst-7806	8	16	networks	network	NOUN
ajst-7806	8	17	[	[	X
ajst-7806	8	18	10,13,14	10,13,14	X
ajst-7806	8	19	]	]	PUNCT
ajst-7806	8	20	in	in	ADP
ajst-7806	8	21	re	re	NOUN
ajst-7806	8	22	-	-	NOUN
ajst-7806	8	23	cent	cent	NOUN
ajst-7806	8	24	years	year	NOUN
ajst-7806	8	25	,	,	PUNCT
ajst-7806	8	26	and	and	CCONJ
ajst-7806	8	27	its	its	PRON
ajst-7806	8	28	accuracy	accuracy	NOUN
ajst-7806	8	29	has	have	AUX
ajst-7806	8	30	reached	reach	VERB
ajst-7806	8	31	a	a	DET
ajst-7806	8	32	very	very	ADV
ajst-7806	8	33	good	good	ADJ
ajst-7806	8	34	level	level	NOUN
ajst-7806	8	35	.	.	PUNCT
ajst-7806	9	1	simply	simply	ADV
ajst-7806	9	2	put	put	VERB
ajst-7806	9	3	,	,	PUNCT
ajst-7806	9	4	object	object	NOUN
ajst-7806	9	5	detection	detection	NOUN
ajst-7806	9	6	is	be	AUX
ajst-7806	9	7	based	base	VERB
ajst-7806	9	8	on	on	ADP
ajst-7806	9	9	image	image	NOUN
ajst-7806	9	10	classification	classification	NOUN
ajst-7806	9	11	by	by	ADP
ajst-7806	9	12	framing	frame	VERB
ajst-7806	9	13	objects	object	NOUN
ajst-7806	9	14	in	in	ADP
ajst-7806	9	15	the	the	DET
ajst-7806	9	16	form	form	NOUN
ajst-7806	9	17	of	of	ADP
ajst-7806	9	18	enclosing	enclosing	NOUN
ajst-7806	9	19	frames	frame	NOUN
ajst-7806	9	20	,	,	PUNCT
ajst-7806	9	21	that	that	ADV
ajst-7806	9	22	is	is	ADV
ajst-7806	9	23	,	,	PUNCT
ajst-7806	9	24	locating	locate	VERB
ajst-7806	9	25	and	and	CCONJ
ajst-7806	9	26	classifying	classify	VERB
ajst-7806	9	27	example	example	NOUN
ajst-7806	9	28	images	image	NOUN
ajst-7806	9	29	.	.	PUNCT
ajst-7806	10	1	at	at	ADP
ajst-7806	10	2	present	present	ADJ
ajst-7806	10	3	,	,	PUNCT
ajst-7806	10	4	these	these	DET
ajst-7806	10	5	object	object	NOUN
ajst-7806	10	6	detection	detection	NOUN
ajst-7806	10	7	algorithms	algorithm	NOUN
ajst-7806	10	8	rely	rely	VERB
ajst-7806	10	9	heavily	heavily	ADV
ajst-7806	10	10	on	on	ADP
ajst-7806	10	11	precisely	precisely	ADV
ajst-7806	10	12	annotated	annotate	VERB
ajst-7806	10	13	large	large	ADJ
ajst-7806	10	14	-	-	PUNCT
ajst-7806	10	15	scale	scale	NOUN
ajst-7806	10	16	datasets	dataset	NOUN
ajst-7806	10	17	[	[	X
ajst-7806	10	18	6,7,20,23,24	6,7,20,23,24	NUM
ajst-7806	10	19	]	]	PUNCT
ajst-7806	10	20	,	,	PUNCT
ajst-7806	10	21	and	and	CCONJ
ajst-7806	10	22	the	the	DET
ajst-7806	10	23	acquisition	acquisition	NOUN
ajst-7806	10	24	of	of	ADP
ajst-7806	10	25	such	such	ADJ
ajst-7806	10	26	instance	instance	NOUN
ajst-7806	10	27	-	-	PUNCT
ajst-7806	10	28	level	level	NOUN
ajst-7806	10	29	strongly	strongly	ADV
ajst-7806	10	30	labeled	label	VERB
ajst-7806	10	31	datasets	dataset	NOUN
ajst-7806	10	32	are	be	AUX
ajst-7806	10	33	very	very	ADV
ajst-7806	10	34	labor	labor	NOUN
ajst-7806	10	35	-	-	PUNCT
ajst-7806	10	36	intensive	intensive	ADJ
ajst-7806	10	37	and	and	CCONJ
ajst-7806	10	38	costly	costly	ADJ
ajst-7806	10	39	.	.	PUNCT
ajst-7806	11	1	in	in	ADP
ajst-7806	11	2	addition	addition	NOUN
ajst-7806	11	3	,	,	PUNCT
ajst-7806	11	4	the	the	DET
ajst-7806	11	5	strongly	strongly	ADV
ajst-7806	11	6	supervised	supervised	ADJ
ajst-7806	11	7	object	object	NOUN
ajst-7806	11	8	detection	detection	NOUN
ajst-7806	11	9	algorithms	algorithm	NOUN
ajst-7806	11	10	still	still	ADV
ajst-7806	11	11	have	have	VERB
ajst-7806	11	12	some	some	DET
ajst-7806	11	13	inevitable	inevitable	ADJ
ajst-7806	11	14	limitations	limitation	NOUN
ajst-7806	11	15	,	,	PUNCT
ajst-7806	11	16	such	such	ADJ
ajst-7806	11	17	as	as	ADP
ajst-7806	11	18	the	the	DET
ajst-7806	11	19	possibility	possibility	NOUN
ajst-7806	11	20	of	of	ADP
ajst-7806	11	21	inadvertently	inadvertently	ADV
ajst-7806	11	22	introducing	introduce	VERB
ajst-7806	11	23	labeling	labeling	NOUN
ajst-7806	11	24	noise	noise	NOUN
ajst-7806	11	25	during	during	ADP
ajst-7806	11	26	the	the	DET
ajst-7806	11	27	manual	manual	ADJ
ajst-7806	11	28	labeling	labeling	NOUN
ajst-7806	11	29	of	of	ADP
ajst-7806	11	30	data	datum	NOUN
ajst-7806	11	31	,	,	PUNCT
ajst-7806	11	32	which	which	PRON
ajst-7806	11	33	makes	make	VERB
ajst-7806	11	34	it	it	PRON
ajst-7806	11	35	more	more	ADV
ajst-7806	11	36	difficult	difficult	ADJ
ajst-7806	11	37	for	for	SCONJ
ajst-7806	11	38	the	the	DET
ajst-7806	11	39	detector	detector	NOUN
ajst-7806	11	40	to	to	PART
ajst-7806	11	41	learn	learn	VERB
ajst-7806	11	42	a	a	DET
ajst-7806	11	43	good	good	ADJ
ajst-7806	11	44	model	model	NOUN
ajst-7806	11	45	.	.	PUNCT
ajst-7806	12	1	therefore	therefore	ADV
ajst-7806	12	2	,	,	PUNCT
ajst-7806	12	3	researchers	researcher	NOUN
ajst-7806	12	4	have	have	AUX
ajst-7806	12	5	begun	begin	VERB
ajst-7806	12	6	to	to	PART
ajst-7806	12	7	explore	explore	VERB
ajst-7806	12	8	weakly	weakly	ADV
ajst-7806	12	9	supervised	supervised	ADJ
ajst-7806	12	10	object	object	NOUN
ajst-7806	12	11	detection	detection	NOUN
ajst-7806	12	12	that	that	PRON
ajst-7806	12	13	requires	require	VERB
ajst-7806	12	14	only	only	ADV
ajst-7806	12	15	image	image	NOUN
ajst-7806	12	16	-	-	PUNCT
ajst-7806	12	17	level	level	NOUN
ajst-7806	12	18	labeled	label	VERB
ajst-7806	12	19	data	datum	NOUN
ajst-7806	12	20	for	for	ADP
ajst-7806	12	21	training	training	NOUN
ajst-7806	12	22	,	,	PUNCT
ajst-7806	12	23	meaning	mean	VERB
ajst-7806	12	24	that	that	SCONJ
ajst-7806	12	25	the	the	DET
ajst-7806	12	26	dataset	dataset	NOUN
ajst-7806	12	27	no	no	ADV
ajst-7806	12	28	longer	long	ADV
ajst-7806	12	29	has	have	VERB
ajst-7806	12	30	precise	precise	ADJ
ajst-7806	12	31	bounding	bounding	NOUN
ajst-7806	12	32	box	box	NOUN
ajst-7806	12	33	annotations	annotation	NOUN
ajst-7806	12	34	,	,	PUNCT
ajst-7806	12	35	but	but	CCONJ
ajst-7806	12	36	only	only	ADJ
ajst-7806	12	37	annotations	annotation	NOUN
ajst-7806	12	38	of	of	ADP
ajst-7806	12	39	image	image	NOUN
ajst-7806	12	40	categories	category	NOUN
ajst-7806	12	41	.	.	PUNCT
ajst-7806	13	1	it	it	PRON
ajst-7806	13	2	is	be	AUX
ajst-7806	13	3	because	because	SCONJ
ajst-7806	13	4	of	of	ADP
ajst-7806	13	5	the	the	DET
ajst-7806	13	6	very	very	ADV
ajst-7806	13	7	simple	simple	ADJ
ajst-7806	13	8	and	and	CCONJ
ajst-7806	13	9	noisy	noisy	ADJ
ajst-7806	13	10	labeling	labeling	NOUN
ajst-7806	13	11	of	of	ADP
ajst-7806	13	12	their	their	PRON
ajst-7806	13	13	datasets	dataset	NOUN
ajst-7806	13	14	that	that	SCONJ
ajst-7806	13	15	although	although	SCONJ
ajst-7806	13	16	many	many	ADJ
ajst-7806	13	17	methods	method	NOUN
ajst-7806	13	18	[	[	X
ajst-7806	13	19	1,2,5,25,26,28,29	1,2,5,25,26,28,29	NUM
ajst-7806	13	20	,	,	PUNCT
ajst-7806	13	21	37	37	NUM
ajst-7806	13	22	,	,	PUNCT
ajst-7806	13	23	38	38	NUM
ajst-7806	13	24	]	]	PUNCT
ajst-7806	13	25	for	for	ADP
ajst-7806	13	26	weakly	weakly	ADJ
ajst-7806	13	27	supervised	supervised	ADJ
ajst-7806	13	28	object	object	NOUN
ajst-7806	13	29	detection	detection	NOUN
ajst-7806	13	30	have	have	AUX
ajst-7806	13	31	been	be	AUX
ajst-7806	13	32	proposed	propose	VERB
ajst-7806	13	33	,	,	PUNCT
ajst-7806	13	34	its	its	PRON
ajst-7806	13	35	performances	performance	NOUN
ajst-7806	13	36	are	be	AUX
ajst-7806	13	37	still	still	ADV
ajst-7806	13	38	far	far	ADV
ajst-7806	13	39	from	from	ADP
ajst-7806	13	40	those	those	PRON
ajst-7806	13	41	of	of	ADP
ajst-7806	13	42	strongly	strongly	ADV
ajst-7806	13	43	supervised	supervise	VERB
ajst-7806	13	44	object	object	NOUN
ajst-7806	13	45	detection	detection	NOUN
ajst-7806	13	46	.	.	PUNCT
ajst-7806	14	1	from	from	ADP
ajst-7806	14	2	the	the	DET
ajst-7806	14	3	recent	recent	ADJ
ajst-7806	14	4	work	work	NOUN
ajst-7806	14	5	,	,	PUNCT
ajst-7806	14	6	a	a	DET
ajst-7806	14	7	number	number	NOUN
ajst-7806	14	8	of	of	ADP
ajst-7806	14	9	approaches	approach	NOUN
ajst-7806	14	10	have	have	AUX
ajst-7806	14	11	been	be	AUX
ajst-7806	14	12	proposed	propose	VERB
ajst-7806	14	13	for	for	ADP
ajst-7806	14	14	solving	solve	VERB
ajst-7806	14	15	the	the	DET
ajst-7806	14	16	wsod	wsod	ADJ
ajst-7806	14	17	problem	problem	NOUN
ajst-7806	14	18	.	.	PUNCT
ajst-7806	15	1	when	when	SCONJ
ajst-7806	15	2	the	the	DET
ajst-7806	15	3	dataset	dataset	NOUN
ajst-7806	15	4	only	only	ADV
ajst-7806	15	5	has	have	VERB
ajst-7806	15	6	image	image	NOUN
ajst-7806	15	7	-	-	PUNCT
ajst-7806	15	8	level	level	NOUN
ajst-7806	15	9	annotations	annotation	NOUN
ajst-7806	15	10	,	,	PUNCT
ajst-7806	15	11	most	most	ADJ
ajst-7806	15	12	of	of	ADP
ajst-7806	15	13	them	they	PRON
ajst-7806	15	14	are	be	AUX
ajst-7806	15	15	formulated	formulate	VERB
ajst-7806	15	16	as	as	ADP
ajst-7806	15	17	a	a	DET
ajst-7806	15	18	multi	multi	ADJ
ajst-7806	15	19	-	-	ADJ
ajst-7806	15	20	instance	instance	ADJ
ajst-7806	15	21	learning	learning	NOUN
ajst-7806	15	22	problem	problem	NOUN
ajst-7806	15	23	.	.	PUNCT
ajst-7806	16	1	integrating	integrate	VERB
ajst-7806	16	2	the	the	DET
ajst-7806	16	3	idea	idea	NOUN
ajst-7806	16	4	of	of	ADP
ajst-7806	16	5	multi	multi	ADJ
ajst-7806	16	6	-	-	NOUN
ajst-7806	16	7	instance	instance	NOUN
ajst-7806	16	8	learning	learning	NOUN
ajst-7806	16	9	into	into	ADP
ajst-7806	16	10	cnn	cnn	PROPN
ajst-7806	16	11	can	can	AUX
ajst-7806	16	12	compensate	compensate	VERB
ajst-7806	16	13	for	for	ADP
ajst-7806	16	14	the	the	DET
ajst-7806	16	15	deficiencies	deficiency	NOUN
ajst-7806	16	16	of	of	ADP
ajst-7806	16	17	training	training	NOUN
ajst-7806	16	18	set	set	VERB
ajst-7806	16	19	labels	label	NOUN
ajst-7806	16	20	and	and	CCONJ
ajst-7806	16	21	improve	improve	VERB
ajst-7806	16	22	the	the	DET
ajst-7806	16	23	detection	detection	NOUN
ajst-7806	16	24	performance	performance	NOUN
ajst-7806	16	25	better	well	ADV
ajst-7806	16	26	.	.	PUNCT
ajst-7806	17	1	the	the	DET
ajst-7806	17	2	main	main	ADJ
ajst-7806	17	3	problem	problem	NOUN
ajst-7806	17	4	of	of	ADP
ajst-7806	17	5	weakly	weakly	ADJ
ajst-7806	17	6	supervised	supervised	ADJ
ajst-7806	17	7	object	object	NOUN
ajst-7806	17	8	detection	detection	NOUN
ajst-7806	17	9	lies	lie	VERB
ajst-7806	17	10	in	in	ADP
ajst-7806	17	11	the	the	DET
ajst-7806	17	12	poor	poor	ADJ
ajst-7806	17	13	localization	localization	NOUN
ajst-7806	17	14	accuracy	accuracy	NOUN
ajst-7806	17	15	due	due	ADP
ajst-7806	17	16	to	to	ADP
ajst-7806	17	17	the	the	DET
ajst-7806	17	18	lack	lack	NOUN
ajst-7806	17	19	of	of	ADP
ajst-7806	17	20	precise	precise	ADJ
ajst-7806	17	21	labels	label	NOUN
ajst-7806	17	22	.	.	PUNCT
ajst-7806	18	1	the	the	DET
ajst-7806	18	2	wraparound	wraparound	PROPN
ajst-7806	18	3	box	box	PROPN
ajst-7806	18	4	is	be	AUX
ajst-7806	18	5	overly	overly	ADV
ajst-7806	18	6	focused	focused	ADJ
ajst-7806	18	7	on	on	ADP
ajst-7806	18	8	the	the	DET
ajst-7806	18	9	part	part	NOUN
ajst-7806	18	10	of	of	ADP
ajst-7806	18	11	the	the	DET
ajst-7806	18	12	feature	feature	NOUN
ajst-7806	18	13	and	and	CCONJ
ajst-7806	18	14	the	the	DET
ajst-7806	18	15	ss	ss	NOUN
ajst-7806	19	1	[	[	X
ajst-7806	19	2	30	30	NUM
ajst-7806	19	3	]	]	PUNCT
ajst-7806	19	4	and	and	CCONJ
ajst-7806	19	5	eb	eb	PROPN
ajst-7806	19	6	[	[	X
ajst-7806	19	7	41	41	NUM
ajst-7806	19	8	]	]	X
ajst-7806	19	9	algorithms	algorithm	NOUN
ajst-7806	19	10	are	be	AUX
ajst-7806	19	11	generally	generally	ADV
ajst-7806	19	12	used	use	VERB
ajst-7806	19	13	in	in	ADP
ajst-7806	19	14	the	the	DET
ajst-7806	19	15	generation	generation	NOUN
ajst-7806	19	16	of	of	ADP
ajst-7806	19	17	the	the	DET
ajst-7806	19	18	proposed	propose	VERB
ajst-7806	19	19	boxes	box	NOUN
ajst-7806	19	20	,	,	PUNCT
ajst-7806	19	21	which	which	PRON
ajst-7806	19	22	is	be	AUX
ajst-7806	19	23	very	very	ADV
ajst-7806	19	24	time	time	NOUN
ajst-7806	19	25	consuming	consume	VERB
ajst-7806	19	26	.	.	PUNCT
ajst-7806	20	1	as	as	SCONJ
ajst-7806	20	2	shown	show	VERB
ajst-7806	20	3	in	in	ADP
ajst-7806	20	4	fig	fig	NOUN
ajst-7806	20	5	.	.	PUNCT
ajst-7806	21	1	1	1	NUM
ajst-7806	21	2	,	,	PUNCT
ajst-7806	21	3	this	this	PRON
ajst-7806	21	4	is	be	AUX
ajst-7806	21	5	the	the	DET
ajst-7806	21	6	classic	classic	ADJ
ajst-7806	21	7	problem	problem	NOUN
ajst-7806	21	8	that	that	PRON
ajst-7806	21	9	weakly	weakly	ADJ
ajst-7806	21	10	supervised	supervised	ADJ
ajst-7806	21	11	object	object	NOUN
ajst-7806	21	12	detection	detection	NOUN
ajst-7806	21	13	will	will	AUX
ajst-7806	21	14	encounter	encounter	VERB
ajst-7806	21	15	.	.	PUNCT
ajst-7806	22	1	figure	figure	VERB
ajst-7806	22	2	1	1	NUM
ajst-7806	22	3	.	.	PUNCT
ajst-7806	22	4	typical	typical	ADJ
ajst-7806	22	5	wsod	wsod	ADJ
ajst-7806	22	6	problem	problem	NOUN
ajst-7806	22	7	,	,	PUNCT
ajst-7806	22	8	you	you	PRON
ajst-7806	22	9	can	can	AUX
ajst-7806	22	10	see	see	VERB
ajst-7806	22	11	the	the	DET
ajst-7806	22	12	partial	partial	ADJ
ajst-7806	22	13	,	,	PUNCT
ajst-7806	22	14	correct	correct	ADJ
ajst-7806	22	15	and	and	CCONJ
ajst-7806	22	16	oversize	oversize	ADJ
ajst-7806	22	17	detection	detection	NOUN
ajst-7806	22	18	results	result	NOUN
ajst-7806	22	19	of	of	ADP
ajst-7806	22	20	an	an	DET
ajst-7806	22	21	object	object	NOUN
ajst-7806	22	22	instance	instance	NOUN
ajst-7806	22	23	from	from	ADP
ajst-7806	22	24	the	the	DET
ajst-7806	22	25	first	first	ADJ
ajst-7806	22	26	,	,	PUNCT
ajst-7806	22	27	second	second	ADJ
ajst-7806	22	28	and	and	CCONJ
ajst-7806	22	29	third	third	ADJ
ajst-7806	22	30	rows	row	NOUN
ajst-7806	22	31	respectively	respectively	ADV
ajst-7806	22	32	.	.	PUNCT
ajst-7806	23	1	both	both	DET
ajst-7806	23	2	oicr	oicr	ADJ
ajst-7806	24	1	[	[	X
ajst-7806	24	2	2	2	NUM
ajst-7806	24	3	]	]	PUNCT
ajst-7806	24	4	and	and	CCONJ
ajst-7806	24	5	pcl	pcl	PROPN
ajst-7806	24	6	[	[	X
ajst-7806	24	7	1	1	NUM
ajst-7806	24	8	]	]	PUNCT
ajst-7806	24	9	are	be	AUX
ajst-7806	24	10	weakly	weakly	ADV
ajst-7806	24	11	supervised	supervised	ADJ
ajst-7806	24	12	object	object	NOUN
ajst-7806	24	13	detection	detection	NOUN
ajst-7806	24	14	based	base	VERB
ajst-7806	24	15	on	on	ADP
ajst-7806	24	16	multiple	multiple	ADJ
ajst-7806	24	17	instance	instance	NOUN
ajst-7806	24	18	learning	learning	NOUN
ajst-7806	24	19	,	,	PUNCT
ajst-7806	24	20	and	and	CCONJ
ajst-7806	24	21	since	since	SCONJ
ajst-7806	24	22	they	they	PRON
ajst-7806	24	23	both	both	PRON
ajst-7806	24	24	use	use	VERB
ajst-7806	24	25	the	the	DET
ajst-7806	24	26	output	output	NOUN
ajst-7806	24	27	of	of	ADP
ajst-7806	24	28	the	the	DET
ajst-7806	24	29	initial	initial	ADJ
ajst-7806	24	30	object	object	NOUN
ajst-7806	24	31	detector	detector	NOUN
ajst-7806	24	32	as	as	ADP
ajst-7806	24	33	the	the	DET
ajst-7806	24	34	true	true	ADJ
ajst-7806	24	35	annotation	annotation	NOUN
ajst-7806	24	36	label	label	NOUN
ajst-7806	24	37	,	,	PUNCT
ajst-7806	24	38	their	their	PRON
ajst-7806	24	39	performance	performance	NOUN
ajst-7806	24	40	is	be	AUX
ajst-7806	24	41	very	very	ADV
ajst-7806	24	42	dependent	dependent	ADJ
ajst-7806	24	43	on	on	ADP
ajst-7806	24	44	the	the	DET
ajst-7806	24	45	accuracy	accuracy	NOUN
ajst-7806	24	46	of	of	ADP
ajst-7806	24	47	the	the	DET
ajst-7806	24	48	initial	initial	ADJ
ajst-7806	24	49	object	object	NOUN
ajst-7806	24	50	detection	detection	NOUN
ajst-7806	24	51	results	result	NOUN
ajst-7806	24	52	and	and	CCONJ
ajst-7806	24	53	do	do	AUX
ajst-7806	24	54	not	not	PART
ajst-7806	24	55	learn	learn	VERB
ajst-7806	24	56	the	the	DET
ajst-7806	24	57	key	key	ADJ
ajst-7806	24	58	step	step	NOUN
ajst-7806	24	59	of	of	ADP
ajst-7806	24	60	bounding	bound	VERB
ajst-7806	24	61	box	box	NOUN
ajst-7806	24	62	regression	regression	NOUN
ajst-7806	24	63	.	.	PUNCT
ajst-7806	25	1	wsod2	wsod2	NOUN
ajst-7806	26	1	[	[	X
ajst-7806	26	2	11	11	NUM
ajst-7806	26	3	]	]	PUNCT
ajst-7806	26	4	precisely	precisely	ADV
ajst-7806	26	5	builds	build	VERB
ajst-7806	26	6	on	on	ADP
ajst-7806	26	7	oicr	oicr	NOUN
ajst-7806	26	8	to	to	PART
ajst-7806	26	9	obtain	obtain	VERB
ajst-7806	26	10	the	the	DET
ajst-7806	26	11	initial	initial	ADJ
ajst-7806	26	12	object	object	NOUN
ajst-7806	26	13	bounding	bounding	NOUN
ajst-7806	26	14	box	box	NOUN
ajst-7806	26	15	,	,	PUNCT
ajst-7806	26	16	based	base	VERB
ajst-7806	26	17	on	on	ADP
ajst-7806	26	18	the	the	DET
ajst-7806	26	19	localization	localization	NOUN
ajst-7806	26	20	of	of	ADP
ajst-7806	26	21	each	each	DET
ajst-7806	26	22	proposed	propose	VERB
ajst-7806	26	23	bounding	bounding	NOUN
ajst-7806	26	24	box	box	NOUN
ajst-7806	26	25	,	,	PUNCT
ajst-7806	26	26	we	we	PRON
ajst-7806	26	27	put	put	VERB
ajst-7806	26	28	the	the	DET
ajst-7806	26	29	bottom	bottom	ADJ
ajst-7806	26	30	-	-	PUNCT
ajst-7806	26	31	up	up	ADP
ajst-7806	26	32	object	object	NOUN
ajst-7806	26	33	evidence	evidence	NOUN
ajst-7806	26	34	to	to	PART
ajst-7806	26	35	use	use	VERB
ajst-7806	26	36	,	,	PUNCT
ajst-7806	26	37	which	which	PRON
ajst-7806	26	38	will	will	AUX
ajst-7806	26	39	guide	guide	VERB
ajst-7806	26	40	the	the	DET
ajst-7806	26	41	conversion	conversion	NOUN
ajst-7806	26	42	from	from	ADP
ajst-7806	26	43	image	image	NOUN
ajst-7806	26	44	-	-	PUNCT
ajst-7806	26	45	level	level	NOUN
ajst-7806	26	46	to	to	ADP
ajst-7806	26	47	instance	instance	NOUN
ajst-7806	26	48	-	-	PUNCT
ajst-7806	26	49	level	level	NOUN
ajst-7806	26	50	annotation	annotation	NOUN
ajst-7806	26	51	.	.	PUNCT
ajst-7806	27	1	138	138	NUM
ajst-7806	27	2	figure	figure	NOUN
ajst-7806	27	3	2	2	NUM
ajst-7806	27	4	.	.	X
ajst-7806	27	5	weakly	weakly	ADJ
ajst-7806	27	6	supervised	supervised	ADJ
ajst-7806	27	7	object	object	NOUN
ajst-7806	27	8	detection	detection	NOUN
ajst-7806	27	9	using	use	VERB
ajst-7806	27	10	image	image	NOUN
ajst-7806	27	11	level	level	NOUN
ajst-7806	27	12	labels	label	NOUN
ajst-7806	27	13	as	as	ADP
ajst-7806	27	14	supervision	supervision	NOUN
ajst-7806	27	15	the	the	DET
ajst-7806	27	16	challenge	challenge	NOUN
ajst-7806	27	17	of	of	ADP
ajst-7806	27	18	weakly	weakly	ADJ
ajst-7806	27	19	supervised	supervised	ADJ
ajst-7806	27	20	object	object	NOUN
ajst-7806	27	21	detection	detection	NOUN
ajst-7806	27	22	is	be	AUX
ajst-7806	27	23	that	that	SCONJ
ajst-7806	27	24	the	the	DET
ajst-7806	27	25	dataset	dataset	NOUN
ajst-7806	27	26	is	be	AUX
ajst-7806	27	27	weakly	weakly	ADV
ajst-7806	27	28	labeled	label	VERB
ajst-7806	27	29	,	,	PUNCT
ajst-7806	27	30	with	with	ADP
ajst-7806	27	31	only	only	ADJ
ajst-7806	27	32	image	image	NOUN
ajst-7806	27	33	-	-	PUNCT
ajst-7806	27	34	level	level	NOUN
ajst-7806	27	35	labels	label	NOUN
ajst-7806	27	36	available	available	ADJ
ajst-7806	27	37	,	,	PUNCT
ajst-7806	27	38	as	as	SCONJ
ajst-7806	27	39	shown	show	VERB
ajst-7806	27	40	in	in	ADP
ajst-7806	27	41	fig	fig	NOUN
ajst-7806	27	42	.	.	PUNCT
ajst-7806	28	1	2	2	NUM
ajst-7806	28	2	.	.	PUNCT
ajst-7806	29	1	but	but	CCONJ
ajst-7806	29	2	we	we	PRON
ajst-7806	29	3	need	need	VERB
ajst-7806	29	4	to	to	PART
ajst-7806	29	5	train	train	VERB
ajst-7806	29	6	a	a	DET
ajst-7806	29	7	good	good	ADJ
ajst-7806	29	8	detector	detector	NOUN
ajst-7806	29	9	with	with	ADP
ajst-7806	29	10	such	such	DET
ajst-7806	29	11	a	a	DET
ajst-7806	29	12	dataset	dataset	NOUN
ajst-7806	29	13	,	,	PUNCT
ajst-7806	29	14	and	and	CCONJ
ajst-7806	29	15	the	the	DET
ajst-7806	29	16	result	result	NOUN
ajst-7806	29	17	of	of	ADP
ajst-7806	29	18	detection	detection	NOUN
ajst-7806	29	19	is	be	AUX
ajst-7806	29	20	to	to	PART
ajst-7806	29	21	get	get	VERB
ajst-7806	29	22	both	both	PRON
ajst-7806	29	23	category	category	NOUN
ajst-7806	29	24	information	information	NOUN
ajst-7806	29	25	and	and	CCONJ
ajst-7806	29	26	location	location	NOUN
ajst-7806	29	27	information	information	NOUN
ajst-7806	29	28	.	.	PUNCT
ajst-7806	30	1	the	the	DET
ajst-7806	30	2	limitation	limitation	NOUN
ajst-7806	30	3	of	of	ADP
ajst-7806	30	4	object	object	NOUN
ajst-7806	30	5	detection	detection	NOUN
ajst-7806	30	6	based	base	VERB
ajst-7806	30	7	on	on	ADP
ajst-7806	30	8	multiple	multiple	ADJ
ajst-7806	30	9	example	example	NOUN
ajst-7806	30	10	learning	learning	NOUN
ajst-7806	30	11	is	be	AUX
ajst-7806	30	12	that	that	SCONJ
ajst-7806	30	13	the	the	DET
ajst-7806	30	14	most	most	ADJ
ajst-7806	30	15	discriminative	discriminative	NOUN
ajst-7806	30	16	of	of	ADP
ajst-7806	30	17	all	all	DET
ajst-7806	30	18	instances	instance	NOUN
ajst-7806	30	19	can	can	AUX
ajst-7806	30	20	be	be	AUX
ajst-7806	30	21	easily	easily	ADV
ajst-7806	30	22	distinguished	distinguish	VERB
ajst-7806	30	23	,	,	PUNCT
ajst-7806	30	24	while	while	SCONJ
ajst-7806	30	25	making	make	VERB
ajst-7806	30	26	the	the	DET
ajst-7806	30	27	network	network	NOUN
ajst-7806	30	28	can	can	AUX
ajst-7806	30	29	easily	easily	ADV
ajst-7806	30	30	fall	fall	VERB
ajst-7806	30	31	into	into	ADP
ajst-7806	30	32	local	local	ADJ
ajst-7806	30	33	optima	optima	NOUN
ajst-7806	30	34	.	.	PUNCT
ajst-7806	31	1	so	so	ADV
ajst-7806	31	2	how	how	SCONJ
ajst-7806	31	3	to	to	PART
ajst-7806	31	4	generate	generate	VERB
ajst-7806	31	5	high	high	ADJ
ajst-7806	31	6	quality	quality	NOUN
ajst-7806	31	7	proposals	proposal	NOUN
ajst-7806	31	8	and	and	CCONJ
ajst-7806	31	9	which	which	DET
ajst-7806	31	10	method	method	NOUN
ajst-7806	31	11	to	to	PART
ajst-7806	31	12	use	use	VERB
ajst-7806	31	13	to	to	PART
ajst-7806	31	14	select	select	VERB
ajst-7806	31	15	high	high	ADJ
ajst-7806	31	16	quality	quality	NOUN
ajst-7806	31	17	proposals	proposal	NOUN
ajst-7806	31	18	becomes	become	VERB
ajst-7806	31	19	the	the	DET
ajst-7806	31	20	key	key	NOUN
ajst-7806	31	21	for	for	ADP
ajst-7806	31	22	weakly	weakly	ADJ
ajst-7806	31	23	supervised	supervised	ADJ
ajst-7806	31	24	object	object	NOUN
ajst-7806	31	25	detection	detection	NOUN
ajst-7806	31	26	.	.	PUNCT
ajst-7806	32	1	this	this	DET
ajst-7806	32	2	paper	paper	NOUN
ajst-7806	32	3	proposes	propose	VERB
ajst-7806	32	4	a	a	DET
ajst-7806	32	5	framework	framework	NOUN
ajst-7806	32	6	is	be	AUX
ajst-7806	32	7	based	base	VERB
ajst-7806	32	8	on	on	ADP
ajst-7806	32	9	oicr	oicr	NOUN
ajst-7806	33	1	[	[	X
ajst-7806	33	2	2	2	NUM
ajst-7806	33	3	]	]	PUNCT
ajst-7806	33	4	as	as	ADP
ajst-7806	33	5	a	a	DET
ajst-7806	33	6	baseline	baseline	NOUN
ajst-7806	33	7	and	and	CCONJ
ajst-7806	33	8	introduce	introduce	VERB
ajst-7806	33	9	three	three	NUM
ajst-7806	33	10	modules	module	NOUN
ajst-7806	33	11	for	for	ADP
ajst-7806	33	12	generating	generate	VERB
ajst-7806	33	13	proposals	proposal	NOUN
ajst-7806	33	14	and	and	CCONJ
ajst-7806	33	15	performing	perform	VERB
ajst-7806	33	16	proposal	proposal	NOUN
ajst-7806	33	17	screening	screening	NOUN
ajst-7806	33	18	.	.	PUNCT
ajst-7806	34	1	firstly	firstly	ADV
ajst-7806	34	2	,	,	PUNCT
ajst-7806	34	3	we	we	PRON
ajst-7806	34	4	generate	generate	VERB
ajst-7806	34	5	high	high	ADJ
ajst-7806	34	6	-	-	PUNCT
ajst-7806	34	7	quality	quality	NOUN
ajst-7806	34	8	proposals	proposal	NOUN
ajst-7806	34	9	specifically	specifically	ADV
ajst-7806	34	10	for	for	ADP
ajst-7806	34	11	weakly	weakly	ADJ
ajst-7806	34	12	supervised	supervised	ADJ
ajst-7806	34	13	object	object	NOUN
ajst-7806	34	14	detection	detection	NOUN
ajst-7806	34	15	,	,	PUNCT
ajst-7806	34	16	and	and	CCONJ
ajst-7806	34	17	for	for	ADP
ajst-7806	34	18	the	the	DET
ajst-7806	34	19	proposal	proposal	NOUN
ajst-7806	34	20	generation	generation	NOUN
ajst-7806	34	21	part	part	NOUN
ajst-7806	34	22	we	we	PRON
ajst-7806	34	23	choose	choose	VERB
ajst-7806	34	24	to	to	PART
ajst-7806	34	25	combine	combine	VERB
ajst-7806	34	26	the	the	DET
ajst-7806	34	27	selection	selection	NOUN
ajst-7806	34	28	search	search	NOUN
ajst-7806	34	29	algorithm	algorithm	NOUN
ajst-7806	35	1	[	[	X
ajst-7806	35	2	30	30	NUM
ajst-7806	35	3	]	]	PUNCT
ajst-7806	35	4	with	with	ADP
ajst-7806	35	5	an	an	DET
ajst-7806	35	6	improved	improved	ADJ
ajst-7806	35	7	version	version	NOUN
ajst-7806	35	8	of	of	ADP
ajst-7806	35	9	the	the	DET
ajst-7806	35	10	gradient	gradient	NOUN
ajst-7806	35	11	-	-	PUNCT
ajst-7806	35	12	weighted	weight	VERB
ajst-7806	35	13	class	class	NOUN
ajst-7806	35	14	activation	activation	NOUN
ajst-7806	35	15	-	-	PUNCT
ajst-7806	35	16	based	base	VERB
ajst-7806	35	17	mapping	mapping	NOUN
ajst-7806	36	1	[	[	X
ajst-7806	36	2	31	31	NUM
ajst-7806	36	3	]	]	PUNCT
ajst-7806	36	4	,	,	PUNCT
ajst-7806	36	5	and	and	CCONJ
ajst-7806	36	6	on	on	ADP
ajst-7806	36	7	top	top	NOUN
ajst-7806	36	8	of	of	ADP
ajst-7806	36	9	this	this	PRON
ajst-7806	36	10	we	we	PRON
ajst-7806	36	11	add	add	VERB
ajst-7806	36	12	an	an	DET
ajst-7806	36	13	improved	improved	ADJ
ajst-7806	36	14	attention	attention	NOUN
ajst-7806	36	15	module	module	NOUN
ajst-7806	36	16	to	to	PART
ajst-7806	36	17	extract	extract	VERB
ajst-7806	36	18	an	an	DET
ajst-7806	36	19	enhanced	enhance	VERB
ajst-7806	36	20	feature	feature	NOUN
ajst-7806	36	21	map	map	NOUN
ajst-7806	36	22	from	from	ADP
ajst-7806	36	23	the	the	DET
ajst-7806	36	24	cnn	cnn	PROPN
ajst-7806	36	25	,	,	PUNCT
ajst-7806	36	26	and	and	CCONJ
ajst-7806	36	27	then	then	ADV
ajst-7806	36	28	have	have	VERB
ajst-7806	36	29	roi	roi	NOUN
ajst-7806	36	30	pooling	pooling	NOUN
ajst-7806	36	31	to	to	PART
ajst-7806	36	32	process	process	VERB
ajst-7806	36	33	the	the	DET
ajst-7806	36	34	generated	generate	VERB
ajst-7806	36	35	regions	region	NOUN
ajst-7806	36	36	with	with	ADP
ajst-7806	36	37	a	a	DET
ajst-7806	36	38	combination	combination	NOUN
ajst-7806	36	39	of	of	ADP
ajst-7806	36	40	bottom	bottom	NOUN
ajst-7806	36	41	-	-	PUNCT
ajst-7806	36	42	up	up	NOUN
ajst-7806	36	43	and	and	CCONJ
ajst-7806	36	44	a	a	DET
ajst-7806	36	45	combination	combination	NOUN
ajst-7806	36	46	of	of	ADP
ajst-7806	36	47	two	two	NUM
ajst-7806	36	48	evidences	evidence	NOUN
ajst-7806	36	49	,	,	PUNCT
ajst-7806	36	50	bottom	bottom	NOUN
ajst-7806	36	51	-	-	PUNCT
ajst-7806	36	52	up	up	NOUN
ajst-7806	36	53	and	and	CCONJ
ajst-7806	36	54	top	top	ADJ
ajst-7806	36	55	-	-	PUNCT
ajst-7806	36	56	down	down	NOUN
ajst-7806	36	57	,	,	PUNCT
ajst-7806	36	58	is	be	AUX
ajst-7806	36	59	used	use	VERB
ajst-7806	36	60	to	to	PART
ajst-7806	36	61	filter	filter	VERB
ajst-7806	36	62	the	the	DET
ajst-7806	36	63	proposals	proposal	NOUN
ajst-7806	36	64	.	.	PUNCT
ajst-7806	37	1	it	it	PRON
ajst-7806	37	2	is	be	AUX
ajst-7806	37	3	also	also	ADV
ajst-7806	37	4	fed	feed	VERB
ajst-7806	37	5	into	into	ADP
ajst-7806	37	6	the	the	DET
ajst-7806	37	7	basic	basic	ADJ
ajst-7806	37	8	multiinstance	multiinstance	NOUN
ajst-7806	37	9	detector	detector	NOUN
ajst-7806	37	10	and	and	CCONJ
ajst-7806	37	11	k	k	ADJ
ajst-7806	37	12	-	-	PUNCT
ajst-7806	37	13	level	level	NOUN
ajst-7806	37	14	instance	instance	NOUN
ajst-7806	37	15	optimizer	optimizer	NOUN
ajst-7806	37	16	and	and	CCONJ
ajst-7806	37	17	bounding	bound	VERB
ajst-7806	37	18	box	box	NOUN
ajst-7806	37	19	regression	regression	NOUN
ajst-7806	37	20	branch	branch	NOUN
ajst-7806	37	21	for	for	ADP
ajst-7806	37	22	iterative	iterative	NOUN
ajst-7806	37	23	training	training	NOUN
ajst-7806	37	24	as	as	ADP
ajst-7806	37	25	a	a	DET
ajst-7806	37	26	way	way	NOUN
ajst-7806	37	27	to	to	PART
ajst-7806	37	28	improve	improve	VERB
ajst-7806	37	29	its	its	PRON
ajst-7806	37	30	performance	performance	NOUN
ajst-7806	37	31	.	.	PUNCT
ajst-7806	38	1	the	the	DET
ajst-7806	38	2	contributions	contribution	NOUN
ajst-7806	38	3	of	of	ADP
ajst-7806	38	4	this	this	DET
ajst-7806	38	5	paper	paper	NOUN
ajst-7806	38	6	are	be	AUX
ajst-7806	38	7	summarized	summarize	VERB
ajst-7806	38	8	as	as	SCONJ
ajst-7806	38	9	follows	follow	VERB
ajst-7806	38	10	:	:	PUNCT
ajst-7806	38	11	1.in	1.in	NUM
ajst-7806	38	12	the	the	DET
ajst-7806	38	13	proposal	proposal	NOUN
ajst-7806	38	14	generation	generation	NOUN
ajst-7806	38	15	module	module	NOUN
ajst-7806	38	16	,	,	PUNCT
ajst-7806	38	17	this	this	DET
ajst-7806	38	18	paper	paper	NOUN
ajst-7806	38	19	uses	use	VERB
ajst-7806	38	20	a	a	DET
ajst-7806	38	21	combination	combination	NOUN
ajst-7806	38	22	of	of	ADP
ajst-7806	38	23	grad	grad	ADJ
ajst-7806	38	24	cam++	cam++	PROPN
ajst-7806	38	25	based	base	VERB
ajst-7806	38	26	class	class	NOUN
ajst-7806	38	27	activation	activation	NOUN
ajst-7806	38	28	graph	graph	NOUN
ajst-7806	38	29	and	and	CCONJ
ajst-7806	38	30	selection	selection	NOUN
ajst-7806	38	31	search	search	NOUN
ajst-7806	38	32	algorithm	algorithm	NOUN
ajst-7806	38	33	,	,	PUNCT
ajst-7806	38	34	and	and	CCONJ
ajst-7806	38	35	incorporate	incorporate	VERB
ajst-7806	38	36	an	an	DET
ajst-7806	38	37	improved	improved	ADJ
ajst-7806	38	38	cbam	cbam	NOUN
ajst-7806	38	39	attention	attention	NOUN
ajst-7806	38	40	mechanism	mechanism	NOUN
ajst-7806	38	41	to	to	PART
ajst-7806	38	42	achieve	achieve	VERB
ajst-7806	38	43	better	well	ADJ
ajst-7806	38	44	results	result	NOUN
ajst-7806	38	45	making	make	VERB
ajst-7806	38	46	it	it	PRON
ajst-7806	38	47	possible	possible	ADJ
ajst-7806	38	48	to	to	PART
ajst-7806	38	49	generate	generate	VERB
ajst-7806	38	50	high	high	ADJ
ajst-7806	38	51	quality	quality	NOUN
ajst-7806	38	52	candidate	candidate	NOUN
ajst-7806	38	53	frames	frame	NOUN
ajst-7806	38	54	in	in	ADP
ajst-7806	38	55	the	the	DET
ajst-7806	38	56	end	end	NOUN
ajst-7806	38	57	.	.	PUNCT
ajst-7806	39	1	2.in	2.in	NUM
ajst-7806	39	2	the	the	DET
ajst-7806	39	3	proposal	proposal	NOUN
ajst-7806	39	4	selection	selection	NOUN
ajst-7806	39	5	module	module	NOUN
ajst-7806	39	6	,	,	PUNCT
ajst-7806	39	7	in	in	ADP
ajst-7806	39	8	order	order	NOUN
ajst-7806	39	9	to	to	PART
ajst-7806	39	10	better	well	ADV
ajst-7806	39	11	select	select	VERB
ajst-7806	39	12	positive	positive	ADJ
ajst-7806	39	13	target	target	NOUN
ajst-7806	39	14	proposals	proposal	NOUN
ajst-7806	39	15	for	for	ADP
ajst-7806	39	16	weakly	weakly	ADJ
ajst-7806	39	17	supervised	supervised	ADJ
ajst-7806	39	18	target	target	NOUN
ajst-7806	39	19	detection	detection	NOUN
ajst-7806	39	20	tasks	task	NOUN
ajst-7806	39	21	,	,	PUNCT
ajst-7806	39	22	this	this	DET
ajst-7806	39	23	paper	paper	NOUN
ajst-7806	39	24	can	can	AUX
ajst-7806	39	25	combine	combine	VERB
ajst-7806	39	26	bottom	bottom	ADJ
ajst-7806	39	27	-	-	PUNCT
ajst-7806	39	28	up	up	ADP
ajst-7806	39	29	target	target	NOUN
ajst-7806	39	30	evidence	evidence	NOUN
ajst-7806	39	31	and	and	CCONJ
ajst-7806	39	32	top	top	ADJ
ajst-7806	39	33	-	-	PUNCT
ajst-7806	39	34	down	down	ADP
ajst-7806	39	35	class	class	NOUN
ajst-7806	39	36	confidence	confidence	NOUN
ajst-7806	39	37	scores	score	NOUN
ajst-7806	39	38	in	in	ADP
ajst-7806	39	39	a	a	DET
ajst-7806	39	40	new	new	ADJ
ajst-7806	39	41	way	way	NOUN
ajst-7806	39	42	to	to	PART
ajst-7806	39	43	better	well	ADV
ajst-7806	39	44	select	select	VERB
ajst-7806	39	45	the	the	DET
ajst-7806	39	46	most	most	ADV
ajst-7806	39	47	suitable	suitable	ADJ
ajst-7806	39	48	bounding	bounding	NOUN
ajst-7806	39	49	boxes	box	NOUN
ajst-7806	39	50	.	.	PUNCT
ajst-7806	40	1	3.this	3.this	NUM
ajst-7806	40	2	paper	paper	NOUN
ajst-7806	40	3	adds	add	VERB
ajst-7806	40	4	a	a	DET
ajst-7806	40	5	bounding	bounding	NOUN
ajst-7806	40	6	box	box	NOUN
ajst-7806	40	7	regression	regression	NOUN
ajst-7806	40	8	branch	branch	NOUN
ajst-7806	40	9	,	,	PUNCT
ajst-7806	40	10	and	and	CCONJ
ajst-7806	40	11	introduces	introduce	VERB
ajst-7806	40	12	three	three	NUM
ajst-7806	40	13	modules	module	NOUN
ajst-7806	40	14	to	to	PART
ajst-7806	40	15	generate	generate	VERB
ajst-7806	40	16	and	and	CCONJ
ajst-7806	40	17	select	select	VERB
ajst-7806	40	18	proposals	proposal	NOUN
ajst-7806	40	19	respectively	respectively	ADV
ajst-7806	40	20	,	,	PUNCT
ajst-7806	40	21	which	which	PRON
ajst-7806	40	22	are	be	AUX
ajst-7806	40	23	unified	unify	VERB
ajst-7806	40	24	into	into	ADP
ajst-7806	40	25	a	a	DET
ajst-7806	40	26	weakly	weakly	ADJ
ajst-7806	40	27	supervised	supervised	ADJ
ajst-7806	40	28	object	object	NOUN
ajst-7806	40	29	detection	detection	NOUN
ajst-7806	40	30	framework	framework	NOUN
ajst-7806	40	31	for	for	ADP
ajst-7806	40	32	end	end	NOUN
ajst-7806	40	33	-	-	PUNCT
ajst-7806	40	34	to	to	ADP
ajst-7806	40	35	-	-	PUNCT
ajst-7806	40	36	end	end	NOUN
ajst-7806	40	37	training	training	NOUN
ajst-7806	40	38	.	.	PUNCT
ajst-7806	41	1	2	2	X
ajst-7806	41	2	.	.	X
ajst-7806	41	3	related	relate	VERB
ajst-7806	41	4	work	work	NOUN
ajst-7806	41	5	2.1	2.1	NUM
ajst-7806	41	6	.	.	PUNCT
ajst-7806	42	1	weakly	weakly	ADJ
ajst-7806	42	2	supervised	supervise	VERB
ajst-7806	42	3	object	object	NOUN
ajst-7806	42	4	detection	detection	NOUN
ajst-7806	42	5	in	in	ADP
ajst-7806	42	6	recent	recent	ADJ
ajst-7806	42	7	years	year	NOUN
ajst-7806	42	8	,	,	PUNCT
ajst-7806	42	9	weakly	weakly	ADV
ajst-7806	42	10	supervised	supervised	ADJ
ajst-7806	42	11	object	object	NOUN
ajst-7806	42	12	detection	detection	NOUN
ajst-7806	42	13	has	have	AUX
ajst-7806	42	14	attracted	attract	VERB
ajst-7806	42	15	a	a	DET
ajst-7806	42	16	lot	lot	NOUN
ajst-7806	42	17	of	of	ADP
ajst-7806	42	18	attention	attention	NOUN
ajst-7806	42	19	from	from	ADP
ajst-7806	42	20	researchers	researcher	NOUN
ajst-7806	42	21	.	.	PUNCT
ajst-7806	43	1	the	the	DET
ajst-7806	43	2	classic	classic	ADJ
ajst-7806	43	3	framework	framework	NOUN
ajst-7806	43	4	on	on	ADP
ajst-7806	43	5	wsod	wsod	PROPN
ajst-7806	43	6	,	,	PUNCT
ajst-7806	43	7	wsddn	wsddn	ADJ
ajst-7806	43	8	,	,	PUNCT
ajst-7806	43	9	is	be	AUX
ajst-7806	43	10	to	to	PART
ajst-7806	43	11	solve	solve	VERB
ajst-7806	43	12	the	the	DET
ajst-7806	43	13	wsod	wsod	ADJ
ajst-7806	43	14	problem	problem	NOUN
ajst-7806	43	15	with	with	ADP
ajst-7806	43	16	a	a	DET
ajst-7806	43	17	multiple	multiple	ADJ
ajst-7806	43	18	-	-	PUNCT
ajst-7806	43	19	instance	instance	NOUN
ajst-7806	43	20	learning	learning	NOUN
ajst-7806	43	21	(	(	PUNCT
ajst-7806	43	22	mil	mil	NOUN
ajst-7806	43	23	)	)	PUNCT
ajst-7806	43	24	approach	approach	NOUN
ajst-7806	43	25	,	,	PUNCT
ajst-7806	43	26	which	which	PRON
ajst-7806	43	27	contributes	contribute	VERB
ajst-7806	43	28	by	by	ADP
ajst-7806	43	29	using	use	VERB
ajst-7806	43	30	dual	dual	ADJ
ajst-7806	43	31	streams	stream	NOUN
ajst-7806	43	32	to	to	PART
ajst-7806	43	33	perform	perform	VERB
ajst-7806	43	34	object	object	NOUN
ajst-7806	43	35	localization	localization	NOUN
ajst-7806	43	36	and	and	CCONJ
ajst-7806	43	37	classification	classification	NOUN
ajst-7806	43	38	simultaneously	simultaneously	ADV
ajst-7806	43	39	,	,	PUNCT
ajst-7806	43	40	but	but	CCONJ
ajst-7806	43	41	since	since	SCONJ
ajst-7806	43	42	only	only	ADV
ajst-7806	43	43	image	image	NOUN
ajst-7806	43	44	-	-	PUNCT
ajst-7806	43	45	level	level	NOUN
ajst-7806	43	46	labeled	label	VERB
ajst-7806	43	47	data	datum	NOUN
ajst-7806	43	48	can	can	AUX
ajst-7806	43	49	be	be	AUX
ajst-7806	43	50	accessed	access	VERB
ajst-7806	43	51	during	during	ADP
ajst-7806	43	52	the	the	DET
ajst-7806	43	53	training	training	NOUN
ajst-7806	43	54	phase	phase	NOUN
ajst-7806	43	55	,	,	PUNCT
ajst-7806	43	56	the	the	DET
ajst-7806	43	57	most	most	ADV
ajst-7806	43	58	discriminative	discriminative	ADJ
ajst-7806	43	59	parts	part	NOUN
ajst-7806	43	60	receive	receive	VERB
ajst-7806	43	61	more	more	ADJ
ajst-7806	43	62	attention	attention	NOUN
ajst-7806	43	63	during	during	ADP
ajst-7806	43	64	training	training	NOUN
ajst-7806	43	65	than	than	ADP
ajst-7806	43	66	the	the	DET
ajst-7806	43	67	whole	whole	ADJ
ajst-7806	43	68	object	object	NOUN
ajst-7806	43	69	instance	instance	NOUN
ajst-7806	43	70	,	,	PUNCT
ajst-7806	43	71	leading	lead	VERB
ajst-7806	43	72	to	to	ADP
ajst-7806	43	73	the	the	DET
ajst-7806	43	74	model	model	NOUN
ajst-7806	43	75	suffers	suffer	VERB
ajst-7806	43	76	from	from	ADP
ajst-7806	43	77	a	a	DET
ajst-7806	43	78	discriminative	discriminative	NOUN
ajst-7806	43	79	region	region	NOUN
ajst-7806	43	80	problem	problem	NOUN
ajst-7806	43	81	,	,	PUNCT
ajst-7806	43	82	which	which	PRON
ajst-7806	43	83	is	be	AUX
ajst-7806	43	84	improved	improve	VERB
ajst-7806	43	85	by	by	ADP
ajst-7806	43	86	the	the	DET
ajst-7806	43	87	later	later	ADJ
ajst-7806	43	88	work	work	NOUN
ajst-7806	43	89	.	.	PUNCT
ajst-7806	44	1	in	in	ADP
ajst-7806	44	2	order	order	NOUN
ajst-7806	44	3	to	to	PART
ajst-7806	44	4	alleviate	alleviate	VERB
ajst-7806	44	5	the	the	DET
ajst-7806	44	6	problem	problem	NOUN
ajst-7806	44	7	of	of	ADP
ajst-7806	44	8	distinguished	distinguished	ADJ
ajst-7806	44	9	regions	region	NOUN
ajst-7806	44	10	,	,	PUNCT
ajst-7806	44	11	online	online	ADJ
ajst-7806	44	12	instance	instance	NOUN
ajst-7806	44	13	classifier	classifier	NOUN
ajst-7806	44	14	refinement	refinement	NOUN
ajst-7806	44	15	strategy	strategy	NOUN
ajst-7806	44	16	(	(	PUNCT
ajst-7806	44	17	oicr	oicr	NOUN
ajst-7806	44	18	)	)	PUNCT
ajst-7806	45	1	[	[	X
ajst-7806	45	2	2	2	X
ajst-7806	45	3	]	]	PUNCT
ajst-7806	45	4	takes	take	VERB
ajst-7806	45	5	wsddn	wsddn	NOUN
ajst-7806	45	6	as	as	ADP
ajst-7806	45	7	the	the	DET
ajst-7806	45	8	baseline	baseline	NOUN
ajst-7806	45	9	and	and	CCONJ
ajst-7806	45	10	adds	add	VERB
ajst-7806	45	11	three	three	NUM
ajst-7806	45	12	more	more	ADJ
ajst-7806	45	13	instance	instance	NOUN
ajst-7806	45	14	classifier	classifier	NOUN
ajst-7806	45	15	refinement	refinement	NOUN
ajst-7806	45	16	processes	process	NOUN
ajst-7806	45	17	after	after	ADP
ajst-7806	45	18	the	the	DET
ajst-7806	45	19	baseline	baseline	NOUN
ajst-7806	45	20	,	,	PUNCT
ajst-7806	45	21	which	which	PRON
ajst-7806	45	22	improves	improve	VERB
ajst-7806	45	23	the	the	DET
ajst-7806	45	24	performance	performance	NOUN
ajst-7806	45	25	of	of	ADP
ajst-7806	45	26	weakly	weakly	ADJ
ajst-7806	45	27	supervised	supervised	ADJ
ajst-7806	45	28	target	target	NOUN
ajst-7806	45	29	detection	detection	NOUN
ajst-7806	45	30	but	but	CCONJ
ajst-7806	45	31	also	also	ADV
ajst-7806	45	32	easily	easily	ADV
ajst-7806	45	33	falls	fall	VERB
ajst-7806	45	34	into	into	ADP
ajst-7806	45	35	local	local	ADJ
ajst-7806	45	36	optimum	optimum	NOUN
ajst-7806	45	37	because	because	SCONJ
ajst-7806	45	38	only	only	ADV
ajst-7806	45	39	the	the	DET
ajst-7806	45	40	most	most	ADV
ajst-7806	45	41	distinguished	distinguished	ADJ
ajst-7806	45	42	instances	instance	NOUN
ajst-7806	45	43	are	be	AUX
ajst-7806	45	44	selected	select	VERB
ajst-7806	45	45	for	for	ADP
ajst-7806	45	46	refinement	refinement	NOUN
ajst-7806	45	47	.	.	PUNCT
ajst-7806	46	1	by	by	ADP
ajst-7806	46	2	combining	combine	VERB
ajst-7806	46	3	waddn	waddn	ADJ
ajst-7806	46	4	and	and	CCONJ
ajst-7806	46	5	oicr	oicr	NOUN
ajst-7806	46	6	,	,	PUNCT
ajst-7806	46	7	zhang	zhang	PROPN
ajst-7806	46	8	et	et	PROPN
ajst-7806	46	9	al	al	PROPN
ajst-7806	47	1	[	[	X
ajst-7806	47	2	40	40	NUM
ajst-7806	47	3	]	]	PUNCT
ajst-7806	47	4	designed	design	VERB
ajst-7806	47	5	a	a	DET
ajst-7806	47	6	framework	framework	NOUN
ajst-7806	47	7	from	from	ADP
ajst-7806	47	8	weakly	weakly	ADV
ajst-7806	47	9	supervised	supervised	ADJ
ajst-7806	47	10	to	to	PART
ajst-7806	47	11	fully	fully	ADV
ajst-7806	47	12	supervised	supervise	VERB
ajst-7806	47	13	,	,	PUNCT
ajst-7806	47	14	which	which	PRON
ajst-7806	47	15	is	be	AUX
ajst-7806	47	16	also	also	ADV
ajst-7806	47	17	implemented	implement	VERB
ajst-7806	47	18	with	with	ADP
ajst-7806	47	19	mil	mil	PROPN
ajst-7806	47	20	.	.	PUNCT
ajst-7806	48	1	pcl	pcl	PROPN
ajst-7806	48	2	is	be	AUX
ajst-7806	48	3	a	a	DET
ajst-7806	48	4	further	further	ADJ
ajst-7806	48	5	improvement	improvement	NOUN
ajst-7806	48	6	of	of	ADP
ajst-7806	48	7	the	the	DET
ajst-7806	48	8	above	above	ADJ
ajst-7806	48	9	oicr	oicr	NOUN
ajst-7806	48	10	,	,	PUNCT
ajst-7806	48	11	which	which	PRON
ajst-7806	48	12	proposes	propose	VERB
ajst-7806	48	13	to	to	PART
ajst-7806	48	14	use	use	VERB
ajst-7806	48	15	proposal	proposal	NOUN
ajst-7806	48	16	clusters	cluster	NOUN
ajst-7806	48	17	on	on	ADP
ajst-7806	48	18	top	top	NOUN
ajst-7806	48	19	of	of	ADP
ajst-7806	48	20	oicr	oicr	NOUN
ajst-7806	48	21	to	to	PART
ajst-7806	48	22	divide	divide	VERB
ajst-7806	48	23	all	all	DET
ajst-7806	48	24	proposals	proposal	NOUN
ajst-7806	48	25	into	into	ADP
ajst-7806	48	26	different	different	ADJ
ajst-7806	48	27	pouches	pouch	NOUN
ajst-7806	48	28	and	and	CCONJ
ajst-7806	48	29	then	then	ADV
ajst-7806	48	30	apply	apply	VERB
ajst-7806	48	31	classifiers	classifier	NOUN
ajst-7806	48	32	for	for	ADP
ajst-7806	48	33	refinement	refinement	NOUN
ajst-7806	48	34	,	,	PUNCT
ajst-7806	48	35	i.e.	i.e.	X
ajst-7806	48	36	,	,	PUNCT
ajst-7806	48	37	proposal	proposal	NOUN
ajst-7806	48	38	clustering	clustering	NOUN
ajst-7806	48	39	.	.	PUNCT
ajst-7806	49	1	arun	arun	PROPN
ajst-7806	49	2	et	et	PROPN
ajst-7806	49	3	al	al	PROPN
ajst-7806	50	1	[	[	X
ajst-7806	50	2	32	32	NUM
ajst-7806	50	3	]	]	PUNCT
ajst-7806	50	4	designed	design	VERB
ajst-7806	50	5	a	a	DET
ajst-7806	50	6	new	new	ADJ
ajst-7806	50	7	phase	phase	NOUN
ajst-7806	50	8	difference	difference	NOUN
ajst-7806	50	9	coefficientbased	coefficientbase	VERB
ajst-7806	50	10	wsod	wsod	ADJ
ajst-7806	50	11	framework	framework	NOUN
ajst-7806	50	12	that	that	PRON
ajst-7806	50	13	implements	implement	VERB
ajst-7806	50	14	the	the	DET
ajst-7806	50	15	wsod	wsod	ADJ
ajst-7806	50	16	task	task	NOUN
ajst-7806	50	17	by	by	ADP
ajst-7806	50	18	minimizing	minimize	VERB
ajst-7806	50	19	the	the	DET
ajst-7806	50	20	difference	difference	NOUN
ajst-7806	50	21	between	between	ADP
ajst-7806	50	22	the	the	DET
ajst-7806	50	23	annotation	annotation	NOUN
ajst-7806	50	24	agnostic	agnostic	ADJ
ajst-7806	50	25	prediction	prediction	NOUN
ajst-7806	50	26	distribution	distribution	NOUN
ajst-7806	50	27	and	and	CCONJ
ajst-7806	50	28	the	the	DET
ajst-7806	50	29	annotated	annotate	VERB
ajst-7806	50	30	perceptual	perceptual	ADJ
ajst-7806	50	31	conditional	conditional	ADJ
ajst-7806	50	32	distribution	distribution	NOUN
ajst-7806	50	33	.	.	PUNCT
ajst-7806	51	1	shen	shen	PROPN
ajst-7806	51	2	et	et	PROPN
ajst-7806	51	3	al	al	PROPN
ajst-7806	52	1	[	[	X
ajst-7806	52	2	33	33	NUM
ajst-7806	52	3	]	]	PUNCT
ajst-7806	52	4	proposed	propose	VERB
ajst-7806	52	5	a	a	DET
ajst-7806	52	6	framework	framework	NOUN
ajst-7806	52	7	called	call	VERB
ajst-7806	52	8	weakly	weakly	ADV
ajst-7806	52	9	supervised	supervised	ADJ
ajst-7806	52	10	joint	joint	ADJ
ajst-7806	52	11	detection	detection	NOUN
ajst-7806	52	12	and	and	CCONJ
ajst-7806	52	13	segmentation	segmentation	NOUN
ajst-7806	52	14	(	(	PUNCT
ajst-7806	52	15	ws	ws	NOUN
ajst-7806	52	16	-	-	PUNCT
ajst-7806	52	17	jds	jds	PROPN
ajst-7806	52	18	)	)	PUNCT
ajst-7806	52	19	by	by	ADP
ajst-7806	52	20	combining	combine	VERB
ajst-7806	52	21	these	these	DET
ajst-7806	52	22	two	two	NUM
ajst-7806	52	23	tasks	task	NOUN
ajst-7806	52	24	into	into	ADP
ajst-7806	52	25	a	a	DET
ajst-7806	52	26	multi	multi	ADJ
ajst-7806	52	27	-	-	ADJ
ajst-7806	52	28	task	task	ADJ
ajst-7806	52	29	learning	learn	VERB
ajst-7806	52	30	framework	framework	NOUN
ajst-7806	52	31	.	.	PUNCT
ajst-7806	53	1	li	li	PROPN
ajst-7806	53	2	et	et	PROPN
ajst-7806	53	3	al	al	PROPN
ajst-7806	54	1	[	[	X
ajst-7806	54	2	34	34	NUM
ajst-7806	54	3	]	]	PUNCT
ajst-7806	54	4	proposed	propose	VERB
ajst-7806	54	5	a	a	DET
ajst-7806	54	6	segmentation	segmentation	NOUN
ajst-7806	54	7	collaboration	collaboration	NOUN
ajst-7806	54	8	network	network	NOUN
ajst-7806	54	9	that	that	PRON
ajst-7806	54	10	uses	use	VERB
ajst-7806	54	11	segmentation	segmentation	NOUN
ajst-7806	54	12	graphs	graph	NOUN
ajst-7806	54	13	as	as	ADP
ajst-7806	54	14	a	a	DET
ajst-7806	54	15	priori	priori	ADJ
ajst-7806	54	16	information	information	NOUN
ajst-7806	54	17	to	to	PART
ajst-7806	54	18	supervise	supervise	VERB
ajst-7806	54	19	the	the	DET
ajst-7806	54	20	learning	learning	NOUN
ajst-7806	54	21	of	of	ADP
ajst-7806	54	22	object	object	NOUN
ajst-7806	54	23	detection	detection	NOUN
ajst-7806	54	24	.	.	PUNCT
ajst-7806	55	1	ze	ze	PROPN
ajst-7806	55	2	chen	chen	PROPN
ajst-7806	55	3	et	et	PROPN
ajst-7806	55	4	al	al	PROPN
ajst-7806	56	1	[	[	X
ajst-7806	56	2	36	36	NUM
ajst-7806	56	3	]	]	PUNCT
ajst-7806	56	4	proposed	propose	VERB
ajst-7806	56	5	a	a	DET
ajst-7806	56	6	spatial	spatial	ADJ
ajst-7806	56	7	likelihood	likelihood	NOUN
ajst-7806	56	8	voting	voting	NOUN
ajst-7806	56	9	module	module	NOUN
ajst-7806	56	10	to	to	PART
ajst-7806	56	11	converge	converge	VERB
ajst-7806	56	12	the	the	DET
ajst-7806	56	13	localization	localization	NOUN
ajst-7806	56	14	process	process	NOUN
ajst-7806	56	15	of	of	ADP
ajst-7806	56	16	proposed	propose	VERB
ajst-7806	56	17	frames	frame	NOUN
ajst-7806	56	18	without	without	ADP
ajst-7806	56	19	any	any	DET
ajst-7806	56	20	bounding	bounding	NOUN
ajst-7806	56	21	box	box	NOUN
ajst-7806	56	22	annotation	annotation	NOUN
ajst-7806	56	23	.	.	PUNCT
ajst-7806	57	1	chenhao	chenhao	PROPN
ajst-7806	57	2	lin	lin	PROPN
ajst-7806	57	3	et	et	PROPN
ajst-7806	57	4	al	al	PROPN
ajst-7806	58	1	[	[	X
ajst-7806	58	2	37	37	NUM
ajst-7806	58	3	]	]	PUNCT
ajst-7806	58	4	proposed	propose	VERB
ajst-7806	58	5	an	an	DET
ajst-7806	58	6	end	end	NOUN
ajst-7806	58	7	-	-	PUNCT
ajst-7806	58	8	to	to	ADP
ajst-7806	58	9	-	-	PUNCT
ajst-7806	58	10	end	end	NOUN
ajst-7806	58	11	object	object	NOUN
ajst-7806	58	12	instance	instance	NOUN
ajst-7806	58	13	mining	mining	NOUN
ajst-7806	58	14	weakly	weakly	ADV
ajst-7806	58	15	supervised	supervised	ADJ
ajst-7806	58	16	object	object	NOUN
ajst-7806	58	17	detection	detection	NOUN
ajst-7806	58	18	framework	framework	NOUN
ajst-7806	58	19	that	that	PRON
ajst-7806	58	20	introduces	introduce	VERB
ajst-7806	58	21	a	a	DET
ajst-7806	58	22	spatial	spatial	ADJ
ajst-7806	58	23	graph	graph	NOUN
ajst-7806	58	24	and	and	CCONJ
ajst-7806	58	25	appearance	appearance	NOUN
ajst-7806	58	26	graph	graph	NOUN
ajst-7806	58	27	based	base	VERB
ajst-7806	58	28	information	information	NOUN
ajst-7806	58	29	propagation	propagation	NOUN
ajst-7806	58	30	mechanism	mechanism	NOUN
ajst-7806	58	31	to	to	PART
ajst-7806	58	32	try	try	VERB
ajst-7806	58	33	to	to	PART
ajst-7806	58	34	mine	mine	VERB
ajst-7806	58	35	all	all	DET
ajst-7806	58	36	object	object	NOUN
ajst-7806	58	37	instances	instance	NOUN
ajst-7806	58	38	in	in	ADP
ajst-7806	58	39	each	each	DET
ajst-7806	58	40	image	image	NOUN
ajst-7806	58	41	during	during	ADP
ajst-7806	58	42	iterative	iterative	NOUN
ajst-7806	58	43	network	network	NOUN
ajst-7806	58	44	learning	learning	NOUN
ajst-7806	58	45	.	.	PUNCT
ajst-7806	59	1	2.2	2.2	NUM
ajst-7806	59	2	.	.	PUNCT
ajst-7806	59	3	boundary	boundary	PROPN
ajst-7806	59	4	box	box	PROPN
ajst-7806	59	5	regression	regression	NOUN
ajst-7806	59	6	because	because	SCONJ
ajst-7806	59	7	only	only	ADV
ajst-7806	59	8	image	image	NOUN
ajst-7806	59	9	-	-	PUNCT
ajst-7806	59	10	level	level	NOUN
ajst-7806	59	11	labels	label	NOUN
ajst-7806	59	12	are	be	AUX
ajst-7806	59	13	available	available	ADJ
ajst-7806	59	14	,	,	PUNCT
ajst-7806	59	15	they	they	PRON
ajst-7806	59	16	only	only	ADV
ajst-7806	59	17	indicate	indicate	VERB
ajst-7806	59	18	whether	whether	SCONJ
ajst-7806	59	19	the	the	DET
ajst-7806	59	20	target	target	NOUN
ajst-7806	59	21	category	category	NOUN
ajst-7806	59	22	has	have	AUX
ajst-7806	59	23	appeared	appear	VERB
ajst-7806	59	24	or	or	CCONJ
ajst-7806	59	25	not	not	PART
ajst-7806	59	26	.	.	PUNCT
ajst-7806	60	1	however	however	ADV
ajst-7806	60	2	,	,	PUNCT
ajst-7806	60	3	in	in	ADP
ajst-7806	60	4	order	order	NOUN
ajst-7806	60	5	to	to	PART
ajst-7806	60	6	train	train	VERB
ajst-7806	60	7	a	a	DET
ajst-7806	60	8	standard	standard	ADJ
ajst-7806	60	9	target	target	NOUN
ajst-7806	60	10	detector	detector	NOUN
ajst-7806	60	11	with	with	ADP
ajst-7806	60	12	a	a	DET
ajst-7806	60	13	regression	regression	NOUN
ajst-7806	60	14	task	task	NOUN
ajst-7806	60	15	,	,	PUNCT
ajst-7806	60	16	it	it	PRON
ajst-7806	60	17	is	be	AUX
ajst-7806	60	18	necessary	necessary	ADJ
ajst-7806	60	19	to	to	ADP
ajst-7806	60	20	mine	mine	VERB
ajst-7806	60	21	instance	instance	NOUN
ajst-7806	60	22	-	-	PUNCT
ajst-7806	60	23	level	level	NOUN
ajst-7806	60	24	supervised	supervised	ADJ
ajst-7806	60	25	information	information	NOUN
ajst-7806	60	26	,	,	PUNCT
ajst-7806	60	27	e.g.	e.g.	ADV
ajst-7806	60	28	,	,	PUNCT
ajst-7806	60	29	bounding	bound	VERB
ajst-7806	60	30	box	box	NOUN
ajst-7806	60	31	annotations	annotation	NOUN
ajst-7806	60	32	.	.	PUNCT
ajst-7806	61	1	therefore	therefore	ADV
ajst-7806	61	2	,	,	PUNCT
ajst-7806	61	3	yang	yang	PROPN
ajst-7806	61	4	et	et	PROPN
ajst-7806	61	5	al	al	PROPN
ajst-7806	62	1	[	[	X
ajst-7806	62	2	22	22	NUM
ajst-7806	62	3	]	]	PUNCT
ajst-7806	62	4	here	here	ADV
ajst-7806	62	5	introduces	introduce	VERB
ajst-7806	62	6	a	a	DET
ajst-7806	62	7	mil	mil	NOUN
ajst-7806	62	8	branch	branch	NOUN
ajst-7806	62	9	to	to	PART
ajst-7806	62	10	obtain	obtain	VERB
ajst-7806	62	11	pseudo	pseudo	NOUN
ajst-7806	62	12	-	-	NOUN
ajst-7806	62	13	gt	gt	ADJ
ajst-7806	62	14	annotation	annotation	NOUN
ajst-7806	62	15	information	information	NOUN
ajst-7806	62	16	,	,	PUNCT
ajst-7806	62	17	and	and	CCONJ
ajst-7806	62	18	chooses	choose	VERB
ajst-7806	62	19	to	to	PART
ajst-7806	62	20	use	use	VERB
ajst-7806	62	21	a	a	DET
ajst-7806	62	22	wsddn	wsddn	NOUN
ajst-7806	62	23	-	-	PUNCT
ajst-7806	62	24	based	base	VERB
ajst-7806	62	25	oicr	oicr	NOUN
ajst-7806	62	26	network	network	NOUN
ajst-7806	62	27	for	for	ADP
ajst-7806	62	28	end	end	NOUN
ajst-7806	62	29	-	-	PUNCT
ajst-7806	62	30	to	to	ADP
ajst-7806	62	31	-	-	PUNCT
ajst-7806	62	32	end	end	NOUN
ajst-7806	62	33	training	training	NOUN
ajst-7806	62	34	.	.	PUNCT
ajst-7806	63	1	bounding	bound	VERB
ajst-7806	63	2	box	box	NOUN
ajst-7806	63	3	regression	regression	NOUN
ajst-7806	63	4	is	be	AUX
ajst-7806	63	5	used	use	VERB
ajst-7806	63	6	after	after	ADP
ajst-7806	63	7	refinement	refinement	NOUN
ajst-7806	63	8	using	use	VERB
ajst-7806	63	9	multiple	multiple	ADJ
ajst-7806	63	10	box	box	NOUN
ajst-7806	63	11	classifications	classification	NOUN
ajst-7806	63	12	and	and	CCONJ
ajst-7806	63	13	only	only	ADV
ajst-7806	63	14	once	once	ADV
ajst-7806	63	15	.	.	PUNCT
ajst-7806	64	1	c	c	X
ajst-7806	64	2	-	-	PUNCT
ajst-7806	64	3	wsl	wsl	PROPN
ajst-7806	65	1	[	[	X
ajst-7806	65	2	28	28	NUM
ajst-7806	65	3	]	]	PUNCT
ajst-7806	65	4	also	also	ADV
ajst-7806	65	5	explores	explore	VERB
ajst-7806	65	6	bounding	bound	VERB
ajst-7806	65	7	box	box	NOUN
ajst-7806	65	8	regression	regression	NOUN
ajst-7806	65	9	for	for	ADP
ajst-7806	65	10	weakly	weakly	ADJ
ajst-7806	65	11	supervised	supervised	ADJ
ajst-7806	65	12	object	object	NOUN
ajst-7806	65	13	detection	detection	NOUN
ajst-7806	65	14	networks	network	NOUN
ajst-7806	65	15	as	as	ADP
ajst-7806	65	16	in	in	ADP
ajst-7806	65	17	[	[	X
ajst-7806	65	18	22	22	NUM
ajst-7806	65	19	]	]	PUNCT
ajst-7806	65	20	.	.	PUNCT
ajst-7806	66	1	and	and	CCONJ
ajst-7806	66	2	both	both	PRON
ajst-7806	66	3	use	use	VERB
ajst-7806	66	4	bounding	bound	VERB
ajst-7806	66	5	box	box	NOUN
ajst-7806	66	6	regression	regression	NOUN
ajst-7806	66	7	in	in	ADP
ajst-7806	66	8	an	an	DET
ajst-7806	66	9	online	online	ADJ
ajst-7806	66	10	fashion	fashion	NOUN
ajst-7806	66	11	,	,	PUNCT
ajst-7806	66	12	cwsl	cwsl	PROPN
ajst-7806	66	13	uses	use	VERB
ajst-7806	66	14	a	a	DET
ajst-7806	66	15	box	box	NOUN
ajst-7806	66	16	regressor	regressor	NOUN
ajst-7806	66	17	to	to	PART
ajst-7806	66	18	refine	refine	VERB
ajst-7806	66	19	each	each	DET
ajst-7806	66	20	box	box	NOUN
ajst-7806	66	21	classifier	classifier	NOUN
ajst-7806	66	22	after	after	ADP
ajst-7806	66	23	the	the	DET
ajst-7806	66	24	mil	mil	NOUN
ajst-7806	66	25	branch	branch	NOUN
ajst-7806	66	26	.	.	PUNCT
ajst-7806	67	1	bounding	bound	VERB
ajst-7806	67	2	box	box	NOUN
ajst-7806	67	3	regression	regression	NOUN
ajst-7806	67	4	is	be	AUX
ajst-7806	67	5	a	a	DET
ajst-7806	67	6	key	key	ADJ
ajst-7806	67	7	step	step	NOUN
ajst-7806	67	8	in	in	ADP
ajst-7806	67	9	object	object	NOUN
ajst-7806	67	10	detection	detection	NOUN
ajst-7806	67	11	139	139	NUM
ajst-7806	67	12	for	for	ADP
ajst-7806	67	13	predicting	predict	VERB
ajst-7806	67	14	rectangular	rectangular	ADJ
ajst-7806	67	15	boxes	box	NOUN
ajst-7806	67	16	to	to	PART
ajst-7806	67	17	locate	locate	VERB
ajst-7806	67	18	targets	target	NOUN
ajst-7806	67	19	,	,	PUNCT
ajst-7806	67	20	so	so	SCONJ
ajst-7806	67	21	almost	almost	ADV
ajst-7806	67	22	all	all	PRON
ajst-7806	67	23	recent	recent	ADJ
ajst-7806	67	24	fully	fully	ADV
ajst-7806	67	25	supervised	supervised	ADJ
ajst-7806	67	26	object	object	NOUN
ajst-7806	67	27	detection	detection	NOUN
ajst-7806	67	28	[	[	X
ajst-7806	67	29	3,4,12,13,21,24	3,4,12,13,21,24	NUM
ajst-7806	67	30	]	]	X
ajst-7806	67	31	used	use	VERB
ajst-7806	67	32	bounding	bounding	NOUN
ajst-7806	67	33	box	box	NOUN
ajst-7806	67	34	regression	regression	NOUN
ajst-7806	67	35	,	,	PUNCT
ajst-7806	67	36	which	which	PRON
ajst-7806	67	37	can	can	AUX
ajst-7806	67	38	reduce	reduce	VERB
ajst-7806	67	39	the	the	DET
ajst-7806	67	40	localization	localization	NOUN
ajst-7806	67	41	error	error	NOUN
ajst-7806	67	42	of	of	ADP
ajst-7806	67	43	prediction	prediction	NOUN
ajst-7806	67	44	boxes	box	NOUN
ajst-7806	67	45	.	.	PUNCT
ajst-7806	68	1	however	however	ADV
ajst-7806	68	2	,	,	PUNCT
ajst-7806	68	3	since	since	SCONJ
ajst-7806	68	4	it	it	PRON
ajst-7806	68	5	is	be	AUX
ajst-7806	68	6	weakly	weakly	ADV
ajst-7806	68	7	supervised	supervised	ADJ
ajst-7806	68	8	learning	learning	NOUN
ajst-7806	68	9	and	and	CCONJ
ajst-7806	68	10	the	the	DET
ajst-7806	68	11	data	data	NOUN
ajst-7806	68	12	lacks	lack	VERB
ajst-7806	68	13	the	the	DET
ajst-7806	68	14	labeling	labeling	NOUN
ajst-7806	68	15	information	information	NOUN
ajst-7806	68	16	of	of	ADP
ajst-7806	68	17	the	the	DET
ajst-7806	68	18	bounding	bounding	NOUN
ajst-7806	68	19	box	box	NOUN
ajst-7806	68	20	,	,	PUNCT
ajst-7806	68	21	only	only	ADV
ajst-7806	68	22	a	a	DET
ajst-7806	68	23	small	small	ADJ
ajst-7806	68	24	number	number	NOUN
ajst-7806	68	25	of	of	ADP
ajst-7806	68	26	works	work	NOUN
ajst-7806	68	27	have	have	AUX
ajst-7806	68	28	introduced	introduce	VERB
ajst-7806	68	29	the	the	DET
ajst-7806	68	30	bounding	bounding	NOUN
ajst-7806	68	31	box	box	NOUN
ajst-7806	68	32	into	into	ADP
ajst-7806	68	33	the	the	DET
ajst-7806	68	34	target	target	NOUN
ajst-7806	68	35	detection	detection	NOUN
ajst-7806	68	36	,	,	PUNCT
ajst-7806	68	37	and	and	CCONJ
ajst-7806	68	38	some	some	PRON
ajst-7806	68	39	of	of	ADP
ajst-7806	68	40	them	they	PRON
ajst-7806	68	41	consider	consider	VERB
ajst-7806	68	42	the	the	DET
ajst-7806	68	43	bounding	bounding	NOUN
ajst-7806	68	44	box	box	NOUN
ajst-7806	68	45	regression	regression	NOUN
ajst-7806	68	46	as	as	ADP
ajst-7806	68	47	a	a	DET
ajst-7806	68	48	post	post	ADJ
ajst-7806	68	49	-	-	ADJ
ajst-7806	68	50	processing	processing	ADJ
ajst-7806	68	51	module	module	NOUN
ajst-7806	68	52	.	.	PUNCT
ajst-7806	69	1	2.3	2.3	NUM
ajst-7806	69	2	.	.	PUNCT
ajst-7806	70	1	attention	attention	NOUN
ajst-7806	70	2	mechanism	mechanism	NOUN
ajst-7806	70	3	the	the	DET
ajst-7806	70	4	use	use	NOUN
ajst-7806	70	5	of	of	ADP
ajst-7806	70	6	attention	attention	NOUN
ajst-7806	70	7	modules	module	NOUN
ajst-7806	70	8	first	first	ADV
ajst-7806	70	9	appeared	appear	VERB
ajst-7806	70	10	in	in	ADP
ajst-7806	70	11	natural	natural	ADJ
ajst-7806	70	12	language	language	NOUN
ajst-7806	70	13	and	and	CCONJ
ajst-7806	70	14	was	be	AUX
ajst-7806	70	15	later	later	ADV
ajst-7806	70	16	introduced	introduce	VERB
ajst-7806	70	17	into	into	ADP
ajst-7806	70	18	computer	computer	NOUN
ajst-7806	70	19	vision	vision	NOUN
ajst-7806	70	20	.	.	PUNCT
ajst-7806	71	1	mixed	mix	VERB
ajst-7806	71	2	spatial	spatial	ADJ
ajst-7806	71	3	and	and	CCONJ
ajst-7806	71	4	channel	channel	NOUN
ajst-7806	71	5	attention	attention	NOUN
ajst-7806	71	6	mechanisms	mechanism	NOUN
ajst-7806	71	7	are	be	AUX
ajst-7806	71	8	widely	widely	ADV
ajst-7806	71	9	used	use	VERB
ajst-7806	71	10	in	in	ADP
ajst-7806	71	11	weakly	weakly	ADJ
ajst-7806	71	12	supervised	supervised	ADJ
ajst-7806	71	13	target	target	NOUN
ajst-7806	71	14	detection	detection	NOUN
ajst-7806	71	15	because	because	SCONJ
ajst-7806	71	16	they	they	PRON
ajst-7806	71	17	can	can	AUX
ajst-7806	71	18	not	not	PART
ajst-7806	71	19	only	only	ADV
ajst-7806	71	20	focus	focus	VERB
ajst-7806	71	21	on	on	ADP
ajst-7806	71	22	important	important	ADJ
ajst-7806	71	23	parts	part	NOUN
ajst-7806	71	24	of	of	ADP
ajst-7806	71	25	the	the	DET
ajst-7806	71	26	image	image	NOUN
ajst-7806	71	27	but	but	CCONJ
ajst-7806	71	28	also	also	ADV
ajst-7806	71	29	assign	assign	VERB
ajst-7806	71	30	more	more	ADJ
ajst-7806	71	31	weight	weight	NOUN
ajst-7806	71	32	to	to	ADP
ajst-7806	71	33	important	important	ADJ
ajst-7806	71	34	channels	channel	NOUN
ajst-7806	71	35	.	.	PUNCT
ajst-7806	72	1	in	in	ADP
ajst-7806	72	2	this	this	DET
ajst-7806	72	3	paper	paper	NOUN
ajst-7806	72	4	,	,	PUNCT
ajst-7806	72	5	the	the	DET
ajst-7806	72	6	attention	attention	NOUN
ajst-7806	72	7	module	module	NOUN
ajst-7806	72	8	of	of	ADP
ajst-7806	72	9	cbam	cbam	NOUN
ajst-7806	72	10	[	[	X
ajst-7806	72	11	39	39	NUM
ajst-7806	72	12	]	]	PUNCT
ajst-7806	72	13	is	be	AUX
ajst-7806	72	14	used	use	VERB
ajst-7806	72	15	and	and	CCONJ
ajst-7806	72	16	improved	improve	VERB
ajst-7806	72	17	to	to	PART
ajst-7806	72	18	make	make	VERB
ajst-7806	72	19	it	it	PRON
ajst-7806	72	20	better	well	ADV
ajst-7806	72	21	embedded	embed	VERB
ajst-7806	72	22	in	in	ADP
ajst-7806	72	23	the	the	DET
ajst-7806	72	24	network	network	NOUN
ajst-7806	72	25	.	.	PUNCT
ajst-7806	73	1	attention	attention	NOUN
ajst-7806	73	2	mechanisms	mechanism	NOUN
ajst-7806	73	3	are	be	AUX
ajst-7806	73	4	very	very	ADV
ajst-7806	73	5	similar	similar	ADJ
ajst-7806	73	6	to	to	ADP
ajst-7806	73	7	human	human	ADJ
ajst-7806	73	8	ones	one	NOUN
ajst-7806	73	9	in	in	ADP
ajst-7806	73	10	that	that	SCONJ
ajst-7806	73	11	both	both	PRON
ajst-7806	73	12	tend	tend	VERB
ajst-7806	73	13	to	to	PART
ajst-7806	73	14	focus	focus	VERB
ajst-7806	73	15	on	on	ADP
ajst-7806	73	16	one	one	NUM
ajst-7806	73	17	part	part	NOUN
ajst-7806	73	18	of	of	ADP
ajst-7806	73	19	the	the	DET
ajst-7806	73	20	information	information	NOUN
ajst-7806	73	21	and	and	CCONJ
ajst-7806	73	22	ignore	ignore	VERB
ajst-7806	73	23	the	the	DET
ajst-7806	73	24	others	other	NOUN
ajst-7806	73	25	when	when	SCONJ
ajst-7806	73	26	they	they	PRON
ajst-7806	73	27	see	see	VERB
ajst-7806	73	28	things	thing	NOUN
ajst-7806	73	29	.	.	PUNCT
ajst-7806	74	1	the	the	DET
ajst-7806	74	2	neural	neural	ADJ
ajst-7806	74	3	network	network	NOUN
ajst-7806	74	4	first	first	ADV
ajst-7806	74	5	learns	learn	VERB
ajst-7806	74	6	to	to	ADP
ajst-7806	74	7	new	new	ADJ
ajst-7806	74	8	features	feature	NOUN
ajst-7806	74	9	by	by	ADP
ajst-7806	74	10	channel	channel	NOUN
ajst-7806	74	11	attention	attention	NOUN
ajst-7806	74	12	,	,	PUNCT
ajst-7806	74	13	and	and	CCONJ
ajst-7806	74	14	then	then	ADV
ajst-7806	74	15	learns	learn	VERB
ajst-7806	74	16	to	to	ADP
ajst-7806	74	17	the	the	DET
ajst-7806	74	18	location	location	NOUN
ajst-7806	74	19	of	of	ADP
ajst-7806	74	20	key	key	ADJ
ajst-7806	74	21	features	feature	NOUN
ajst-7806	74	22	by	by	ADP
ajst-7806	74	23	serial	serial	ADJ
ajst-7806	74	24	structure	structure	NOUN
ajst-7806	74	25	to	to	ADP
ajst-7806	74	26	the	the	DET
ajst-7806	74	27	spatial	spatial	ADJ
ajst-7806	74	28	attention	attention	NOUN
ajst-7806	74	29	module	module	NOUN
ajst-7806	74	30	,	,	PUNCT
ajst-7806	74	31	and	and	CCONJ
ajst-7806	74	32	makes	make	VERB
ajst-7806	74	33	efforts	effort	NOUN
ajst-7806	74	34	to	to	PART
ajst-7806	74	35	acquire	acquire	VERB
ajst-7806	74	36	the	the	DET
ajst-7806	74	37	features	feature	NOUN
ajst-7806	74	38	with	with	ADP
ajst-7806	74	39	discriminative	discriminative	NOUN
ajst-7806	74	40	nature	nature	NOUN
ajst-7806	74	41	for	for	ADP
ajst-7806	74	42	images	image	NOUN
ajst-7806	74	43	to	to	PART
ajst-7806	74	44	achieve	achieve	VERB
ajst-7806	74	45	the	the	DET
ajst-7806	74	46	effect	effect	NOUN
ajst-7806	74	47	of	of	ADP
ajst-7806	74	48	adaptive	adaptive	ADJ
ajst-7806	74	49	attention	attention	NOUN
ajst-7806	74	50	of	of	ADP
ajst-7806	74	51	the	the	DET
ajst-7806	74	52	network	network	NOUN
ajst-7806	74	53	.	.	PUNCT
ajst-7806	75	1	3	3	X
ajst-7806	75	2	.	.	X
ajst-7806	75	3	method	method	NOUN
ajst-7806	75	4	in	in	ADP
ajst-7806	75	5	this	this	DET
ajst-7806	75	6	section	section	NOUN
ajst-7806	75	7	,	,	PUNCT
ajst-7806	75	8	we	we	PRON
ajst-7806	75	9	will	will	AUX
ajst-7806	75	10	describe	describe	VERB
ajst-7806	75	11	in	in	ADP
ajst-7806	75	12	detail	detail	NOUN
ajst-7806	75	13	the	the	DET
ajst-7806	75	14	introduced	introduce	VERB
ajst-7806	75	15	proposal	proposal	NOUN
ajst-7806	75	16	generation	generation	NOUN
ajst-7806	75	17	module	module	NOUN
ajst-7806	75	18	and	and	CCONJ
ajst-7806	75	19	the	the	DET
ajst-7806	75	20	proposal	proposal	NOUN
ajst-7806	75	21	selection	selection	NOUN
ajst-7806	75	22	and	and	CCONJ
ajst-7806	75	23	attention	attention	NOUN
ajst-7806	75	24	modules	module	NOUN
ajst-7806	75	25	.	.	PUNCT
ajst-7806	76	1	figure	figure	NOUN
ajst-7806	76	2	3	3	NUM
ajst-7806	76	3	.	.	PUNCT
ajst-7806	77	1	network	network	NOUN
ajst-7806	77	2	structure	structure	NOUN
ajst-7806	77	3	of	of	ADP
ajst-7806	77	4	our	our	PRON
ajst-7806	77	5	method	method	NOUN
ajst-7806	77	6	.	.	PUNCT
ajst-7806	78	1	each	each	DET
ajst-7806	78	2	proposed	propose	VERB
ajst-7806	78	3	feature	feature	NOUN
ajst-7806	78	4	is	be	AUX
ajst-7806	78	5	extracted	extract	VERB
ajst-7806	78	6	using	use	VERB
ajst-7806	78	7	a	a	DET
ajst-7806	78	8	base	base	NOUN
ajst-7806	78	9	network	network	NOUN
ajst-7806	78	10	with	with	ADP
ajst-7806	78	11	vgg16	vgg16	PROPN
ajst-7806	78	12	.	.	PUNCT
ajst-7806	79	1	then	then	ADV
ajst-7806	79	2	,	,	PUNCT
ajst-7806	79	3	the	the	DET
ajst-7806	79	4	proposed	propose	VERB
ajst-7806	79	5	features	feature	NOUN
ajst-7806	79	6	are	be	AUX
ajst-7806	79	7	passed	pass	VERB
ajst-7806	79	8	through	through	ADP
ajst-7806	79	9	two	two	NUM
ajst-7806	79	10	fully	fully	ADV
ajst-7806	79	11	connected	connect	VERB
ajst-7806	79	12	layers	layer	NOUN
ajst-7806	79	13	and	and	CCONJ
ajst-7806	79	14	the	the	DET
ajst-7806	79	15	generated	generate	VERB
ajst-7806	79	16	feature	feature	NOUN
ajst-7806	79	17	vectors	vector	NOUN
ajst-7806	79	18	are	be	AUX
ajst-7806	79	19	branched	branch	VERB
ajst-7806	79	20	to	to	ADP
ajst-7806	79	21	the	the	DET
ajst-7806	79	22	basic	basic	ADJ
ajst-7806	79	23	mil	mil	NOUN
ajst-7806	79	24	module	module	NOUN
ajst-7806	79	25	and	and	CCONJ
ajst-7806	79	26	to	to	ADP
ajst-7806	79	27	a	a	DET
ajst-7806	79	28	new	new	ADJ
ajst-7806	79	29	module	module	NOUN
ajst-7806	79	30	(	(	PUNCT
ajst-7806	79	31	reclassification	reclassification	NOUN
ajst-7806	79	32	branch	branch	NOUN
ajst-7806	79	33	)	)	PUNCT
ajst-7806	79	34	.	.	PUNCT
ajst-7806	80	1	in	in	ADP
ajst-7806	80	2	the	the	DET
ajst-7806	80	3	basic	basic	ADJ
ajst-7806	80	4	mil	mil	NOUN
ajst-7806	80	5	module	module	NOUN
ajst-7806	80	6	,	,	PUNCT
ajst-7806	80	7	there	there	PRON
ajst-7806	80	8	is	be	VERB
ajst-7806	80	9	one	one	NUM
ajst-7806	80	10	wsddn	wsddn	ADJ
ajst-7806	80	11	branch	branch	NOUN
ajst-7806	80	12	and	and	CCONJ
ajst-7806	80	13	three	three	NUM
ajst-7806	80	14	refinement	refinement	ADJ
ajst-7806	80	15	branches	branch	NOUN
ajst-7806	80	16	.	.	PUNCT
ajst-7806	81	1	the	the	DET
ajst-7806	81	2	average	average	ADJ
ajst-7806	81	3	classification	classification	NOUN
ajst-7806	81	4	scores	score	NOUN
ajst-7806	81	5	of	of	ADP
ajst-7806	81	6	the	the	DET
ajst-7806	81	7	three	three	NUM
ajst-7806	81	8	refinement	refinement	NOUN
ajst-7806	81	9	branches	branch	NOUN
ajst-7806	81	10	are	be	AUX
ajst-7806	81	11	input	input	NOUN
ajst-7806	81	12	to	to	ADP
ajst-7806	81	13	the	the	DET
ajst-7806	81	14	new	new	ADJ
ajst-7806	81	15	module	module	NOUN
ajst-7806	81	16	to	to	PART
ajst-7806	81	17	generate	generate	VERB
ajst-7806	81	18	supervision	supervision	NOUN
ajst-7806	81	19	.	.	PUNCT
ajst-7806	82	1	the	the	DET
ajst-7806	82	2	overall	overall	ADJ
ajst-7806	82	3	architecture	architecture	NOUN
ajst-7806	82	4	of	of	ADP
ajst-7806	82	5	the	the	DET
ajst-7806	82	6	proposed	propose	VERB
ajst-7806	82	7	network	network	NOUN
ajst-7806	82	8	framework	framework	NOUN
ajst-7806	82	9	is	be	AUX
ajst-7806	82	10	shown	show	VERB
ajst-7806	82	11	in	in	ADP
ajst-7806	82	12	fig	fig	NOUN
ajst-7806	82	13	.	.	PUNCT
ajst-7806	83	1	3	3	X
ajst-7806	83	2	.	.	X
ajst-7806	83	3	this	this	DET
ajst-7806	83	4	paper	paper	NOUN
ajst-7806	83	5	puts	put	VERB
ajst-7806	83	6	forward	forward	ADV
ajst-7806	83	7	a	a	DET
ajst-7806	83	8	framework	framework	NOUN
ajst-7806	83	9	based	base	VERB
ajst-7806	83	10	on	on	ADP
ajst-7806	83	11	oicr	oicr	NOUN
ajst-7806	83	12	,	,	PUNCT
ajst-7806	83	13	and	and	CCONJ
ajst-7806	83	14	introduces	introduce	VERB
ajst-7806	83	15	three	three	NUM
ajst-7806	83	16	modules	module	NOUN
ajst-7806	83	17	for	for	ADP
ajst-7806	83	18	generating	generate	VERB
ajst-7806	83	19	proposals	proposal	NOUN
ajst-7806	83	20	and	and	CCONJ
ajst-7806	83	21	filtering	filter	VERB
ajst-7806	83	22	them	they	PRON
ajst-7806	83	23	.	.	PUNCT
ajst-7806	84	1	firstly	firstly	ADV
ajst-7806	84	2	,	,	PUNCT
ajst-7806	84	3	high	high	ADJ
ajst-7806	84	4	-	-	PUNCT
ajst-7806	84	5	quality	quality	NOUN
ajst-7806	84	6	proposals	proposal	NOUN
ajst-7806	84	7	are	be	AUX
ajst-7806	84	8	generated	generate	VERB
ajst-7806	84	9	specifically	specifically	ADV
ajst-7806	84	10	for	for	ADP
ajst-7806	84	11	weakly	weakly	ADJ
ajst-7806	84	12	supervised	supervised	ADJ
ajst-7806	84	13	target	target	NOUN
ajst-7806	84	14	detection	detection	NOUN
ajst-7806	84	15	.	.	PUNCT
ajst-7806	85	1	for	for	ADP
ajst-7806	85	2	the	the	DET
ajst-7806	85	3	proposal	proposal	NOUN
ajst-7806	85	4	generation	generation	NOUN
ajst-7806	85	5	part	part	NOUN
ajst-7806	85	6	,	,	PUNCT
ajst-7806	85	7	the	the	DET
ajst-7806	85	8	selection	selection	NOUN
ajst-7806	85	9	search	search	NOUN
ajst-7806	85	10	algorithm	algorithm	NOUN
ajst-7806	85	11	is	be	AUX
ajst-7806	85	12	combined	combine	VERB
ajst-7806	85	13	with	with	ADP
ajst-7806	85	14	the	the	DET
ajst-7806	85	15	improved	improve	VERB
ajst-7806	85	16	gradient	gradient	NOUN
ajst-7806	85	17	-	-	PUNCT
ajst-7806	85	18	weighted	weight	VERB
ajst-7806	85	19	class	class	NOUN
ajst-7806	85	20	activation	activation	NOUN
ajst-7806	85	21	mapping	mapping	NOUN
ajst-7806	85	22	.	.	PUNCT
ajst-7806	86	1	on	on	ADP
ajst-7806	86	2	this	this	DET
ajst-7806	86	3	basis	basis	NOUN
ajst-7806	86	4	,	,	PUNCT
ajst-7806	86	5	an	an	DET
ajst-7806	86	6	attention	attention	NOUN
ajst-7806	86	7	module	module	NOUN
ajst-7806	86	8	is	be	AUX
ajst-7806	86	9	added	add	VERB
ajst-7806	86	10	to	to	PART
ajst-7806	86	11	extract	extract	VERB
ajst-7806	86	12	enhanced	enhanced	ADJ
ajst-7806	86	13	feature	feature	NOUN
ajst-7806	86	14	maps	map	NOUN
ajst-7806	86	15	from	from	ADP
ajst-7806	86	16	cnn	cnn	PROPN
ajst-7806	86	17	,	,	PUNCT
ajst-7806	86	18	and	and	CCONJ
ajst-7806	86	19	then	then	ADV
ajst-7806	86	20	the	the	DET
ajst-7806	86	21	enhanced	enhanced	ADJ
ajst-7806	86	22	feature	feature	NOUN
ajst-7806	86	23	maps	map	NOUN
ajst-7806	86	24	are	be	AUX
ajst-7806	86	25	sent	send	VERB
ajst-7806	86	26	to	to	ADP
ajst-7806	86	27	the	the	DET
ajst-7806	86	28	roi	roi	NOUN
ajst-7806	86	29	pool	pool	NOUN
ajst-7806	86	30	layer	layer	NOUN
ajst-7806	86	31	to	to	PART
ajst-7806	86	32	process	process	VERB
ajst-7806	86	33	the	the	DET
ajst-7806	86	34	generated	generate	VERB
ajst-7806	86	35	areas	area	NOUN
ajst-7806	86	36	.	.	PUNCT
ajst-7806	87	1	in	in	ADP
ajst-7806	87	2	the	the	DET
ajst-7806	87	3	proposal	proposal	NOUN
ajst-7806	87	4	selection	selection	NOUN
ajst-7806	87	5	module	module	NOUN
ajst-7806	87	6	,	,	PUNCT
ajst-7806	87	7	the	the	DET
ajst-7806	87	8	proposals	proposal	NOUN
ajst-7806	87	9	are	be	AUX
ajst-7806	87	10	screened	screen	VERB
ajst-7806	87	11	by	by	ADP
ajst-7806	87	12	combining	combine	VERB
ajst-7806	87	13	low	low	ADJ
ajst-7806	87	14	-	-	PUNCT
ajst-7806	87	15	level	level	NOUN
ajst-7806	87	16	semantic	semantic	ADJ
ajst-7806	87	17	information	information	NOUN
ajst-7806	87	18	with	with	ADP
ajst-7806	87	19	high	high	ADJ
ajst-7806	87	20	-	-	PUNCT
ajst-7806	87	21	level	level	NOUN
ajst-7806	87	22	semantic	semantic	ADJ
ajst-7806	87	23	information	information	NOUN
ajst-7806	87	24	,	,	PUNCT
ajst-7806	87	25	and	and	CCONJ
ajst-7806	87	26	sent	send	VERB
ajst-7806	87	27	to	to	ADP
ajst-7806	87	28	the	the	DET
ajst-7806	87	29	basic	basic	ADJ
ajst-7806	87	30	multi	multi	ADJ
ajst-7806	87	31	-	-	ADJ
ajst-7806	87	32	instance	instance	NOUN
ajst-7806	87	33	detector	detector	NOUN
ajst-7806	87	34	,	,	PUNCT
ajst-7806	87	35	the	the	DET
ajst-7806	87	36	k	k	NOUN
ajst-7806	87	37	-	-	PUNCT
ajst-7806	87	38	level	level	NOUN
ajst-7806	87	39	instance	instance	NOUN
ajst-7806	87	40	optimizer	optimizer	NOUN
ajst-7806	87	41	and	and	CCONJ
ajst-7806	87	42	the	the	DET
ajst-7806	87	43	bounding	bounding	NOUN
ajst-7806	87	44	box	box	NOUN
ajst-7806	87	45	regression	regression	NOUN
ajst-7806	87	46	branch	branch	NOUN
ajst-7806	87	47	for	for	ADP
ajst-7806	87	48	iterative	iterative	NOUN
ajst-7806	87	49	training	training	NOUN
ajst-7806	87	50	,	,	PUNCT
ajst-7806	87	51	so	so	SCONJ
ajst-7806	87	52	as	as	SCONJ
ajst-7806	87	53	to	to	PART
ajst-7806	87	54	improve	improve	VERB
ajst-7806	87	55	its	its	PRON
ajst-7806	87	56	performance	performance	NOUN
ajst-7806	87	57	.	.	PUNCT
ajst-7806	88	1	the	the	DET
ajst-7806	88	2	input	input	NOUN
ajst-7806	88	3	picture	picture	NOUN
ajst-7806	88	4	passes	pass	VERB
ajst-7806	88	5	through	through	ADP
ajst-7806	88	6	the	the	DET
ajst-7806	88	7	convolution	convolution	NOUN
ajst-7806	88	8	layer	layer	NOUN
ajst-7806	88	9	,	,	PUNCT
ajst-7806	88	10	relu	relu	NOUN
ajst-7806	88	11	activation	activation	NOUN
ajst-7806	88	12	function	function	NOUN
ajst-7806	88	13	and	and	CCONJ
ajst-7806	88	14	pooling	pool	VERB
ajst-7806	88	15	layer	layer	NOUN
ajst-7806	88	16	of	of	ADP
ajst-7806	88	17	convolutional	convolutional	ADJ
ajst-7806	88	18	neural	neural	ADJ
ajst-7806	88	19	network	network	NOUN
ajst-7806	88	20	to	to	PART
ajst-7806	88	21	generate	generate	VERB
ajst-7806	88	22	the	the	DET
ajst-7806	88	23	feature	feature	NOUN
ajst-7806	88	24	map	map	NOUN
ajst-7806	88	25	of	of	ADP
ajst-7806	88	26	the	the	DET
ajst-7806	88	27	image	image	NOUN
ajst-7806	88	28	,	,	PUNCT
ajst-7806	88	29	which	which	PRON
ajst-7806	88	30	is	be	AUX
ajst-7806	88	31	used	use	VERB
ajst-7806	88	32	to	to	PART
ajst-7806	88	33	extract	extract	VERB
ajst-7806	88	34	the	the	DET
ajst-7806	88	35	proposal	proposal	NOUN
ajst-7806	88	36	box	box	NOUN
ajst-7806	88	37	later	later	ADV
ajst-7806	88	38	.	.	PUNCT
ajst-7806	89	1	the	the	DET
ajst-7806	89	2	selection	selection	NOUN
ajst-7806	89	3	search	search	NOUN
ajst-7806	89	4	algorithm	algorithm	NOUN
ajst-7806	89	5	is	be	AUX
ajst-7806	89	6	combined	combine	VERB
ajst-7806	89	7	with	with	ADP
ajst-7806	89	8	grad	grad	ADJ
ajst-7806	89	9	cam++	cam++	PROPN
ajst-7806	89	10	to	to	PART
ajst-7806	89	11	generate	generate	VERB
ajst-7806	89	12	proposals	proposal	NOUN
ajst-7806	89	13	,	,	PUNCT
ajst-7806	89	14	and	and	CCONJ
ajst-7806	89	15	an	an	DET
ajst-7806	89	16	improved	improved	ADJ
ajst-7806	89	17	cbam	cbam	NOUN
ajst-7806	89	18	attention	attention	NOUN
ajst-7806	89	19	module	module	NOUN
ajst-7806	89	20	is	be	AUX
ajst-7806	89	21	added	add	VERB
ajst-7806	89	22	to	to	PART
ajst-7806	89	23	generate	generate	VERB
ajst-7806	89	24	an	an	DET
ajst-7806	89	25	enhanced	enhance	VERB
ajst-7806	89	26	feature	feature	NOUN
ajst-7806	89	27	map	map	NOUN
ajst-7806	89	28	.	.	PUNCT
ajst-7806	90	1	the	the	DET
ajst-7806	90	2	proposals	proposal	NOUN
ajst-7806	90	3	and	and	CCONJ
ajst-7806	90	4	the	the	DET
ajst-7806	90	5	enhanced	enhance	VERB
ajst-7806	90	6	feature	feature	NOUN
ajst-7806	90	7	map	map	NOUN
ajst-7806	90	8	are	be	AUX
ajst-7806	90	9	sent	send	VERB
ajst-7806	90	10	to	to	ADP
ajst-7806	90	11	the	the	DET
ajst-7806	90	12	roi	roi	NOUN
ajst-7806	90	13	pooling	pool	VERB
ajst-7806	90	14	layer	layer	NOUN
ajst-7806	90	15	to	to	PART
ajst-7806	90	16	generate	generate	VERB
ajst-7806	90	17	a	a	DET
ajst-7806	90	18	7×7	7×7	NUM
ajst-7806	90	19	roi	roi	NOUN
ajst-7806	90	20	pooled	pool	VERB
ajst-7806	90	21	feature	feature	NOUN
ajst-7806	90	22	.	.	PUNCT
ajst-7806	91	1	finally	finally	ADV
ajst-7806	91	2	,	,	PUNCT
ajst-7806	91	3	the	the	DET
ajst-7806	91	4	feature	feature	NOUN
ajst-7806	91	5	vector	vector	NOUN
ajst-7806	91	6	is	be	AUX
ajst-7806	91	7	processed	process	VERB
ajst-7806	91	8	by	by	ADP
ajst-7806	91	9	the	the	DET
ajst-7806	91	10	multi	multi	ADJ
ajst-7806	91	11	-	-	ADJ
ajst-7806	91	12	instance	instance	NOUN
ajst-7806	91	13	learning	learning	NOUN
ajst-7806	91	14	module	module	NOUN
ajst-7806	91	15	and	and	CCONJ
ajst-7806	91	16	the	the	DET
ajst-7806	91	17	refined	refined	ADJ
ajst-7806	91	18	branch	branch	NOUN
ajst-7806	91	19	instance	instance	NOUN
ajst-7806	91	20	detector	detector	NOUN
ajst-7806	91	21	module	module	NOUN
ajst-7806	91	22	for	for	ADP
ajst-7806	91	23	subsequent	subsequent	ADJ
ajst-7806	91	24	classification	classification	NOUN
ajst-7806	91	25	and	and	CCONJ
ajst-7806	91	26	boundary	boundary	ADJ
ajst-7806	91	27	box	box	PROPN
ajst-7806	91	28	regression	regression	NOUN
ajst-7806	91	29	,	,	PUNCT
ajst-7806	91	30	and	and	CCONJ
ajst-7806	91	31	the	the	DET
ajst-7806	91	32	object	object	NOUN
ajst-7806	91	33	category	category	NOUN
ajst-7806	91	34	and	and	CCONJ
ajst-7806	91	35	positioning	positioning	NOUN
ajst-7806	91	36	prediction	prediction	NOUN
ajst-7806	91	37	results	result	NOUN
ajst-7806	91	38	are	be	AUX
ajst-7806	91	39	output	output	ADJ
ajst-7806	91	40	.	.	PUNCT
ajst-7806	92	1	during	during	ADP
ajst-7806	92	2	the	the	DET
ajst-7806	92	3	forward	forward	ADJ
ajst-7806	92	4	propagation	propagation	NOUN
ajst-7806	92	5	of	of	ADP
ajst-7806	92	6	training	training	NOUN
ajst-7806	92	7	,	,	PUNCT
ajst-7806	92	8	the	the	DET
ajst-7806	92	9	extracted	extract	VERB
ajst-7806	92	10	proposal	proposal	NOUN
ajst-7806	92	11	features	feature	NOUN
ajst-7806	92	12	are	be	AUX
ajst-7806	92	13	sent	send	VERB
ajst-7806	92	14	to	to	ADP
ajst-7806	92	15	the	the	DET
ajst-7806	92	16	basic	basic	ADJ
ajst-7806	92	17	mil	mil	NOUN
ajst-7806	92	18	module	module	NOUN
ajst-7806	92	19	to	to	PART
ajst-7806	92	20	generate	generate	VERB
ajst-7806	92	21	proposal	proposal	NOUN
ajst-7806	92	22	score	score	NOUN
ajst-7806	92	23	matrices	matrix	NOUN
ajst-7806	92	24	.	.	PUNCT
ajst-7806	93	1	after	after	ADP
ajst-7806	93	2	the	the	DET
ajst-7806	93	3	proposal	proposal	NOUN
ajst-7806	93	4	selection	selection	NOUN
ajst-7806	93	5	module	module	NOUN
ajst-7806	93	6	,	,	PUNCT
ajst-7806	93	7	more	more	ADV
ajst-7806	93	8	plausible	plausible	ADJ
ajst-7806	93	9	positive	positive	ADJ
ajst-7806	93	10	proposals	proposal	NOUN
ajst-7806	93	11	are	be	AUX
ajst-7806	93	12	selected	select	VERB
ajst-7806	93	13	,	,	PUNCT
ajst-7806	93	14	and	and	CCONJ
ajst-7806	93	15	subsequently	subsequently	ADV
ajst-7806	93	16	,	,	PUNCT
ajst-7806	93	17	these	these	DET
ajst-7806	93	18	proposal	proposal	NOUN
ajst-7806	93	19	score	score	NOUN
ajst-7806	93	20	matrices	matrix	NOUN
ajst-7806	93	21	are	be	AUX
ajst-7806	93	22	used	use	VERB
ajst-7806	93	23	for	for	ADP
ajst-7806	93	24	140	140	NUM
ajst-7806	93	25	subsequent	subsequent	ADJ
ajst-7806	93	26	training	training	NOUN
ajst-7806	93	27	supervision	supervision	NOUN
ajst-7806	93	28	.	.	PUNCT
ajst-7806	94	1	3.1	3.1	NUM
ajst-7806	94	2	.	.	PUNCT
ajst-7806	94	3	proposal	proposal	NOUN
ajst-7806	94	4	generation	generation	NOUN
ajst-7806	94	5	at	at	ADP
ajst-7806	94	6	first	first	ADV
ajst-7806	94	7	,	,	PUNCT
ajst-7806	94	8	the	the	DET
ajst-7806	94	9	vgg16	vgg16	NOUN
ajst-7806	94	10	model	model	NOUN
ajst-7806	94	11	is	be	AUX
ajst-7806	94	12	used	use	VERB
ajst-7806	94	13	to	to	PART
ajst-7806	94	14	train	train	VERB
ajst-7806	94	15	the	the	DET
ajst-7806	94	16	basic	basic	ADJ
ajst-7806	94	17	multiinstance	multiinstance	NOUN
ajst-7806	94	18	classifier	classifier	NOUN
ajst-7806	94	19	with	with	ADP
ajst-7806	94	20	only	only	ADJ
ajst-7806	94	21	image	image	NOUN
ajst-7806	94	22	-	-	PUNCT
ajst-7806	94	23	level	level	NOUN
ajst-7806	94	24	labels	label	NOUN
ajst-7806	94	25	,	,	PUNCT
ajst-7806	94	26	and	and	CCONJ
ajst-7806	94	27	the	the	DET
ajst-7806	94	28	multiclass	multiclass	PROPN
ajst-7806	94	29	cross	cross	PROPN
ajst-7806	94	30	entropy	entropy	PROPN
ajst-7806	94	31	loss	loss	NOUN
ajst-7806	94	32	function	function	NOUN
ajst-7806	94	33	is	be	AUX
ajst-7806	94	34	used	use	VERB
ajst-7806	94	35	in	in	ADP
ajst-7806	94	36	eq.1	eq.1	PROPN
ajst-7806	94	37	:	:	PUNCT
ajst-7806	94	38	i	i	PRON
ajst-7806	94	39	1	1	NUM
ajst-7806	94	40	(	(	PUNCT
ajst-7806	94	41	log	log	NOUN
ajst-7806	94	42	(	(	PUNCT
ajst-7806	94	43	1	1	NUM
ajst-7806	94	44	)	)	PUNCT
ajst-7806	94	45	log(1	log(1	NOUN
ajst-7806	94	46	)	)	PUNCT
ajst-7806	94	47	)	)	PUNCT
ajst-7806	95	1	c	c	X
ajst-7806	96	1	i	i	PRON
ajst-7806	96	2	i	i	PRON
ajst-7806	97	1	i	i	PRON
ajst-7806	98	1	i	i	PRON
ajst-7806	98	2	s	s	VERB
ajst-7806	99	1	y	y	PROPN
ajst-7806	100	1	p	p	NOUN
ajst-7806	101	1	y	y	PROPN
ajst-7806	102	1	p	p	NOUN
ajst-7806	103	1			PROPN
ajst-7806	103	2			PROPN
ajst-7806	103	3			PROPN
ajst-7806	103	4			VERB
ajst-7806	103	5			PROPN
ajst-7806	103	6			PROPN
ajst-7806	103	7	(	(	PUNCT
ajst-7806	103	8	1	1	NUM
ajst-7806	103	9	)	)	PUNCT
ajst-7806	103	10	where	where	SCONJ
ajst-7806	103	11	c	c	NOUN
ajst-7806	103	12	is	be	AUX
ajst-7806	103	13	the	the	DET
ajst-7806	103	14	total	total	ADJ
ajst-7806	103	15	number	number	NOUN
ajst-7806	103	16	of	of	ADP
ajst-7806	103	17	image	image	NOUN
ajst-7806	103	18	categories	category	NOUN
ajst-7806	103	19	,	,	PUNCT
ajst-7806	103	20	iy	iy	PROPN
ajst-7806	103	21	is	be	AUX
ajst-7806	103	22	the	the	DET
ajst-7806	103	23	label	label	NOUN
ajst-7806	103	24	representation	representation	NOUN
ajst-7806	103	25	of	of	ADP
ajst-7806	103	26	the	the	DET
ajst-7806	103	27	i	i	PROPN
ajst-7806	103	28	th	th	NOUN
ajst-7806	103	29	image	image	NOUN
ajst-7806	103	30	category	category	NOUN
ajst-7806	103	31	,	,	PUNCT
ajst-7806	103	32	and	and	CCONJ
ajst-7806	103	33	ip	ip	NOUN
ajst-7806	103	34	is	be	AUX
ajst-7806	103	35	the	the	DET
ajst-7806	103	36	prediction	prediction	NOUN
ajst-7806	103	37	result	result	NOUN
ajst-7806	103	38	of	of	ADP
ajst-7806	103	39	the	the	DET
ajst-7806	103	40	i	i	PROPN
ajst-7806	103	41	-	-	PUNCT
ajst-7806	103	42	th	th	X
ajst-7806	103	43	sigmoid	sigmoid	NOUN
ajst-7806	103	44	classifier	classifier	NOUN
ajst-7806	103	45	,	,	PUNCT
ajst-7806	103	46	which	which	PRON
ajst-7806	103	47	finally	finally	ADV
ajst-7806	103	48	constitutes	constitute	VERB
ajst-7806	103	49	this	this	DET
ajst-7806	103	50	loss	loss	NOUN
ajst-7806	103	51	function	function	NOUN
ajst-7806	103	52	.	.	PUNCT
ajst-7806	104	1	for	for	ADP
ajst-7806	104	2	each	each	DET
ajst-7806	104	3	image	image	NOUN
ajst-7806	104	4	containing	contain	VERB
ajst-7806	104	5	category	category	NOUN
ajst-7806	104	6	c	c	NOUN
ajst-7806	104	7	,	,	PUNCT
ajst-7806	104	8	a	a	DET
ajst-7806	104	9	group	group	NOUN
ajst-7806	104	10	of	of	ADP
ajst-7806	104	11	feature	feature	NOUN
ajst-7806	104	12	maps	map	NOUN
ajst-7806	104	13	are	be	AUX
ajst-7806	104	14	weighted	weight	VERB
ajst-7806	104	15	and	and	CCONJ
ajst-7806	104	16	combined	combine	VERB
ajst-7806	104	17	by	by	ADP
ajst-7806	104	18	using	use	VERB
ajst-7806	104	19	the	the	DET
ajst-7806	104	20	basic	basic	ADJ
ajst-7806	104	21	multi	multi	ADJ
ajst-7806	104	22	-	-	ADJ
ajst-7806	104	23	instance	instance	NOUN
ajst-7806	104	24	classifier	classifier	NOUN
ajst-7806	104	25	to	to	PART
ajst-7806	104	26	obtain	obtain	VERB
ajst-7806	104	27	its	its	PRON
ajst-7806	104	28	category	category	NOUN
ajst-7806	104	29	-	-	PUNCT
ajst-7806	104	30	specific	specific	ADJ
ajst-7806	104	31	activation	activation	NOUN
ajst-7806	104	32	map	map	NOUN
ajst-7806	104	33	,	,	PUNCT
ajst-7806	104	34	as	as	SCONJ
ajst-7806	104	35	shown	show	VERB
ajst-7806	104	36	in	in	ADP
ajst-7806	104	37	eq.2	eq.2	NOUN
ajst-7806	104	38	:	:	PUNCT
ajst-7806	104	39	re	re	X
ajst-7806	104	40	(	(	PUNCT
ajst-7806	104	41	)	)	PUNCT
ajst-7806	105	1	c	c	NOUN
ajst-7806	105	2	c	c	NOUN
ajst-7806	105	3	k	k	PROPN
ajst-7806	106	1	k	k	PROPN
ajst-7806	106	2	k	k	PROPN
ajst-7806	107	1	m	m	VERB
ajst-7806	107	2	lu	lu	VERB
ajst-7806	107	3	w	w	ADJ
ajst-7806	107	4	a	a	NOUN
ajst-7806	107	5			X
ajst-7806	107	6	(	(	PUNCT
ajst-7806	107	7	2	2	NUM
ajst-7806	107	8	)	)	PUNCT
ajst-7806	107	9	among	among	ADP
ajst-7806	107	10	them	they	PRON
ajst-7806	107	11	,	,	PUNCT
ajst-7806	107	12	ka	ka	PROPN
ajst-7806	107	13	is	be	AUX
ajst-7806	107	14	the	the	DET
ajst-7806	107	15	k	k	PROPN
ajst-7806	107	16	-	-	PUNCT
ajst-7806	107	17	th	th	VERB
ajst-7806	107	18	convolution	convolution	NOUN
ajst-7806	107	19	feature	feature	NOUN
ajst-7806	107	20	map	map	NOUN
ajst-7806	107	21	,	,	PUNCT
ajst-7806	107	22	and	and	CCONJ
ajst-7806	107	23	c	c	X
ajst-7806	107	24	kw	kw	PROPN
ajst-7806	107	25	is	be	AUX
ajst-7806	107	26	the	the	DET
ajst-7806	107	27	importance	importance	NOUN
ajst-7806	107	28	of	of	ADP
ajst-7806	107	29	the	the	DET
ajst-7806	107	30	feature	feature	NOUN
ajst-7806	107	31	map	map	NOUN
ajst-7806	107	32	ka	ka	PROPN
ajst-7806	107	33	of	of	ADP
ajst-7806	107	34	class	class	PROPN
ajst-7806	107	35	c	c	PROPN
ajst-7806	107	36	in	in	ADP
ajst-7806	107	37	the	the	DET
ajst-7806	107	38	object	object	NOUN
ajst-7806	107	39	,	,	PUNCT
ajst-7806	107	40	which	which	PRON
ajst-7806	107	41	is	be	AUX
ajst-7806	107	42	calculated	calculate	VERB
ajst-7806	107	43	as	as	SCONJ
ajst-7806	107	44	follows	follow	VERB
ajst-7806	107	45	in	in	ADP
ajst-7806	107	46	eq.3	eq.3	PROPN
ajst-7806	107	47	:	:	PUNCT
ajst-7806	107	48	k	k	X
ajst-7806	107	49	re	re	X
ajst-7806	107	50	(	(	PUNCT
ajst-7806	107	51	)	)	PUNCT
ajst-7806	107	52	(	(	PUNCT
ajst-7806	107	53	)	)	PUNCT
ajst-7806	108	1	c	c	NOUN
ajst-7806	108	2	kc	kc	PROPN
ajst-7806	108	3	c	c	PROPN
ajst-7806	108	4	k	k	PROPN
ajst-7806	109	1	ij	ij	INTJ
ajst-7806	110	1	i	i	PRON
ajst-7806	110	2	j	j	PROPN
ajst-7806	111	1	ij	ij	INTJ
ajst-7806	111	2	y	y	PROPN
ajst-7806	111	3	w	w	PROPN
ajst-7806	111	4	lu	lu	PROPN
ajst-7806	111	5	a	a	DET
ajst-7806	111	6			X
ajst-7806	111	7			ADJ
ajst-7806	111	8			ADJ
ajst-7806	111	9			ADJ
ajst-7806	111	10			NOUN
ajst-7806	111	11			PRON
ajst-7806	111	12	(	(	PUNCT
ajst-7806	111	13	3	3	X
ajst-7806	111	14	)	)	PUNCT
ajst-7806	111	15	grad	grad	NOUN
ajst-7806	111	16	-	-	PUNCT
ajst-7806	111	17	cam++	cam++	PROPN
ajst-7806	111	18	further	far	ADV
ajst-7806	111	19	improves	improve	VERB
ajst-7806	111	20	on	on	ADP
ajst-7806	111	21	grad	grad	NOUN
ajst-7806	111	22	-	-	PUNCT
ajst-7806	111	23	cam	cam	NOUN
ajst-7806	111	24	,	,	PUNCT
ajst-7806	111	25	which	which	PRON
ajst-7806	111	26	can	can	AUX
ajst-7806	111	27	better	well	ADV
ajst-7806	111	28	locate	locate	VERB
ajst-7806	111	29	the	the	DET
ajst-7806	111	30	complete	complete	ADJ
ajst-7806	111	31	object	object	NOUN
ajst-7806	111	32	position	position	NOUN
ajst-7806	111	33	compared	compare	VERB
ajst-7806	111	34	to	to	ADP
ajst-7806	111	35	gradcam	gradcam	PROPN
ajst-7806	111	36	,	,	PUNCT
ajst-7806	111	37	grad	grad	PROPN
ajst-7806	111	38	-	-	PUNCT
ajst-7806	111	39	cam++	cam++	PROPN
ajst-7806	111	40	improves	improve	VERB
ajst-7806	111	41	the	the	DET
ajst-7806	111	42	representation	representation	NOUN
ajst-7806	111	43	when	when	SCONJ
ajst-7806	111	44	there	there	PRON
ajst-7806	111	45	are	be	VERB
ajst-7806	111	46	multiple	multiple	ADJ
ajst-7806	111	47	targets	target	NOUN
ajst-7806	111	48	in	in	ADP
ajst-7806	111	49	the	the	DET
ajst-7806	111	50	image	image	NOUN
ajst-7806	111	51	.	.	PUNCT
ajst-7806	112	1	it	it	PRON
ajst-7806	112	2	obtains	obtain	VERB
ajst-7806	112	3	the	the	DET
ajst-7806	112	4	importance	importance	NOUN
ajst-7806	112	5	of	of	ADP
ajst-7806	112	6	each	each	DET
ajst-7806	112	7	pixel	pixel	NOUN
ajst-7806	112	8	in	in	ADP
ajst-7806	112	9	the	the	DET
ajst-7806	112	10	feature	feature	NOUN
ajst-7806	112	11	map	map	NOUN
ajst-7806	112	12	mainly	mainly	ADV
ajst-7806	112	13	by	by	ADP
ajst-7806	112	14	adding	add	VERB
ajst-7806	112	15	relu	relu	NOUN
ajst-7806	112	16	and	and	CCONJ
ajst-7806	112	17	pixel	pixel	ADJ
ajst-7806	112	18	-	-	PUNCT
ajst-7806	112	19	level	level	NOUN
ajst-7806	112	20	weighting	weighting	NOUN
ajst-7806	112	21	to	to	ADP
ajst-7806	112	22	the	the	DET
ajst-7806	112	23	weights	weight	NOUN
ajst-7806	112	24	of	of	ADP
ajst-7806	112	25	the	the	DET
ajst-7806	112	26	feature	feature	NOUN
ajst-7806	112	27	map	map	NOUN
ajst-7806	112	28	output	output	NOUN
ajst-7806	112	29	of	of	ADP
ajst-7806	112	30	the	the	DET
ajst-7806	112	31	corresponding	correspond	VERB
ajst-7806	112	32	classification	classification	NOUN
ajst-7806	112	33	to	to	PART
ajst-7806	112	34	find	find	VERB
ajst-7806	112	35	out	out	ADP
ajst-7806	112	36	more	more	ADV
ajst-7806	112	37	accurate	accurate	ADJ
ajst-7806	112	38	position	position	NOUN
ajst-7806	112	39	information	information	NOUN
ajst-7806	112	40	.	.	PUNCT
ajst-7806	113	1	this	this	DET
ajst-7806	113	2	paper	paper	NOUN
ajst-7806	113	3	is	be	AUX
ajst-7806	113	4	mainly	mainly	ADV
ajst-7806	113	5	based	base	VERB
ajst-7806	113	6	on	on	ADP
ajst-7806	113	7	the	the	DET
ajst-7806	113	8	combination	combination	NOUN
ajst-7806	113	9	of	of	ADP
ajst-7806	113	10	grad	grad	NOUN
ajst-7806	113	11	-	-	PUNCT
ajst-7806	113	12	cam++	cam++	PROPN
ajst-7806	113	13	and	and	CCONJ
ajst-7806	113	14	ss	ss	NOUN
ajst-7806	113	15	to	to	PART
ajst-7806	113	16	generate	generate	VERB
ajst-7806	113	17	a	a	DET
ajst-7806	113	18	large	large	ADJ
ajst-7806	113	19	number	number	NOUN
ajst-7806	113	20	of	of	ADP
ajst-7806	113	21	object	object	NOUN
ajst-7806	113	22	proposals	proposal	NOUN
ajst-7806	113	23	with	with	ADP
ajst-7806	113	24	higher	high	ADJ
ajst-7806	113	25	target	target	NOUN
ajst-7806	113	26	overlap	overlap	NOUN
ajst-7806	113	27	based	base	VERB
ajst-7806	113	28	on	on	ADP
ajst-7806	113	29	specific	specific	ADJ
ajst-7806	113	30	categories	category	NOUN
ajst-7806	113	31	.	.	PUNCT
ajst-7806	114	1	3.2	3.2	NUM
ajst-7806	114	2	.	.	PUNCT
ajst-7806	114	3	attention	attention	NOUN
ajst-7806	114	4	module	module	NOUN
ajst-7806	114	5	in	in	ADP
ajst-7806	114	6	order	order	NOUN
ajst-7806	114	7	to	to	PART
ajst-7806	114	8	better	well	ADV
ajst-7806	114	9	generate	generate	VERB
ajst-7806	114	10	high	high	ADJ
ajst-7806	114	11	quality	quality	NOUN
ajst-7806	114	12	proposal	proposal	NOUN
ajst-7806	114	13	candidate	candidate	NOUN
ajst-7806	114	14	frames	frame	NOUN
ajst-7806	114	15	,	,	PUNCT
ajst-7806	114	16	an	an	DET
ajst-7806	114	17	attention	attention	NOUN
ajst-7806	114	18	module	module	NOUN
ajst-7806	114	19	is	be	AUX
ajst-7806	114	20	added	add	VERB
ajst-7806	114	21	on	on	ADP
ajst-7806	114	22	top	top	NOUN
ajst-7806	114	23	of	of	ADP
ajst-7806	114	24	the	the	DET
ajst-7806	114	25	previously	previously	ADV
ajst-7806	114	26	described	describe	VERB
ajst-7806	114	27	proposal	proposal	NOUN
ajst-7806	114	28	generation	generation	NOUN
ajst-7806	114	29	method	method	NOUN
ajst-7806	114	30	,	,	PUNCT
ajst-7806	114	31	starting	start	VERB
ajst-7806	114	32	with	with	ADP
ajst-7806	114	33	a	a	DET
ajst-7806	114	34	brief	brief	ADJ
ajst-7806	114	35	description	description	NOUN
ajst-7806	114	36	of	of	ADP
ajst-7806	114	37	the	the	DET
ajst-7806	114	38	spatial	spatial	ADJ
ajst-7806	114	39	attention	attention	NOUN
ajst-7806	114	40	structure	structure	NOUN
ajst-7806	114	41	.	.	PUNCT
ajst-7806	115	1	first	first	ADV
ajst-7806	115	2	,	,	PUNCT
ajst-7806	115	3	the	the	DET
ajst-7806	115	4	proposed	propose	VERB
ajst-7806	115	5	feature	feature	NOUN
ajst-7806	115	6	maps	map	NOUN
ajst-7806	115	7	generated	generate	VERB
ajst-7806	115	8	from	from	ADP
ajst-7806	115	9	the	the	DET
ajst-7806	115	10	ss	ss	NOUN
ajst-7806	115	11	-	-	PUNCT
ajst-7806	115	12	based	base	VERB
ajst-7806	115	13	algorithm	algorithm	NOUN
ajst-7806	115	14	combined	combine	VERB
ajst-7806	115	15	with	with	ADP
ajst-7806	115	16	grad	grad	ADJ
ajst-7806	115	17	cam++	cam++	PROPN
ajst-7806	115	18	,	,	PUNCT
ajst-7806	115	19	which	which	PRON
ajst-7806	115	20	will	will	AUX
ajst-7806	115	21	be	be	AUX
ajst-7806	115	22	used	use	VERB
ajst-7806	115	23	as	as	ADP
ajst-7806	115	24	input	input	NOUN
ajst-7806	115	25	to	to	ADP
ajst-7806	115	26	the	the	DET
ajst-7806	115	27	attention	attention	NOUN
ajst-7806	115	28	module	module	NOUN
ajst-7806	115	29	,	,	PUNCT
ajst-7806	115	30	are	be	AUX
ajst-7806	115	31	then	then	ADV
ajst-7806	115	32	augmented	augment	VERB
ajst-7806	115	33	by	by	ADP
ajst-7806	115	34	a	a	DET
ajst-7806	115	35	modified	modify	VERB
ajst-7806	115	36	cbam	cbam	NOUN
ajst-7806	115	37	module	module	NOUN
ajst-7806	115	38	.	.	PUNCT
ajst-7806	116	1	figure	figure	NOUN
ajst-7806	116	2	4	4	NUM
ajst-7806	116	3	.	.	PUNCT
ajst-7806	116	4	cbam	cbam	NOUN
ajst-7806	116	5	attention	attention	NOUN
ajst-7806	116	6	module	module	NOUN
ajst-7806	116	7	as	as	SCONJ
ajst-7806	116	8	shown	show	VERB
ajst-7806	116	9	in	in	ADP
ajst-7806	116	10	fig	fig	NOUN
ajst-7806	116	11	.	.	PUNCT
ajst-7806	117	1	4	4	NUM
ajst-7806	117	2	,	,	PUNCT
ajst-7806	117	3	this	this	PRON
ajst-7806	117	4	is	be	AUX
ajst-7806	117	5	a	a	DET
ajst-7806	117	6	schematic	schematic	ADJ
ajst-7806	117	7	diagram	diagram	NOUN
ajst-7806	117	8	of	of	ADP
ajst-7806	117	9	the	the	DET
ajst-7806	117	10	structure	structure	NOUN
ajst-7806	117	11	of	of	ADP
ajst-7806	117	12	cbam	cbam	NOUN
ajst-7806	117	13	.	.	PUNCT
ajst-7806	118	1	first	first	ADV
ajst-7806	118	2	,	,	PUNCT
ajst-7806	118	3	the	the	DET
ajst-7806	118	4	size	size	NOUN
ajst-7806	118	5	of	of	ADP
ajst-7806	118	6	the	the	DET
ajst-7806	118	7	feature	feature	NOUN
ajst-7806	118	8	map	map	NOUN
ajst-7806	118	9	f	f	PROPN
ajst-7806	118	10	is	be	AUX
ajst-7806	118	11	h×w×c	h×w×c	VERB
ajst-7806	118	12	.	.	PUNCT
ajst-7806	119	1	then	then	ADV
ajst-7806	119	2	,	,	PUNCT
ajst-7806	119	3	the	the	DET
ajst-7806	119	4	global	global	ADJ
ajst-7806	119	5	information	information	NOUN
ajst-7806	119	6	is	be	AUX
ajst-7806	119	7	extracted	extract	VERB
ajst-7806	119	8	through	through	ADP
ajst-7806	119	9	the	the	DET
ajst-7806	119	10	global	global	ADJ
ajst-7806	119	11	average	average	ADJ
ajst-7806	119	12	pooling	pooling	NOUN
ajst-7806	119	13	layer	layer	NOUN
ajst-7806	119	14	and	and	CCONJ
ajst-7806	119	15	the	the	DET
ajst-7806	119	16	maximum	maximum	ADJ
ajst-7806	119	17	pooling	pool	VERB
ajst-7806	119	18	layer	layer	NOUN
ajst-7806	119	19	based	base	VERB
ajst-7806	119	20	on	on	ADP
ajst-7806	119	21	width	width	NOUN
ajst-7806	119	22	and	and	CCONJ
ajst-7806	119	23	height	height	NOUN
ajst-7806	119	24	to	to	PART
ajst-7806	119	25	generate	generate	VERB
ajst-7806	119	26	a	a	DET
ajst-7806	119	27	1×1×c	1×1×c	NUM
ajst-7806	119	28	feature	feature	NOUN
ajst-7806	119	29	map	map	NOUN
ajst-7806	119	30	and	and	CCONJ
ajst-7806	119	31	fed	feed	VERB
ajst-7806	119	32	into	into	ADP
ajst-7806	119	33	a	a	DET
ajst-7806	119	34	two	two	NUM
ajst-7806	119	35	-	-	PUNCT
ajst-7806	119	36	layer	layer	NOUN
ajst-7806	119	37	neural	neural	ADJ
ajst-7806	119	38	network	network	NOUN
ajst-7806	119	39	with	with	ADP
ajst-7806	119	40	shared	share	VERB
ajst-7806	119	41	weights	weight	NOUN
ajst-7806	119	42	,	,	PUNCT
ajst-7806	119	43	i.e.	i.e.	X
ajst-7806	119	44	,	,	PUNCT
ajst-7806	119	45	a	a	DET
ajst-7806	119	46	multi	multi	ADJ
ajst-7806	119	47	-	-	ADJ
ajst-7806	119	48	layer	layer	ADJ
ajst-7806	119	49	perceptron	perceptron	NOUN
ajst-7806	119	50	(	(	PUNCT
ajst-7806	119	51	mlp	mlp	PROPN
ajst-7806	119	52	,	,	PUNCT
ajst-7806	119	53	multi	multi	ADJ
ajst-7806	119	54	-	-	ADJ
ajst-7806	119	55	layer	layer	ADJ
ajst-7806	119	56	perceptron	perceptron	PROPN
ajst-7806	119	57	)	)	PUNCT
ajst-7806	119	58	,	,	PUNCT
ajst-7806	119	59	which	which	PRON
ajst-7806	119	60	learns	learn	VERB
ajst-7806	119	61	through	through	ADP
ajst-7806	119	62	inter	inter	ADJ
ajst-7806	119	63	-	-	ADJ
ajst-7806	119	64	channel	channel	NOUN
ajst-7806	119	65	dependencies	dependency	NOUN
ajst-7806	119	66	.	.	PUNCT
ajst-7806	120	1	dimensionality	dimensionality	NOUN
ajst-7806	120	2	reduction	reduction	NOUN
ajst-7806	120	3	is	be	AUX
ajst-7806	120	4	achieved	achieve	VERB
ajst-7806	120	5	between	between	ADP
ajst-7806	120	6	the	the	DET
ajst-7806	120	7	two	two	NUM
ajst-7806	120	8	neural	neural	ADJ
ajst-7806	120	9	layers	layer	NOUN
ajst-7806	120	10	by	by	ADP
ajst-7806	120	11	compression	compression	NOUN
ajst-7806	120	12	ratio	ratio	NOUN
ajst-7806	120	13	r.	r.	PROPN
ajst-7806	120	14	the	the	DET
ajst-7806	120	15	channel	channel	PROPN
ajst-7806	120	16	attention	attention	NOUN
ajst-7806	120	17	weighting	weighting	NOUN
ajst-7806	120	18	factor	factor	NOUN
ajst-7806	120	19	equation	equation	NOUN
ajst-7806	120	20	is	be	AUX
ajst-7806	120	21	shown	show	VERB
ajst-7806	120	22	in	in	ADP
ajst-7806	120	23	eq.4	eq.4	PROPN
ajst-7806	120	24	:	:	PUNCT
ajst-7806	120	25	1	1	NUM
ajst-7806	120	26	0	0	NUM
ajst-7806	120	27	1	1	NUM
ajst-7806	120	28	0	0	NUM
ajst-7806	120	29	max	max	NOUN
ajst-7806	120	30	(	(	PUNCT
ajst-7806	120	31	)	)	PUNCT
ajst-7806	120	32	(	(	PUNCT
ajst-7806	120	33	(	(	PUNCT
ajst-7806	120	34	(	(	PUNCT
ajst-7806	120	35	)	)	PUNCT
ajst-7806	120	36	)	)	PUNCT
ajst-7806	120	37	(	(	PUNCT
ajst-7806	120	38	(	(	PUNCT
ajst-7806	120	39	)	)	PUNCT
ajst-7806	120	40	)	)	PUNCT
ajst-7806	120	41	)	)	PUNCT
ajst-7806	121	1	(	(	PUNCT
ajst-7806	121	2	(	(	PUNCT
ajst-7806	121	3	(	(	PUNCT
ajst-7806	121	4	)	)	PUNCT
ajst-7806	121	5	)	)	PUNCT
ajst-7806	121	6	(	(	PUNCT
ajst-7806	121	7	(	(	PUNCT
ajst-7806	121	8	)	)	PUNCT
ajst-7806	121	9	)	)	PUNCT
ajst-7806	121	10	)	)	PUNCT
ajst-7806	122	1	c	c	NOUN
ajst-7806	123	1	c	c	X
ajst-7806	123	2	c	c	PROPN
ajst-7806	123	3	avg	avg	PROPN
ajst-7806	123	4	m	m	PROPN
ajst-7806	123	5	f	f	PROPN
ajst-7806	123	6	mlp	mlp	PROPN
ajst-7806	123	7	avgpool	avgpool	PROPN
ajst-7806	123	8	f	f	PROPN
ajst-7806	124	1	mlp	mlp	PROPN
ajst-7806	124	2	maxpool	maxpool	PROPN
ajst-7806	124	3	f	f	PROPN
ajst-7806	124	4	w	w	PROPN
ajst-7806	124	5	w	w	PROPN
ajst-7806	124	6	f	f	PROPN
ajst-7806	124	7	w	w	PROPN
ajst-7806	124	8	w	w	PROPN
ajst-7806	124	9	f	f	PROPN
ajst-7806	124	10			PROPN
ajst-7806	124	11			PROPN
ajst-7806	124	12			NOUN
ajst-7806	124	13			ADV
ajst-7806	124	14			NUM
ajst-7806	124	15			X
ajst-7806	124	16	(	(	PUNCT
ajst-7806	124	17	4	4	NUM
ajst-7806	124	18	)	)	PUNCT
ajst-7806	124	19	1	1	NUM
ajst-7806	124	20	w	w	NOUN
ajst-7806	124	21	and	and	CCONJ
ajst-7806	124	22	0	0	NUM
ajst-7806	124	23	w	w	NOUN
ajst-7806	124	24	is	be	AUX
ajst-7806	124	25	the	the	DET
ajst-7806	124	26	full	full	ADJ
ajst-7806	124	27	connection	connection	NOUN
ajst-7806	124	28	weight	weight	NOUN
ajst-7806	124	29	of	of	ADP
ajst-7806	124	30	two	two	NUM
ajst-7806	124	31	layers	layer	NOUN
ajst-7806	124	32	contained	contain	VERB
ajst-7806	124	33	in	in	ADP
ajst-7806	124	34	mlp	mlp	PROPN
ajst-7806	124	35	,	,	PUNCT
ajst-7806	124	36	with	with	ADP
ajst-7806	124	37	hidden	hidden	ADJ
ajst-7806	124	38	layer	layer	NOUN
ajst-7806	124	39	and	and	CCONJ
ajst-7806	124	40	relu	relu	NOUN
ajst-7806	124	41	activation	activation	NOUN
ajst-7806	124	42	function	function	NOUN
ajst-7806	124	43	in	in	ADP
ajst-7806	124	44	the	the	DET
ajst-7806	124	45	middle	middle	NOUN
ajst-7806	124	46	,	,	PUNCT
ajst-7806	124	47			PROPN
ajst-7806	124	48	represents	represent	VERB
ajst-7806	124	49	sigmoid	sigmoid	NOUN
ajst-7806	124	50	activation	activation	NOUN
ajst-7806	124	51	function	function	NOUN
ajst-7806	124	52	.	.	PUNCT
ajst-7806	125	1	as	as	SCONJ
ajst-7806	125	2	shown	show	VERB
ajst-7806	125	3	in	in	ADP
ajst-7806	125	4	figure	figure	NOUN
ajst-7806	125	5	4	4	NUM
ajst-7806	125	6	,	,	PUNCT
ajst-7806	125	7	spatial	spatial	ADJ
ajst-7806	125	8	attention	attention	NOUN
ajst-7806	125	9	takes	take	VERB
ajst-7806	125	10	the	the	DET
ajst-7806	125	11	output	output	NOUN
ajst-7806	125	12	characteristic	characteristic	ADJ
ajst-7806	125	13	map	map	NOUN
ajst-7806	125	14	of	of	ADP
ajst-7806	125	15	channel	channel	NOUN
ajst-7806	125	16	attention	attention	NOUN
ajst-7806	125	17	module	module	NOUN
ajst-7806	125	18	as	as	ADP
ajst-7806	125	19	the	the	DET
ajst-7806	125	20	input	input	NOUN
ajst-7806	125	21	characteristic	characteristic	ADJ
ajst-7806	125	22	map	map	NOUN
ajst-7806	125	23	of	of	ADP
ajst-7806	125	24	this	this	DET
ajst-7806	125	25	module	module	NOUN
ajst-7806	125	26	,	,	PUNCT
ajst-7806	125	27	focusing	focus	VERB
ajst-7806	125	28	on	on	ADP
ajst-7806	125	29	the	the	DET
ajst-7806	125	30	most	most	ADV
ajst-7806	125	31	informative	informative	ADJ
ajst-7806	125	32	part	part	NOUN
ajst-7806	125	33	,	,	PUNCT
ajst-7806	125	34	which	which	PRON
ajst-7806	125	35	is	be	AUX
ajst-7806	125	36	a	a	DET
ajst-7806	125	37	supplement	supplement	NOUN
ajst-7806	125	38	to	to	PART
ajst-7806	125	39	channel	channel	VERB
ajst-7806	125	40	attention	attention	NOUN
ajst-7806	125	41	.	.	PUNCT
ajst-7806	126	1	firstly	firstly	ADV
ajst-7806	126	2	,	,	PUNCT
ajst-7806	126	3	the	the	DET
ajst-7806	126	4	maximum	maximum	ADJ
ajst-7806	126	5	pooling	pooling	NOUN
ajst-7806	126	6	and	and	CCONJ
ajst-7806	126	7	average	average	ADJ
ajst-7806	126	8	pooling	pooling	NOUN
ajst-7806	126	9	are	be	AUX
ajst-7806	126	10	carried	carry	VERB
ajst-7806	126	11	141	141	NUM
ajst-7806	126	12	out	out	ADP
ajst-7806	126	13	in	in	ADP
ajst-7806	126	14	the	the	DET
ajst-7806	126	15	channel	channel	NOUN
ajst-7806	126	16	dimension	dimension	NOUN
ajst-7806	126	17	to	to	PART
ajst-7806	126	18	fuse	fuse	VERB
ajst-7806	126	19	the	the	DET
ajst-7806	126	20	information	information	NOUN
ajst-7806	126	21	of	of	ADP
ajst-7806	126	22	different	different	ADJ
ajst-7806	126	23	channels	channel	NOUN
ajst-7806	126	24	in	in	ADP
ajst-7806	126	25	the	the	DET
ajst-7806	126	26	same	same	ADJ
ajst-7806	126	27	position	position	NOUN
ajst-7806	126	28	,	,	PUNCT
ajst-7806	126	29	which	which	PRON
ajst-7806	126	30	is	be	AUX
ajst-7806	126	31	used	use	VERB
ajst-7806	126	32	as	as	ADP
ajst-7806	126	33	the	the	DET
ajst-7806	126	34	feature	feature	NOUN
ajst-7806	126	35	information	information	NOUN
ajst-7806	126	36	of	of	ADP
ajst-7806	126	37	this	this	DET
ajst-7806	126	38	position	position	NOUN
ajst-7806	126	39	.	.	PUNCT
ajst-7806	127	1	then	then	ADV
ajst-7806	127	2	,	,	PUNCT
ajst-7806	127	3	the	the	DET
ajst-7806	127	4	position	position	NOUN
ajst-7806	127	5	information	information	NOUN
ajst-7806	127	6	obtained	obtain	VERB
ajst-7806	127	7	by	by	ADP
ajst-7806	127	8	the	the	DET
ajst-7806	127	9	maximum	maximum	ADJ
ajst-7806	127	10	pooling	pooling	NOUN
ajst-7806	127	11	and	and	CCONJ
ajst-7806	127	12	average	average	ADJ
ajst-7806	127	13	pooling	pooling	NOUN
ajst-7806	127	14	is	be	AUX
ajst-7806	127	15	spliced	splice	VERB
ajst-7806	127	16	in	in	ADP
ajst-7806	127	17	the	the	DET
ajst-7806	127	18	channel	channel	NOUN
ajst-7806	127	19	dimension	dimension	NOUN
ajst-7806	127	20	,	,	PUNCT
ajst-7806	127	21	and	and	CCONJ
ajst-7806	127	22	the	the	DET
ajst-7806	127	23	heat	heat	NOUN
ajst-7806	127	24	map	map	NOUN
ajst-7806	127	25	of	of	ADP
ajst-7806	127	26	spatial	spatial	ADJ
ajst-7806	127	27	importance	importance	NOUN
ajst-7806	127	28	is	be	AUX
ajst-7806	127	29	obtained	obtain	VERB
ajst-7806	127	30	through	through	ADP
ajst-7806	127	31	convolution	convolution	NOUN
ajst-7806	127	32	.	.	PUNCT
ajst-7806	128	1	finally	finally	ADV
ajst-7806	128	2	,	,	PUNCT
ajst-7806	128	3	the	the	DET
ajst-7806	128	4	real	real	ADJ
ajst-7806	128	5	heat	heat	NOUN
ajst-7806	128	6	map	map	NOUN
ajst-7806	128	7	is	be	AUX
ajst-7806	128	8	generated	generate	VERB
ajst-7806	128	9	through	through	ADP
ajst-7806	128	10	sigmoid	sigmoid	NOUN
ajst-7806	128	11	activation	activation	NOUN
ajst-7806	128	12	function	function	NOUN
ajst-7806	128	13	,	,	PUNCT
ajst-7806	128	14	and	and	CCONJ
ajst-7806	128	15	multiplied	multiply	VERB
ajst-7806	128	16	by	by	ADP
ajst-7806	128	17	the	the	DET
ajst-7806	128	18	original	original	ADJ
ajst-7806	128	19	input	input	NOUN
ajst-7806	128	20	to	to	PART
ajst-7806	128	21	obtain	obtain	VERB
ajst-7806	128	22	the	the	DET
ajst-7806	128	23	calibrated	calibrate	VERB
ajst-7806	128	24	feature	feature	NOUN
ajst-7806	128	25	map	map	NOUN
ajst-7806	128	26	ms(f	ms(f	NUM
ajst-7806	128	27	)	)	PUNCT
ajst-7806	128	28	,	,	PUNCT
ajst-7806	128	29	which	which	PRON
ajst-7806	128	30	encodes	encode	VERB
ajst-7806	128	31	the	the	DET
ajst-7806	128	32	position	position	NOUN
ajst-7806	128	33	that	that	PRON
ajst-7806	128	34	needs	need	VERB
ajst-7806	128	35	attention	attention	NOUN
ajst-7806	128	36	or	or	CCONJ
ajst-7806	128	37	suppression	suppression	NOUN
ajst-7806	128	38	.	.	PUNCT
ajst-7806	129	1	7	7	NUM
ajst-7806	129	2	7	7	NUM
ajst-7806	129	3	7	7	NUM
ajst-7806	129	4	7	7	NUM
ajst-7806	129	5	max	max	NOUN
ajst-7806	129	6	(	(	PUNCT
ajst-7806	129	7	)	)	PUNCT
ajst-7806	129	8	(	(	PUNCT
ajst-7806	129	9	(	(	PUNCT
ajst-7806	129	10	[	[	PUNCT
ajst-7806	129	11	(	(	PUNCT
ajst-7806	129	12	)	)	PUNCT
ajst-7806	129	13	;	;	PUNCT
ajst-7806	129	14	(	(	PUNCT
ajst-7806	129	15	)	)	PUNCT
ajst-7806	129	16	]	]	X
ajst-7806	129	17	)	)	PUNCT
ajst-7806	129	18	)	)	PUNCT
ajst-7806	129	19	(	(	PUNCT
ajst-7806	129	20	(	(	PUNCT
ajst-7806	129	21	[	[	PUNCT
ajst-7806	129	22	;	;	PUNCT
ajst-7806	129	23	]	]	PUNCT
ajst-7806	129	24	)	)	PUNCT
ajst-7806	129	25	)	)	PUNCT
ajst-7806	130	1	s	s	PART
ajst-7806	130	2	s	s	NOUN
ajst-7806	130	3	s	s	NOUN
ajst-7806	130	4	avg	avg	NOUN
ajst-7806	130	5	m	m	PROPN
ajst-7806	130	6	f	f	PROPN
ajst-7806	130	7	f	f	PROPN
ajst-7806	130	8	avgpool	avgpool	PROPN
ajst-7806	131	1	f	f	PROPN
ajst-7806	131	2	maxpool	maxpool	NOUN
ajst-7806	131	3	f	f	PROPN
ajst-7806	132	1	f	f	PROPN
ajst-7806	132	2	f	f	PROPN
ajst-7806	132	3	f	f	PROPN
ajst-7806	132	4			PROPN
ajst-7806	132	5			PROPN
ajst-7806	132	6			NOUN
ajst-7806	132	7			PROPN
ajst-7806	133	1			PROPN
ajst-7806	134	1			NOUN
ajst-7806	135	1	(	(	PUNCT
ajst-7806	135	2	5	5	NUM
ajst-7806	135	3	)	)	PUNCT
ajst-7806	135	4	as	as	SCONJ
ajst-7806	135	5	shown	show	VERB
ajst-7806	135	6	in	in	ADP
ajst-7806	135	7	eq.5	eq.5	PROPN
ajst-7806	135	8	,	,	PUNCT
ajst-7806	135	9	two	two	NUM
ajst-7806	135	10	feature	feature	NOUN
ajst-7806	135	11	maps	map	NOUN
ajst-7806	135	12	are	be	AUX
ajst-7806	135	13	obtained	obtain	VERB
ajst-7806	135	14	by	by	ADP
ajst-7806	135	15	two	two	NUM
ajst-7806	135	16	pooling	pool	VERB
ajst-7806	135	17	operations	operation	NOUN
ajst-7806	135	18	in	in	ADP
ajst-7806	135	19	the	the	DET
ajst-7806	135	20	spatial	spatial	ADJ
ajst-7806	135	21	dimension	dimension	NOUN
ajst-7806	135	22	,	,	PUNCT
ajst-7806	135	23	namely	namely	ADV
ajst-7806	135	24	,	,	PUNCT
ajst-7806	135	25	s	s	VERB
ajst-7806	135	26	avgf	avgf	NOUN
ajst-7806	135	27	and	and	CCONJ
ajst-7806	135	28	max	max	PROPN
ajst-7806	135	29	sf	sf	PROPN
ajst-7806	135	30	.	.	PUNCT
ajst-7806	136	1	these	these	DET
ajst-7806	136	2	two	two	NUM
ajst-7806	136	3	feature	feature	NOUN
ajst-7806	136	4	maps	map	NOUN
ajst-7806	136	5	are	be	AUX
ajst-7806	136	6	spliced	splice	VERB
ajst-7806	136	7	based	base	VERB
ajst-7806	136	8	on	on	ADP
ajst-7806	136	9	the	the	DET
ajst-7806	136	10	channel	channel	NOUN
ajst-7806	136	11	dimension	dimension	NOUN
ajst-7806	136	12	,	,	PUNCT
ajst-7806	136	13	and	and	CCONJ
ajst-7806	136	14	then	then	ADV
ajst-7806	136	15	the	the	DET
ajst-7806	136	16	channel	channel	NOUN
ajst-7806	136	17	dimension	dimension	NOUN
ajst-7806	136	18	is	be	AUX
ajst-7806	136	19	reduced	reduce	VERB
ajst-7806	136	20	by	by	ADP
ajst-7806	136	21	using	use	VERB
ajst-7806	136	22	a	a	DET
ajst-7806	136	23	7×7	7×7	NUM
ajst-7806	136	24	convolution	convolution	NOUN
ajst-7806	136	25	kernel	kernel	NOUN
ajst-7806	136	26	,	,	PUNCT
ajst-7806	136	27	7	7	NUM
ajst-7806	136	28	7f	7f	X
ajst-7806	136	29			NOUN
ajst-7806	136	30	represents	represent	VERB
ajst-7806	136	31	a	a	DET
ajst-7806	136	32	convolution	convolution	NOUN
ajst-7806	136	33	operation	operation	NOUN
ajst-7806	136	34	with	with	ADP
ajst-7806	136	35	a	a	DET
ajst-7806	136	36	filter	filter	NOUN
ajst-7806	136	37	size	size	NOUN
ajst-7806	136	38	of	of	ADP
ajst-7806	136	39	7×7	7×7	NUM
ajst-7806	136	40	,	,	PUNCT
ajst-7806	136	41	and	and	CCONJ
ajst-7806	136	42	the	the	DET
ajst-7806	136	43	dimension	dimension	NOUN
ajst-7806	136	44	is	be	AUX
ajst-7806	136	45	reduced	reduce	VERB
ajst-7806	136	46	to	to	ADP
ajst-7806	136	47	a	a	DET
ajst-7806	136	48	single	single	ADJ
ajst-7806	136	49	channel	channel	NOUN
ajst-7806	136	50	feature	feature	NOUN
ajst-7806	136	51	map	map	NOUN
ajst-7806	136	52	.	.	PUNCT
ajst-7806	137	1	finally	finally	ADV
ajst-7806	137	2	,	,	PUNCT
ajst-7806	137	3	the	the	DET
ajst-7806	137	4	weight	weight	NOUN
ajst-7806	137	5	of	of	ADP
ajst-7806	137	6	the	the	DET
ajst-7806	137	7	spatial	spatial	ADJ
ajst-7806	137	8	dimension	dimension	NOUN
ajst-7806	137	9	is	be	AUX
ajst-7806	137	10	generated	generate	VERB
ajst-7806	137	11	by	by	ADP
ajst-7806	137	12	learning	learn	VERB
ajst-7806	137	13	the	the	DET
ajst-7806	137	14	dependency	dependency	NOUN
ajst-7806	137	15	relationship	relationship	NOUN
ajst-7806	137	16	between	between	ADP
ajst-7806	137	17	spatial	spatial	ADJ
ajst-7806	137	18	elements	element	NOUN
ajst-7806	137	19	through	through	ADP
ajst-7806	137	20	sigmoid	sigmoid	NOUN
ajst-7806	137	21	.	.	PUNCT
ajst-7806	138	1	figure	figure	NOUN
ajst-7806	138	2	5	5	NUM
ajst-7806	138	3	.	.	PUNCT
ajst-7806	138	4	improved	improve	VERB
ajst-7806	138	5	cbam	cbam	NOUN
ajst-7806	138	6	attention	attention	NOUN
ajst-7806	138	7	module	module	NOUN
ajst-7806	138	8	as	as	SCONJ
ajst-7806	138	9	shown	show	VERB
ajst-7806	138	10	in	in	ADP
ajst-7806	138	11	fig	fig	NOUN
ajst-7806	138	12	.	.	PUNCT
ajst-7806	139	1	5	5	NUM
ajst-7806	139	2	,	,	PUNCT
ajst-7806	139	3	the	the	DET
ajst-7806	139	4	channel	channel	NOUN
ajst-7806	139	5	attention	attention	NOUN
ajst-7806	139	6	module	module	NOUN
ajst-7806	139	7	in	in	ADP
ajst-7806	139	8	cbam	cbam	NOUN
ajst-7806	139	9	is	be	AUX
ajst-7806	139	10	simply	simply	ADV
ajst-7806	139	11	improved	improve	VERB
ajst-7806	139	12	in	in	ADP
ajst-7806	139	13	this	this	DET
ajst-7806	139	14	section	section	NOUN
ajst-7806	139	15	,	,	PUNCT
ajst-7806	139	16	so	so	SCONJ
ajst-7806	139	17	that	that	SCONJ
ajst-7806	139	18	the	the	DET
ajst-7806	139	19	two	two	NUM
ajst-7806	139	20	groups	group	NOUN
ajst-7806	139	21	of	of	ADP
ajst-7806	139	22	features	feature	NOUN
ajst-7806	139	23	that	that	PRON
ajst-7806	139	24	have	have	AUX
ajst-7806	139	25	undergone	undergo	VERB
ajst-7806	139	26	maximum	maximum	ADJ
ajst-7806	139	27	pooling	pooling	NOUN
ajst-7806	139	28	and	and	CCONJ
ajst-7806	139	29	average	average	ADJ
ajst-7806	139	30	pooling	pooling	NOUN
ajst-7806	139	31	are	be	AUX
ajst-7806	139	32	concat	concat	NOUN
ajst-7806	139	33	spliced	splice	VERB
ajst-7806	139	34	,	,	PUNCT
ajst-7806	139	35	and	and	CCONJ
ajst-7806	139	36	then	then	ADV
ajst-7806	139	37	the	the	DET
ajst-7806	139	38	weights	weight	NOUN
ajst-7806	139	39	0w	0w	NOUN
ajst-7806	139	40	and	and	CCONJ
ajst-7806	139	41	1w	1w	NUM
ajst-7806	139	42	are	be	AUX
ajst-7806	139	43	trained	train	VERB
ajst-7806	139	44	by	by	ADP
ajst-7806	139	45	mlp	mlp	PROPN
ajst-7806	139	46	,	,	PUNCT
ajst-7806	139	47	and	and	CCONJ
ajst-7806	139	48	the	the	DET
ajst-7806	139	49	formula	formula	NOUN
ajst-7806	139	50	is	be	AUX
ajst-7806	139	51	shown	show	VERB
ajst-7806	139	52	in	in	ADP
ajst-7806	139	53	eq.6	eq.6	NOUN
ajst-7806	139	54	:	:	PUNCT
ajst-7806	139	55	1	1	NUM
ajst-7806	139	56	0	0	NUM
ajst-7806	139	57	max	max	NOUN
ajst-7806	139	58	(	(	PUNCT
ajst-7806	139	59	)	)	PUNCT
ajst-7806	139	60	(	(	PUNCT
ajst-7806	139	61	[	[	PUNCT
ajst-7806	139	62	(	(	PUNCT
ajst-7806	139	63	)	)	PUNCT
ajst-7806	139	64	;	;	PUNCT
ajst-7806	139	65	(	(	PUNCT
ajst-7806	139	66	)	)	PUNCT
ajst-7806	139	67	]	]	X
ajst-7806	139	68	)	)	PUNCT
ajst-7806	139	69	(	(	PUNCT
ajst-7806	139	70	(	(	PUNCT
ajst-7806	139	71	(	(	PUNCT
ajst-7806	139	72	[	[	PUNCT
ajst-7806	139	73	;	;	PUNCT
ajst-7806	139	74	]	]	PUNCT
ajst-7806	139	75	)	)	PUNCT
ajst-7806	139	76	)	)	PUNCT
ajst-7806	139	77	)	)	PUNCT
ajst-7806	140	1	c	c	NOUN
ajst-7806	141	1	c	c	X
ajst-7806	141	2	c	c	PROPN
ajst-7806	141	3	avg	avg	PROPN
ajst-7806	141	4	m	m	PROPN
ajst-7806	141	5	f	f	PROPN
ajst-7806	141	6	mlp	mlp	PROPN
ajst-7806	141	7	maxpool	maxpool	PROPN
ajst-7806	141	8	f	f	PROPN
ajst-7806	141	9	avgpool	avgpool	PROPN
ajst-7806	141	10	f	f	PROPN
ajst-7806	141	11	w	w	PROPN
ajst-7806	141	12	w	w	PROPN
ajst-7806	141	13	f	f	PROPN
ajst-7806	141	14	f	f	PROPN
ajst-7806	141	15			PROPN
ajst-7806	141	16			PROPN
ajst-7806	142	1			NUM
ajst-7806	142	2			NOUN
ajst-7806	142	3	(	(	PUNCT
ajst-7806	142	4	6	6	NUM
ajst-7806	142	5	)	)	PUNCT
ajst-7806	142	6	the	the	DET
ajst-7806	142	7	fused	fuse	VERB
ajst-7806	142	8	features	feature	NOUN
ajst-7806	142	9	after	after	ADP
ajst-7806	142	10	splicing	splicing	NOUN
ajst-7806	142	11	are	be	AUX
ajst-7806	142	12	sent	send	VERB
ajst-7806	142	13	to	to	ADP
ajst-7806	142	14	mlp	mlp	PROPN
ajst-7806	142	15	,	,	PUNCT
ajst-7806	142	16	which	which	PRON
ajst-7806	142	17	is	be	AUX
ajst-7806	142	18	composed	compose	VERB
ajst-7806	142	19	of	of	ADP
ajst-7806	142	20	two	two	NUM
ajst-7806	142	21	fully	fully	ADV
ajst-7806	142	22	connected	connected	ADJ
ajst-7806	142	23	layers	layer	NOUN
ajst-7806	142	24	.	.	PUNCT
ajst-7806	143	1	the	the	DET
ajst-7806	143	2	input	input	NOUN
ajst-7806	143	3	features	feature	VERB
ajst-7806	143	4	x	x	PUNCT
ajst-7806	143	5	of	of	ADP
ajst-7806	143	6	the	the	DET
ajst-7806	143	7	first	first	ADJ
ajst-7806	143	8	fully	fully	ADV
ajst-7806	143	9	connected	connect	VERB
ajst-7806	143	10	layer	layer	NOUN
ajst-7806	143	11	are	be	AUX
ajst-7806	143	12	reduced	reduce	VERB
ajst-7806	143	13	in	in	ADP
ajst-7806	143	14	dimension	dimension	NOUN
ajst-7806	143	15	to	to	PART
ajst-7806	143	16	obtain	obtain	VERB
ajst-7806	143	17	feature	feature	NOUN
ajst-7806	143	18	0y	0y	NOUN
ajst-7806	143	19	,	,	PUNCT
ajst-7806	143	20	and	and	CCONJ
ajst-7806	143	21	the	the	DET
ajst-7806	143	22	second	second	ADJ
ajst-7806	143	23	fully	fully	ADV
ajst-7806	143	24	connected	connect	VERB
ajst-7806	143	25	layer	layer	NOUN
ajst-7806	143	26	is	be	AUX
ajst-7806	143	27	upgraded	upgrade	VERB
ajst-7806	143	28	in	in	ADP
ajst-7806	143	29	dimension	dimension	NOUN
ajst-7806	143	30	to	to	PART
ajst-7806	143	31	obtain	obtain	VERB
ajst-7806	143	32	output	output	NOUN
ajst-7806	143	33	feature	feature	NOUN
ajst-7806	143	34	1y	1y	PROPN
ajst-7806	143	35	,	,	PUNCT
ajst-7806	143	36	as	as	SCONJ
ajst-7806	143	37	shown	show	VERB
ajst-7806	143	38	in	in	ADP
ajst-7806	143	39	formula	formula	NOUN
ajst-7806	143	40	eq.7	eq.7	PROPN
ajst-7806	143	41	and	and	CCONJ
ajst-7806	143	42	eq.8	eq.8	PROPN
ajst-7806	143	43	:	:	PUNCT
ajst-7806	144	1	0	0	NUM
ajst-7806	144	2	0	0	NUM
ajst-7806	145	1	y	y	PROPN
ajst-7806	145	2	w	w	PROPN
ajst-7806	145	3	x	x	PROPN
ajst-7806	145	4			PROPN
ajst-7806	145	5	(	(	PUNCT
ajst-7806	145	6	7	7	NUM
ajst-7806	145	7	)	)	SYM
ajst-7806	145	8	1	1	NUM
ajst-7806	145	9	1	1	NUM
ajst-7806	145	10	0	0	NUM
ajst-7806	145	11	y	y	PROPN
ajst-7806	145	12	w	w	PROPN
ajst-7806	145	13	y	y	PROPN
ajst-7806	145	14			NOUN
ajst-7806	145	15	(	(	PUNCT
ajst-7806	145	16	8)	8)	NUM
ajst-7806	145	17	it	it	PRON
ajst-7806	145	18	can	can	AUX
ajst-7806	145	19	be	be	AUX
ajst-7806	145	20	seen	see	VERB
ajst-7806	145	21	that	that	SCONJ
ajst-7806	145	22	the	the	DET
ajst-7806	145	23	weight	weight	NOUN
ajst-7806	145	24	parameters	parameter	NOUN
ajst-7806	145	25	of	of	ADP
ajst-7806	145	26	the	the	DET
ajst-7806	145	27	first	first	ADJ
ajst-7806	145	28	fully	fully	ADV
ajst-7806	145	29	connected	connect	VERB
ajst-7806	145	30	layer	layer	NOUN
ajst-7806	145	31	in	in	ADP
ajst-7806	145	32	the	the	DET
ajst-7806	145	33	improved	improved	ADJ
ajst-7806	145	34	attention	attention	NOUN
ajst-7806	145	35	module	module	NOUN
ajst-7806	145	36	mentioned	mention	VERB
ajst-7806	145	37	above	above	ADV
ajst-7806	145	38	have	have	AUX
ajst-7806	145	39	increased	increase	VERB
ajst-7806	145	40	,	,	PUNCT
ajst-7806	145	41	and	and	CCONJ
ajst-7806	145	42	the	the	DET
ajst-7806	145	43	model	model	NOUN
ajst-7806	145	44	performance	performance	NOUN
ajst-7806	145	45	has	have	AUX
ajst-7806	145	46	been	be	AUX
ajst-7806	145	47	relatively	relatively	ADV
ajst-7806	145	48	enhanced	enhance	VERB
ajst-7806	145	49	.	.	PUNCT
ajst-7806	146	1	this	this	DET
ajst-7806	146	2	improved	improve	VERB
ajst-7806	146	3	attention	attention	NOUN
ajst-7806	146	4	module	module	NOUN
ajst-7806	146	5	is	be	AUX
ajst-7806	146	6	embedded	embed	VERB
ajst-7806	146	7	into	into	ADP
ajst-7806	146	8	the	the	DET
ajst-7806	146	9	weak	weak	ADJ
ajst-7806	146	10	supervision	supervision	NOUN
ajst-7806	146	11	network	network	NOUN
ajst-7806	146	12	architecture	architecture	NOUN
ajst-7806	146	13	proposed	propose	VERB
ajst-7806	146	14	in	in	ADP
ajst-7806	146	15	this	this	DET
ajst-7806	146	16	chapter	chapter	NOUN
ajst-7806	146	17	to	to	PART
ajst-7806	146	18	achieve	achieve	VERB
ajst-7806	146	19	better	well	ADJ
ajst-7806	146	20	detection	detection	NOUN
ajst-7806	146	21	results	result	NOUN
ajst-7806	146	22	.	.	PUNCT
ajst-7806	147	1	3.3	3.3	NUM
ajst-7806	147	2	.	.	PUNCT
ajst-7806	148	1	basic	basic	ADJ
ajst-7806	148	2	multiple	multiple	ADJ
ajst-7806	148	3	instance	instance	NOUN
ajst-7806	148	4	detector	detector	NOUN
ajst-7806	148	5	this	this	DET
ajst-7806	148	6	paper	paper	NOUN
ajst-7806	148	7	mainly	mainly	ADV
ajst-7806	148	8	takes	take	VERB
ajst-7806	148	9	oicr	oicr	NOUN
ajst-7806	148	10	as	as	ADP
ajst-7806	148	11	the	the	DET
ajst-7806	148	12	main	main	ADJ
ajst-7806	148	13	framework	framework	NOUN
ajst-7806	148	14	,	,	PUNCT
ajst-7806	148	15	and	and	CCONJ
ajst-7806	148	16	oicr	oicr	NOUN
ajst-7806	148	17	is	be	AUX
ajst-7806	148	18	divided	divide	VERB
ajst-7806	148	19	into	into	ADP
ajst-7806	148	20	two	two	NUM
ajst-7806	148	21	parts	part	NOUN
ajst-7806	148	22	.	.	PUNCT
ajst-7806	149	1	the	the	DET
ajst-7806	149	2	first	first	ADJ
ajst-7806	149	3	part	part	NOUN
ajst-7806	149	4	is	be	AUX
ajst-7806	149	5	to	to	PART
ajst-7806	149	6	train	train	VERB
ajst-7806	149	7	the	the	DET
ajst-7806	149	8	midn	midn	PROPN
ajst-7806	149	9	of	of	ADP
ajst-7806	149	10	the	the	DET
ajst-7806	149	11	basic	basic	ADJ
ajst-7806	149	12	case	case	NOUN
ajst-7806	149	13	classifier	classifier	NOUN
ajst-7806	149	14	,	,	PUNCT
ajst-7806	149	15	which	which	PRON
ajst-7806	149	16	is	be	AUX
ajst-7806	149	17	transformed	transform	VERB
ajst-7806	149	18	from	from	ADP
ajst-7806	149	19	wsddn	wsddn	ADJ
ajst-7806	149	20	network	network	NOUN
ajst-7806	149	21	;	;	PUNCT
ajst-7806	149	22	the	the	DET
ajst-7806	149	23	second	second	ADJ
ajst-7806	149	24	part	part	NOUN
ajst-7806	149	25	is	be	AUX
ajst-7806	149	26	the	the	DET
ajst-7806	149	27	refinement	refinement	NOUN
ajst-7806	149	28	classifier	classifier	NOUN
ajst-7806	149	29	,	,	PUNCT
ajst-7806	149	30	and	and	CCONJ
ajst-7806	149	31	the	the	DET
ajst-7806	149	32	supervision	supervision	NOUN
ajst-7806	149	33	of	of	ADP
ajst-7806	149	34	the	the	DET
ajst-7806	149	35	refinement	refinement	NOUN
ajst-7806	149	36	classifier	classifier	NOUN
ajst-7806	149	37	is	be	AUX
ajst-7806	149	38	determined	determine	VERB
ajst-7806	149	39	by	by	ADP
ajst-7806	149	40	the	the	DET
ajst-7806	149	41	output	output	NOUN
ajst-7806	149	42	of	of	ADP
ajst-7806	149	43	the	the	DET
ajst-7806	149	44	previous	previous	ADJ
ajst-7806	149	45	stage	stage	NOUN
ajst-7806	149	46	.	.	PUNCT
ajst-7806	150	1	on	on	ADP
ajst-7806	150	2	this	this	DET
ajst-7806	150	3	basis	basis	NOUN
ajst-7806	150	4	,	,	PUNCT
ajst-7806	150	5	three	three	NUM
ajst-7806	150	6	modules	module	NOUN
ajst-7806	150	7	are	be	AUX
ajst-7806	150	8	introduced	introduce	VERB
ajst-7806	150	9	,	,	PUNCT
ajst-7806	150	10	namely	namely	ADV
ajst-7806	150	11	,	,	PUNCT
ajst-7806	150	12	proposal	proposal	NOUN
ajst-7806	150	13	generation	generation	NOUN
ajst-7806	150	14	and	and	CCONJ
ajst-7806	150	15	proposal	proposal	NOUN
ajst-7806	150	16	selection	selection	NOUN
ajst-7806	150	17	and	and	CCONJ
ajst-7806	150	18	attention	attention	NOUN
ajst-7806	150	19	module	module	NOUN
ajst-7806	150	20	.	.	PUNCT
ajst-7806	151	1	firstly	firstly	ADV
ajst-7806	151	2	,	,	PUNCT
ajst-7806	151	3	gradcam++	gradcam++	PROPN
ajst-7806	151	4	is	be	AUX
ajst-7806	151	5	combined	combine	VERB
ajst-7806	151	6	with	with	ADP
ajst-7806	151	7	ss	ss	PROPN
ajst-7806	151	8	algorithm	algorithm	NOUN
ajst-7806	151	9	to	to	PART
ajst-7806	151	10	generate	generate	VERB
ajst-7806	151	11	several	several	ADJ
ajst-7806	151	12	candidate	candidate	NOUN
ajst-7806	151	13	boxes	box	NOUN
ajst-7806	151	14	,	,	PUNCT
ajst-7806	151	15	and	and	CCONJ
ajst-7806	151	16	an	an	DET
ajst-7806	151	17	improved	improved	ADJ
ajst-7806	151	18	attention	attention	NOUN
ajst-7806	151	19	module	module	NOUN
ajst-7806	151	20	is	be	AUX
ajst-7806	151	21	added	add	VERB
ajst-7806	151	22	to	to	PART
ajst-7806	151	23	achieve	achieve	VERB
ajst-7806	151	24	better	well	ADJ
ajst-7806	151	25	results	result	NOUN
ajst-7806	151	26	.	.	PUNCT
ajst-7806	152	1	142	142	NUM
ajst-7806	152	2	figure	figure	NOUN
ajst-7806	152	3	6	6	NUM
ajst-7806	152	4	.	.	PUNCT
ajst-7806	152	5	basic	basic	ADJ
ajst-7806	152	6	multi	multi	ADJ
ajst-7806	152	7	-	-	ADJ
ajst-7806	152	8	instance	instance	ADJ
ajst-7806	152	9	learning	learn	VERB
ajst-7806	152	10	detector	detector	NOUN
ajst-7806	152	11	and	and	CCONJ
ajst-7806	152	12	refinement	refinement	NOUN
ajst-7806	152	13	branch	branch	NOUN
ajst-7806	152	14	instance	instance	NOUN
ajst-7806	152	15	detector	detector	NOUN
ajst-7806	152	16	because	because	SCONJ
ajst-7806	152	17	of	of	ADP
ajst-7806	152	18	weakly	weakly	ADJ
ajst-7806	152	19	supervised	supervised	ADJ
ajst-7806	152	20	learning	learning	NOUN
ajst-7806	152	21	,	,	PUNCT
ajst-7806	152	22	only	only	ADV
ajst-7806	152	23	image	image	NOUN
ajst-7806	152	24	-	-	PUNCT
ajst-7806	152	25	level	level	NOUN
ajst-7806	152	26	annotations	annotation	NOUN
ajst-7806	152	27	can	can	AUX
ajst-7806	152	28	be	be	AUX
ajst-7806	152	29	used	use	VERB
ajst-7806	152	30	,	,	PUNCT
ajst-7806	152	31	that	that	PRON
ajst-7806	152	32	is	be	AUX
ajst-7806	152	33	to	to	PART
ajst-7806	152	34	say	say	VERB
ajst-7806	152	35	,	,	PUNCT
ajst-7806	152	36	there	there	PRON
ajst-7806	152	37	is	be	VERB
ajst-7806	152	38	only	only	ADV
ajst-7806	152	39	classification	classification	NOUN
ajst-7806	152	40	information	information	NOUN
ajst-7806	152	41	but	but	CCONJ
ajst-7806	152	42	no	no	DET
ajst-7806	152	43	location	location	NOUN
ajst-7806	152	44	information	information	NOUN
ajst-7806	152	45	in	in	ADP
ajst-7806	152	46	the	the	DET
ajst-7806	152	47	data	datum	NOUN
ajst-7806	152	48	set	set	VERB
ajst-7806	152	49	.	.	PUNCT
ajst-7806	153	1	in	in	ADP
ajst-7806	153	2	order	order	NOUN
ajst-7806	153	3	to	to	PART
ajst-7806	153	4	better	well	ADV
ajst-7806	153	5	understand	understand	VERB
ajst-7806	153	6	the	the	DET
ajst-7806	153	7	semantic	semantic	ADJ
ajst-7806	153	8	information	information	NOUN
ajst-7806	153	9	inside	inside	ADP
ajst-7806	153	10	the	the	DET
ajst-7806	153	11	image	image	NOUN
ajst-7806	153	12	,	,	PUNCT
ajst-7806	153	13	it	it	PRON
ajst-7806	153	14	is	be	AUX
ajst-7806	153	15	necessary	necessary	ADJ
ajst-7806	153	16	to	to	PART
ajst-7806	153	17	examine	examine	VERB
ajst-7806	153	18	the	the	DET
ajst-7806	153	19	map	map	NOUN
ajst-7806	153	20	to	to	ADP
ajst-7806	153	21	the	the	DET
ajst-7806	153	22	regional	regional	ADJ
ajst-7806	153	23	level	level	NOUN
ajst-7806	153	24	and	and	CCONJ
ajst-7806	153	25	analyze	analyze	VERB
ajst-7806	153	26	the	the	DET
ajst-7806	153	27	characteristics	characteristic	NOUN
ajst-7806	153	28	of	of	ADP
ajst-7806	153	29	each	each	DET
ajst-7806	153	30	bounding	bounding	NOUN
ajst-7806	153	31	box	box	NOUN
ajst-7806	153	32	.	.	PUNCT
ajst-7806	154	1	firstly	firstly	ADV
ajst-7806	154	2	,	,	PUNCT
ajst-7806	154	3	a	a	DET
ajst-7806	154	4	basic	basic	ADJ
ajst-7806	154	5	detector	detector	NOUN
ajst-7806	154	6	is	be	AUX
ajst-7806	154	7	used	use	VERB
ajst-7806	154	8	to	to	PART
ajst-7806	154	9	obtain	obtain	VERB
ajst-7806	154	10	the	the	DET
ajst-7806	154	11	preliminary	preliminary	ADJ
ajst-7806	154	12	detection	detection	NOUN
ajst-7806	154	13	results	result	NOUN
ajst-7806	154	14	,	,	PUNCT
ajst-7806	154	15	and	and	CCONJ
ajst-7806	154	16	the	the	DET
ajst-7806	154	17	basic	basic	ADJ
ajst-7806	154	18	detector	detector	NOUN
ajst-7806	154	19	is	be	AUX
ajst-7806	154	20	optimized	optimize	VERB
ajst-7806	154	21	by	by	ADP
ajst-7806	154	22	transforming	transform	VERB
ajst-7806	154	23	the	the	DET
ajst-7806	154	24	weak	weak	ADJ
ajst-7806	154	25	supervised	supervised	ADJ
ajst-7806	154	26	object	object	NOUN
ajst-7806	154	27	detection	detection	NOUN
ajst-7806	154	28	problem	problem	NOUN
ajst-7806	154	29	into	into	ADP
ajst-7806	154	30	a	a	DET
ajst-7806	154	31	multi	multi	ADJ
ajst-7806	154	32	-	-	ADJ
ajst-7806	154	33	label	label	ADJ
ajst-7806	154	34	classification	classification	NOUN
ajst-7806	154	35	problem	problem	NOUN
ajst-7806	154	36	following	follow	VERB
ajst-7806	154	37	the	the	DET
ajst-7806	154	38	idea	idea	NOUN
ajst-7806	154	39	of	of	ADP
ajst-7806	154	40	wsddn	wsddn	NOUN
ajst-7806	154	41	using	use	VERB
ajst-7806	154	42	multi	multi	ADJ
ajst-7806	154	43	-	-	ADJ
ajst-7806	154	44	instance	instance	NOUN
ajst-7806	154	45	learning	learning	NOUN
ajst-7806	154	46	.	.	PUNCT
ajst-7806	155	1	the	the	DET
ajst-7806	155	2	proposed	propose	VERB
ajst-7806	155	3	score	score	NOUN
ajst-7806	155	4	obtained	obtain	VERB
ajst-7806	155	5	from	from	ADP
ajst-7806	155	6	the	the	DET
ajst-7806	155	7	basic	basic	ADJ
ajst-7806	155	8	detector	detector	NOUN
ajst-7806	155	9	can	can	AUX
ajst-7806	155	10	guide	guide	VERB
ajst-7806	155	11	the	the	DET
ajst-7806	155	12	first	first	ADJ
ajst-7806	155	13	level	level	NOUN
ajst-7806	155	14	of	of	ADP
ajst-7806	155	15	the	the	DET
ajst-7806	155	16	multi	multi	ADJ
ajst-7806	155	17	-	-	ADJ
ajst-7806	155	18	level	level	ADJ
ajst-7806	155	19	case	case	NOUN
ajst-7806	155	20	optimizer	optimizer	NOUN
ajst-7806	155	21	,	,	PUNCT
ajst-7806	155	22	and	and	CCONJ
ajst-7806	155	23	the	the	DET
ajst-7806	155	24	supervision	supervision	NOUN
ajst-7806	155	25	of	of	ADP
ajst-7806	155	26	the	the	DET
ajst-7806	155	27	case	case	NOUN
ajst-7806	155	28	optimizer	optimizer	NOUN
ajst-7806	155	29	is	be	AUX
ajst-7806	155	30	determined	determine	VERB
ajst-7806	155	31	by	by	ADP
ajst-7806	155	32	the	the	DET
ajst-7806	155	33	output	output	NOUN
ajst-7806	155	34	of	of	ADP
ajst-7806	155	35	its	its	PRON
ajst-7806	155	36	previous	previous	ADJ
ajst-7806	155	37	level	level	NOUN
ajst-7806	155	38	.	.	PUNCT
ajst-7806	156	1	multiple	multiple	ADJ
ajst-7806	156	2	refinements	refinement	NOUN
ajst-7806	156	3	at	at	ADP
ajst-7806	156	4	the	the	DET
ajst-7806	156	5	first	first	ADJ
ajst-7806	156	6	level	level	NOUN
ajst-7806	156	7	can	can	AUX
ajst-7806	156	8	gradually	gradually	ADV
ajst-7806	156	9	detect	detect	VERB
ajst-7806	156	10	a	a	DET
ajst-7806	156	11	larger	large	ADJ
ajst-7806	156	12	part	part	NOUN
ajst-7806	156	13	of	of	ADP
ajst-7806	156	14	the	the	DET
ajst-7806	156	15	target	target	NOUN
ajst-7806	156	16	,	,	PUNCT
ajst-7806	156	17	as	as	SCONJ
ajst-7806	156	18	shown	show	VERB
ajst-7806	156	19	in	in	ADP
ajst-7806	156	20	fig	fig	NOUN
ajst-7806	156	21	.	.	PUNCT
ajst-7806	157	1	6	6	NUM
ajst-7806	157	2	.	.	PUNCT
ajst-7806	157	3	the	the	DET
ajst-7806	157	4	regional	regional	ADJ
ajst-7806	157	5	features	feature	NOUN
ajst-7806	157	6	x	x	VERB
ajst-7806	157	7	are	be	AUX
ajst-7806	157	8	then	then	ADV
ajst-7806	157	9	fed	feed	VERB
ajst-7806	157	10	into	into	ADP
ajst-7806	157	11	the	the	DET
ajst-7806	157	12	two	two	NUM
ajst-7806	157	13	streams	stream	NOUN
ajst-7806	157	14	by	by	ADP
ajst-7806	157	15	two	two	NUM
ajst-7806	157	16	separate	separate	ADJ
ajst-7806	157	17	fully	fully	ADV
ajst-7806	157	18	connected	connect	VERB
ajst-7806	157	19	layers	layer	NOUN
ajst-7806	157	20	and	and	CCONJ
ajst-7806	157	21	produce	produce	VERB
ajst-7806	157	22	two	two	NUM
ajst-7806	157	23	feature	feature	NOUN
ajst-7806	157	24	matrices	matrix	NOUN
ajst-7806	157	25	denoted	denote	VERB
ajst-7806	157	26	as	as	ADP
ajst-7806	157	27	xcls	xcls	PROPN
ajst-7806	157	28	and	and	CCONJ
ajst-7806	157	29	det	det	PROPN
ajst-7806	158	1	|	|	ADV
ajst-7806	158	2	|x	|x	PROPN
ajst-7806	159	1	c	c	PROPN
ajst-7806	159	2	rr	rr	PROPN
ajst-7806	159	3			NOUN
ajst-7806	159	4	,	,	PUNCT
ajst-7806	159	5	where	where	SCONJ
ajst-7806	159	6	c	c	PROPN
ajst-7806	159	7	denotes	denote	VERB
ajst-7806	159	8	the	the	DET
ajst-7806	159	9	number	number	NOUN
ajst-7806	159	10	of	of	ADP
ajst-7806	159	11	categories	category	NOUN
ajst-7806	159	12	and	and	CCONJ
ajst-7806	159	13	r	r	NOUN
ajst-7806	159	14	denotes	denote	NOUN
ajst-7806	159	15	the	the	DET
ajst-7806	159	16	number	number	NOUN
ajst-7806	159	17	of	of	ADP
ajst-7806	159	18	proposals	proposal	NOUN
ajst-7806	159	19	.	.	PUNCT
ajst-7806	160	1	the	the	DET
ajst-7806	160	2	two	two	NUM
ajst-7806	160	3	softmax	softmax	NOUN
ajst-7806	160	4	functions	function	NOUN
ajst-7806	160	5	are	be	AUX
ajst-7806	160	6	applied	apply	VERB
ajst-7806	160	7	to	to	ADP
ajst-7806	160	8	xcls	xcls	PROPN
ajst-7806	160	9	and	and	CCONJ
ajst-7806	160	10	xcls	xcls	PROPN
ajst-7806	160	11	for	for	ADP
ajst-7806	160	12	two	two	NUM
ajst-7806	160	13	different	different	ADJ
ajst-7806	160	14	directions	direction	NOUN
ajst-7806	160	15	,	,	PUNCT
ajst-7806	160	16	as	as	SCONJ
ajst-7806	160	17	shown	show	VERB
ajst-7806	160	18	in	in	ADP
ajst-7806	160	19	the	the	DET
ajst-7806	160	20	following	follow	VERB
ajst-7806	160	21	eq.9	eq.9	PROPN
ajst-7806	160	22	and	and	CCONJ
ajst-7806	160	23	eq.10	eq.10	PROPN
ajst-7806	160	24	:	:	PUNCT
ajst-7806	160	25	1	1	NUM
ajst-7806	160	26	[	[	PUNCT
ajst-7806	160	27	(	(	PUNCT
ajst-7806	160	28	)	)	PUNCT
ajst-7806	160	29	]	]	PUNCT
ajst-7806	161	1	c	c	X
ajst-7806	162	1	ij	ij	INTJ
ajst-7806	162	2	c	c	NOUN
ajst-7806	162	3	kj	kj	PROPN
ajst-7806	162	4	x	x	PUNCT
ajst-7806	162	5	cls	cls	NOUN
ajst-7806	162	6	ij	ij	NOUN
ajst-7806	162	7	c	c	NOUN
ajst-7806	162	8	x	x	X
ajst-7806	162	9	k	k	NOUN
ajst-7806	162	10	e	e	NOUN
ajst-7806	162	11	x	x	X
ajst-7806	162	12	e	e	X
ajst-7806	162	13			PROPN
ajst-7806	162	14			NUM
ajst-7806	162	15			NOUN
ajst-7806	162	16			X
ajst-7806	162	17	(	(	PUNCT
ajst-7806	162	18	9	9	X
ajst-7806	162	19	)	)	PUNCT
ajst-7806	162	20	det	det	PROPN
ajst-7806	162	21	det	det	PROPN
ajst-7806	162	22	det	det	PROPN
ajst-7806	163	1	|	|	ADV
ajst-7806	163	2	|	|	ADV
ajst-7806	163	3	1	1	NUM
ajst-7806	163	4	[	[	PUNCT
ajst-7806	163	5	(	(	PUNCT
ajst-7806	163	6	)	)	PUNCT
ajst-7806	163	7	]	]	PUNCT
ajst-7806	164	1	ij	ij	X
ajst-7806	164	2	ik	ik	X
ajst-7806	164	3	x	x	SYM
ajst-7806	164	4	ij	ij	INTJ
ajst-7806	164	5	r	r	NOUN
ajst-7806	164	6	x	x	X
ajst-7806	164	7	k	k	NOUN
ajst-7806	164	8	e	e	NOUN
ajst-7806	164	9	x	x	X
ajst-7806	164	10	e	e	X
ajst-7806	164	11			PROPN
ajst-7806	164	12			NUM
ajst-7806	164	13			NOUN
ajst-7806	164	14			X
ajst-7806	164	15	(	(	PUNCT
ajst-7806	164	16	10	10	NUM
ajst-7806	164	17	)	)	PUNCT
ajst-7806	164	18	the	the	DET
ajst-7806	164	19	formula	formula	NOUN
ajst-7806	164	20	for	for	ADP
ajst-7806	164	21	generating	generate	VERB
ajst-7806	164	22	the	the	DET
ajst-7806	164	23	region	region	NOUN
ajst-7806	164	24	fraction	fraction	NOUN
ajst-7806	164	25	by	by	ADP
ajst-7806	164	26	multiplying	multiply	VERB
ajst-7806	164	27	the	the	DET
ajst-7806	164	28	elemental	elemental	ADJ
ajst-7806	164	29	aspects	aspect	NOUN
ajst-7806	164	30	is	be	AUX
ajst-7806	164	31	as	as	SCONJ
ajst-7806	164	32	follows	follow	VERB
ajst-7806	164	33	:	:	PUNCT
ajst-7806	164	34	(	(	PUNCT
ajst-7806	164	35	)	)	PUNCT
ajst-7806	164	36	(	(	PUNCT
ajst-7806	164	37	)	)	PUNCT
ajst-7806	164	38	r	r	NOUN
ajst-7806	164	39	cls	cls	NOUN
ajst-7806	164	40	dx	dx	PROPN
ajst-7806	164	41	x	x	PROPN
ajst-7806	164	42	x	x	PROPN
ajst-7806	164	43			PROPN
ajst-7806	164	44	�	�	PROPN
ajst-7806	164	45	(	(	PUNCT
ajst-7806	164	46	11	11	NUM
ajst-7806	164	47	)	)	PUNCT
ajst-7806	164	48	finally	finally	ADV
ajst-7806	164	49	,	,	PUNCT
ajst-7806	164	50	the	the	DET
ajst-7806	164	51	category	category	NOUN
ajst-7806	164	52	c	c	NOUN
ajst-7806	164	53	image	image	NOUN
ajst-7806	164	54	score	score	NOUN
ajst-7806	164	55	can	can	AUX
ajst-7806	164	56	be	be	AUX
ajst-7806	164	57	obtained	obtain	VERB
ajst-7806	164	58	by	by	ADP
ajst-7806	164	59	summing	sum	VERB
ajst-7806	164	60	all	all	DET
ajst-7806	164	61	the	the	DET
ajst-7806	164	62	proposed	propose	VERB
ajst-7806	164	63	scores	score	NOUN
ajst-7806	164	64	with	with	ADP
ajst-7806	164	65	the	the	DET
ajst-7806	164	66	following	follow	VERB
ajst-7806	164	67	equation	equation	NOUN
ajst-7806	164	68	:	:	PUNCT
ajst-7806	165	1	|	|	ADV
ajst-7806	165	2	|	|	ADV
ajst-7806	165	3	r	r	NOUN
ajst-7806	165	4	1	1	NUM
ajst-7806	165	5	r	r	NOUN
ajst-7806	165	6	r	r	NOUN
ajst-7806	165	7	c	c	NOUN
ajst-7806	165	8	crx	crx	VERB
ajst-7806	165	9			NOUN
ajst-7806	165	10			ADJ
ajst-7806	165	11			X
ajst-7806	165	12	(	(	PUNCT
ajst-7806	165	13	12	12	NUM
ajst-7806	165	14	)	)	PUNCT
ajst-7806	165	15	given	give	VERB
ajst-7806	165	16	an	an	DET
ajst-7806	165	17	image	image	NOUN
ajst-7806	165	18	label	label	NOUN
ajst-7806	165	19	1	1	NUM
ajst-7806	165	20	1	1	NUM
ajst-7806	165	21	2[y	2[y	NUM
ajst-7806	165	22	,	,	PUNCT
ajst-7806	165	23	y	y	PROPN
ajst-7806	165	24	,	,	PUNCT
ajst-7806	165	25	...	...	PUNCT
ajst-7806	165	26	,	,	PUNCT
ajst-7806	165	27	y	y	PROPN
ajst-7806	165	28	]	]	PUNCT
ajst-7806	165	29	t	t	X
ajst-7806	165	30	c	c	X
ajst-7806	165	31	cy	cy	PROPN
ajst-7806	165	32	r	r	PROPN
ajst-7806	165	33			NOUN
ajst-7806	165	34			NOUN
ajst-7806	165	35	,	,	PUNCT
ajst-7806	165	36	1cy	1cy	ADJ
ajst-7806	165	37			NOUN
ajst-7806	165	38	or	or	CCONJ
ajst-7806	165	39	0cy	0cy	NOUN
ajst-7806	165	40			NOUN
ajst-7806	165	41	indicates	indicate	VERB
ajst-7806	165	42	whether	whether	SCONJ
ajst-7806	165	43	the	the	DET
ajst-7806	165	44	image	image	NOUN
ajst-7806	165	45	has	have	AUX
ajst-7806	165	46	target	target	VERB
ajst-7806	165	47	c.	c.	NOUN
ajst-7806	165	48	in	in	ADP
ajst-7806	165	49	this	this	DET
ajst-7806	165	50	training	training	NOUN
ajst-7806	165	51	phase	phase	NOUN
ajst-7806	165	52	,	,	PUNCT
ajst-7806	165	53	we	we	PRON
ajst-7806	165	54	can	can	AUX
ajst-7806	165	55	perform	perform	VERB
ajst-7806	165	56	the	the	DET
ajst-7806	165	57	multi	multi	ADJ
ajst-7806	165	58	-	-	ADJ
ajst-7806	165	59	label	label	ADJ
ajst-7806	165	60	classification	classification	NOUN
ajst-7806	165	61	task	task	NOUN
ajst-7806	165	62	by	by	ADP
ajst-7806	165	63	the	the	DET
ajst-7806	165	64	standard	standard	ADJ
ajst-7806	165	65	multi	multi	ADJ
ajst-7806	165	66	-	-	ADJ
ajst-7806	165	67	category	category	ADJ
ajst-7806	165	68	crossentropy	crossentropy	NOUN
ajst-7806	165	69	loss	loss	NOUN
ajst-7806	165	70	function	function	NOUN
ajst-7806	165	71	like	like	ADP
ajst-7806	165	72	the	the	DET
ajst-7806	165	73	following	follow	VERB
ajst-7806	165	74	equation	equation	NOUN
ajst-7806	165	75	,	,	PUNCT
ajst-7806	165	76	and	and	CCONJ
ajst-7806	165	77	then	then	ADV
ajst-7806	165	78	the	the	DET
ajst-7806	165	79	instance	instance	NOUN
ajst-7806	165	80	classifier	classifier	NOUN
ajst-7806	165	81	can	can	AUX
ajst-7806	165	82	be	be	AUX
ajst-7806	165	83	obtained	obtain	VERB
ajst-7806	165	84	according	accord	VERB
ajst-7806	165	85	to	to	ADP
ajst-7806	165	86	the	the	DET
ajst-7806	165	87	proposed	propose	VERB
ajst-7806	165	88	score	score	NOUN
ajst-7806	165	89	rx	rx	VERB
ajst-7806	165	90	.	.	PUNCT
ajst-7806	166	1	in	in	ADP
ajst-7806	166	2	this	this	DET
ajst-7806	166	3	training	training	NOUN
ajst-7806	166	4	phase	phase	NOUN
ajst-7806	166	5	,	,	PUNCT
ajst-7806	166	6	the	the	DET
ajst-7806	166	7	loss	loss	NOUN
ajst-7806	166	8	function	function	NOUN
ajst-7806	166	9	can	can	AUX
ajst-7806	166	10	be	be	AUX
ajst-7806	166	11	formulated	formulate	VERB
ajst-7806	166	12	as	as	ADP
ajst-7806	166	13	eq.13	eq.13	NOUN
ajst-7806	166	14	:	:	PUNCT
ajst-7806	166	15	1	1	NUM
ajst-7806	166	16	{	{	PUNCT
ajst-7806	166	17	log	log	NOUN
ajst-7806	166	18	(	(	PUNCT
ajst-7806	166	19	1	1	NUM
ajst-7806	166	20	)	)	PUNCT
ajst-7806	166	21	log(1	log(1	NOUN
ajst-7806	166	22	)	)	PUNCT
ajst-7806	166	23	}	}	PUNCT
ajst-7806	166	24	c	c	NOUN
ajst-7806	166	25	base	base	NOUN
ajst-7806	166	26	c	c	NOUN
ajst-7806	166	27	c	c	NOUN
ajst-7806	166	28	c	c	NOUN
ajst-7806	166	29	c	c	NOUN
ajst-7806	166	30	c	c	NOUN
ajst-7806	166	31	l	l	NOUN
ajst-7806	166	32	y	y	NOUN
ajst-7806	166	33	y	y	NUM
ajst-7806	167	1			ADJ
ajst-7806	167	2			PROPN
ajst-7806	167	3			PROPN
ajst-7806	167	4			PROPN
ajst-7806	167	5			VERB
ajst-7806	167	6			PROPN
ajst-7806	167	7			PROPN
ajst-7806	167	8	(	(	PUNCT
ajst-7806	167	9	13	13	NUM
ajst-7806	167	10	)	)	PUNCT
ajst-7806	167	11	3.4	3.4	NUM
ajst-7806	167	12	.	.	PUNCT
ajst-7806	168	1	proposal	proposal	NOUN
ajst-7806	168	2	selection	selection	NOUN
ajst-7806	168	3	after	after	SCONJ
ajst-7806	168	4	all	all	DET
ajst-7806	168	5	the	the	DET
ajst-7806	168	6	regional	regional	ADJ
ajst-7806	168	7	proposal	proposal	NOUN
ajst-7806	168	8	boxes	box	NOUN
ajst-7806	168	9	and	and	CCONJ
ajst-7806	168	10	scores	score	NOUN
ajst-7806	168	11	are	be	AUX
ajst-7806	168	12	obtained	obtain	VERB
ajst-7806	168	13	through	through	ADP
ajst-7806	168	14	the	the	DET
ajst-7806	168	15	above	above	ADJ
ajst-7806	168	16	modules	module	NOUN
ajst-7806	168	17	,	,	PUNCT
ajst-7806	168	18	how	how	SCONJ
ajst-7806	168	19	to	to	PART
ajst-7806	168	20	adaptively	adaptively	ADV
ajst-7806	168	21	select	select	VERB
ajst-7806	168	22	high	high	ADJ
ajst-7806	168	23	-	-	PUNCT
ajst-7806	168	24	quality	quality	NOUN
ajst-7806	168	25	proposals	proposal	NOUN
ajst-7806	168	26	becomes	become	VERB
ajst-7806	168	27	the	the	DET
ajst-7806	168	28	key	key	NOUN
ajst-7806	168	29	.	.	PUNCT
ajst-7806	169	1	due	due	ADP
ajst-7806	169	2	to	to	ADP
ajst-7806	169	3	the	the	DET
ajst-7806	169	4	lack	lack	NOUN
ajst-7806	169	5	of	of	ADP
ajst-7806	169	6	accurate	accurate	ADJ
ajst-7806	169	7	location	location	NOUN
ajst-7806	169	8	labels	label	NOUN
ajst-7806	169	9	in	in	ADP
ajst-7806	169	10	the	the	DET
ajst-7806	169	11	data	datum	NOUN
ajst-7806	169	12	,	,	PUNCT
ajst-7806	169	13	it	it	PRON
ajst-7806	169	14	is	be	AUX
ajst-7806	169	15	difficult	difficult	ADJ
ajst-7806	169	16	for	for	SCONJ
ajst-7806	169	17	the	the	DET
ajst-7806	169	18	weakly	weakly	ADJ
ajst-7806	169	19	supervised	supervised	ADJ
ajst-7806	169	20	object	object	NOUN
ajst-7806	169	21	detector	detector	NOUN
ajst-7806	169	22	to	to	PART
ajst-7806	169	23	select	select	VERB
ajst-7806	169	24	the	the	DET
ajst-7806	169	25	most	most	ADV
ajst-7806	169	26	suitable	suitable	ADJ
ajst-7806	169	27	bounding	bounding	NOUN
ajst-7806	169	28	box	box	NOUN
ajst-7806	169	29	from	from	ADP
ajst-7806	169	30	all	all	DET
ajst-7806	169	31	the	the	DET
ajst-7806	169	32	proposals	proposal	NOUN
ajst-7806	169	33	of	of	ADP
ajst-7806	169	34	the	the	DET
ajst-7806	169	35	object	object	NOUN
ajst-7806	169	36	.	.	PUNCT
ajst-7806	170	1	suggestions	suggestion	NOUN
ajst-7806	170	2	that	that	PRON
ajst-7806	170	3	get	get	VERB
ajst-7806	170	4	the	the	DET
ajst-7806	170	5	highest	high	ADJ
ajst-7806	170	6	classification	classification	NOUN
ajst-7806	170	7	score	score	NOUN
ajst-7806	170	8	usually	usually	ADV
ajst-7806	170	9	cover	cover	VERB
ajst-7806	170	10	the	the	DET
ajst-7806	170	11	different	different	ADJ
ajst-7806	170	12	parts	part	NOUN
ajst-7806	170	13	of	of	ADP
ajst-7806	170	14	the	the	DET
ajst-7806	170	15	object	object	NOUN
ajst-7806	170	16	,	,	PUNCT
ajst-7806	170	17	while	while	SCONJ
ajst-7806	170	18	many	many	ADJ
ajst-7806	170	19	other	other	ADJ
ajst-7806	170	20	suggestions	suggestion	NOUN
ajst-7806	170	21	that	that	PRON
ajst-7806	170	22	cover	cover	VERB
ajst-7806	170	23	a	a	DET
ajst-7806	170	24	larger	large	ADJ
ajst-7806	170	25	part	part	NOUN
ajst-7806	170	26	often	often	ADV
ajst-7806	170	27	have	have	VERB
ajst-7806	170	28	lower	low	ADJ
ajst-7806	170	29	scores	score	NOUN
ajst-7806	170	30	.	.	PUNCT
ajst-7806	171	1	inspired	inspire	VERB
ajst-7806	171	2	by	by	ADP
ajst-7806	171	3	wsod2	wsod2	NOUN
ajst-7806	171	4	,	,	PUNCT
ajst-7806	171	5	a	a	DET
ajst-7806	171	6	simple	simple	ADJ
ajst-7806	171	7	strategy	strategy	NOUN
ajst-7806	171	8	is	be	AUX
ajst-7806	171	9	used	use	VERB
ajst-7806	171	10	to	to	PART
ajst-7806	171	11	combine	combine	VERB
ajst-7806	171	12	low	low	ADJ
ajst-7806	171	13	-	-	PUNCT
ajst-7806	171	14	level	level	NOUN
ajst-7806	171	15	semantic	semantic	ADJ
ajst-7806	171	16	information	information	NOUN
ajst-7806	171	17	to	to	PART
ajst-7806	171	18	train	train	VERB
ajst-7806	171	19	the	the	DET
ajst-7806	171	20	weakly	weakly	ADV
ajst-7806	171	21	supervised	supervised	ADJ
ajst-7806	171	22	object	object	NOUN
ajst-7806	171	23	detector	detector	NOUN
ajst-7806	171	24	.	.	PUNCT
ajst-7806	172	1	low	low	ADJ
ajst-7806	172	2	-	-	PUNCT
ajst-7806	172	3	level	level	NOUN
ajst-7806	172	4	semantic	semantic	ADJ
ajst-7806	172	5	information	information	NOUN
ajst-7806	172	6	summarizes	summarize	VERB
ajst-7806	172	7	the	the	DET
ajst-7806	172	8	boundary	boundary	ADJ
ajst-7806	172	9	characteristics	characteristic	NOUN
ajst-7806	172	10	of	of	ADP
ajst-7806	172	11	common	common	ADJ
ajst-7806	172	12	objects	object	NOUN
ajst-7806	172	13	,	,	PUNCT
ajst-7806	172	14	which	which	PRON
ajst-7806	172	15	is	be	AUX
ajst-7806	172	16	helpful	helpful	ADJ
ajst-7806	172	17	to	to	PART
ajst-7806	172	18	make	make	VERB
ajst-7806	172	19	up	up	ADP
ajst-7806	172	20	for	for	ADP
ajst-7806	172	21	the	the	DET
ajst-7806	172	22	shortage	shortage	NOUN
ajst-7806	172	23	of	of	ADP
ajst-7806	172	24	cnn	cnn	PROPN
ajst-7806	172	25	in	in	ADP
ajst-7806	172	26	boundary	boundary	ADJ
ajst-7806	172	27	discovery	discovery	NOUN
ajst-7806	172	28	.	.	PUNCT
ajst-7806	173	1	in	in	ADP
ajst-7806	173	2	oicr	oicr	NOUN
ajst-7806	173	3	method	method	NOUN
ajst-7806	173	4	,	,	PUNCT
ajst-7806	173	5	given	give	VERB
ajst-7806	173	6	an	an	DET
ajst-7806	173	7	image	image	NOUN
ajst-7806	173	8	,	,	PUNCT
ajst-7806	173	9	which	which	PRON
ajst-7806	173	10	contains	contain	VERB
ajst-7806	173	11	the	the	DET
ajst-7806	173	12	category	category	NOUN
ajst-7806	173	13	of	of	ADP
ajst-7806	173	14	the	the	DET
ajst-7806	173	15	target	target	NOUN
ajst-7806	173	16	object	object	NOUN
ajst-7806	173	17	,	,	PUNCT
ajst-7806	173	18	it	it	PRON
ajst-7806	173	19	selects	select	VERB
ajst-7806	173	20	only	only	ADV
ajst-7806	173	21	the	the	DET
ajst-7806	173	22	candidate	candidate	NOUN
ajst-7806	173	23	box	box	NOUN
ajst-7806	173	24	with	with	ADP
ajst-7806	173	25	the	the	DET
ajst-7806	173	26	highest	high	ADJ
ajst-7806	173	27	category	category	NOUN
ajst-7806	173	28	score	score	NOUN
ajst-7806	173	29	and	and	CCONJ
ajst-7806	173	30	the	the	DET
ajst-7806	173	31	candidate	candidate	NOUN
ajst-7806	173	32	box	box	NOUN
ajst-7806	173	33	with	with	ADP
ajst-7806	173	34	spatial	spatial	ADJ
ajst-7806	173	35	overlap	overlap	NOUN
ajst-7806	173	36	,	,	PUNCT
ajst-7806	173	37	and	and	CCONJ
ajst-7806	173	38	the	the	DET
ajst-7806	173	39	rest	rest	NOUN
ajst-7806	173	40	are	be	AUX
ajst-7806	173	41	all	all	PRON
ajst-7806	173	42	negative	negative	ADJ
ajst-7806	173	43	examples	example	NOUN
ajst-7806	173	44	.	.	PUNCT
ajst-7806	174	1	however	however	ADV
ajst-7806	174	2	,	,	PUNCT
ajst-7806	174	3	if	if	SCONJ
ajst-7806	174	4	the	the	DET
ajst-7806	174	5	image	image	NOUN
ajst-7806	174	6	contains	contain	VERB
ajst-7806	174	7	multiple	multiple	ADJ
ajst-7806	174	8	target	target	NOUN
ajst-7806	174	9	objects	object	NOUN
ajst-7806	174	10	of	of	ADP
ajst-7806	174	11	the	the	DET
ajst-7806	174	12	same	same	ADJ
ajst-7806	174	13	category	category	NOUN
ajst-7806	174	14	,	,	PUNCT
ajst-7806	174	15	it	it	PRON
ajst-7806	174	16	is	be	AUX
ajst-7806	174	17	impossible	impossible	ADJ
ajst-7806	174	18	to	to	PART
ajst-7806	174	19	distinguish	distinguish	VERB
ajst-7806	174	20	the	the	DET
ajst-7806	174	21	positive	positive	ADJ
ajst-7806	174	22	and	and	CCONJ
ajst-7806	174	23	negative	negative	ADJ
ajst-7806	174	24	examples	example	NOUN
ajst-7806	174	25	well	well	ADV
ajst-7806	174	26	,	,	PUNCT
ajst-7806	174	27	which	which	PRON
ajst-7806	174	28	will	will	AUX
ajst-7806	174	29	lead	lead	VERB
ajst-7806	174	30	to	to	ADP
ajst-7806	174	31	the	the	DET
ajst-7806	174	32	omission	omission	NOUN
ajst-7806	174	33	of	of	ADP
ajst-7806	174	34	some	some	DET
ajst-7806	174	35	actual	actual	ADJ
ajst-7806	174	36	and	and	CCONJ
ajst-7806	174	37	valuable	valuable	ADJ
ajst-7806	174	38	positive	positive	ADJ
ajst-7806	174	39	suggestion	suggestion	NOUN
ajst-7806	174	40	candidate	candidate	NOUN
ajst-7806	174	41	boxes	box	NOUN
ajst-7806	174	42	and	and	CCONJ
ajst-7806	174	43	the	the	DET
ajst-7806	174	44	introduction	introduction	NOUN
ajst-7806	174	45	of	of	ADP
ajst-7806	174	46	some	some	DET
ajst-7806	174	47	inaccurate	inaccurate	ADJ
ajst-7806	174	48	negative	negative	ADJ
ajst-7806	174	49	suggestion	suggestion	NOUN
ajst-7806	174	50	143	143	NUM
ajst-7806	174	51	boxes	box	NOUN
ajst-7806	174	52	.	.	PUNCT
ajst-7806	175	1	in	in	ADP
ajst-7806	175	2	this	this	DET
ajst-7806	175	3	paper	paper	NOUN
ajst-7806	175	4	,	,	PUNCT
ajst-7806	175	5	a	a	DET
ajst-7806	175	6	simple	simple	ADJ
ajst-7806	175	7	but	but	CCONJ
ajst-7806	175	8	very	very	ADV
ajst-7806	175	9	effective	effective	ADJ
ajst-7806	175	10	suggestion	suggestion	NOUN
ajst-7806	175	11	selection	selection	NOUN
ajst-7806	175	12	strategy	strategy	NOUN
ajst-7806	175	13	is	be	AUX
ajst-7806	175	14	proposed	propose	VERB
ajst-7806	175	15	,	,	PUNCT
ajst-7806	175	16	which	which	PRON
ajst-7806	175	17	combines	combine	VERB
ajst-7806	175	18	low	low	ADJ
ajst-7806	175	19	-	-	PUNCT
ajst-7806	175	20	level	level	NOUN
ajst-7806	175	21	semantic	semantic	ADJ
ajst-7806	175	22	information	information	NOUN
ajst-7806	175	23	to	to	PART
ajst-7806	175	24	screen	screen	VERB
ajst-7806	175	25	high	high	ADJ
ajst-7806	175	26	-	-	PUNCT
ajst-7806	175	27	quality	quality	NOUN
ajst-7806	175	28	suggestions	suggestion	NOUN
ajst-7806	175	29	.	.	PUNCT
ajst-7806	176	1	firstly	firstly	ADV
ajst-7806	176	2	,	,	PUNCT
ajst-7806	176	3	a	a	DET
ajst-7806	176	4	simple	simple	ADJ
ajst-7806	176	5	strategy	strategy	NOUN
ajst-7806	176	6	is	be	AUX
ajst-7806	176	7	used	use	VERB
ajst-7806	176	8	to	to	PART
ajst-7806	176	9	select	select	VERB
ajst-7806	176	10	the	the	DET
ajst-7806	176	11	proposal	proposal	NOUN
ajst-7806	176	12	candidate	candidate	NOUN
ajst-7806	176	13	box	box	PROPN
ajst-7806	176	14	with	with	ADP
ajst-7806	176	15	high	high	ADJ
ajst-7806	176	16	score	score	NOUN
ajst-7806	176	17	,	,	PUNCT
ajst-7806	176	18	and	and	CCONJ
ajst-7806	176	19	then	then	ADV
ajst-7806	176	20	the	the	DET
ajst-7806	176	21	low	low	ADJ
ajst-7806	176	22	-	-	PUNCT
ajst-7806	176	23	level	level	NOUN
ajst-7806	176	24	semantic	semantic	ADJ
ajst-7806	176	25	information	information	NOUN
ajst-7806	176	26	is	be	AUX
ajst-7806	176	27	used	use	VERB
ajst-7806	176	28	to	to	PART
ajst-7806	176	29	screen	screen	VERB
ajst-7806	176	30	and	and	CCONJ
ajst-7806	176	31	adjust	adjust	VERB
ajst-7806	176	32	the	the	DET
ajst-7806	176	33	candidate	candidate	NOUN
ajst-7806	176	34	box	box	NOUN
ajst-7806	176	35	.	.	PUNCT
ajst-7806	177	1	specifically	specifically	ADV
ajst-7806	177	2	,	,	PUNCT
ajst-7806	177	3	an	an	DET
ajst-7806	177	4	image	image	NOUN
ajst-7806	177	5	is	be	AUX
ajst-7806	177	6	input	input	NOUN
ajst-7806	177	7	,	,	PUNCT
ajst-7806	177	8	and	and	CCONJ
ajst-7806	177	9	a	a	DET
ajst-7806	177	10	group	group	NOUN
ajst-7806	177	11	of	of	ADP
ajst-7806	177	12	proposals	proposal	NOUN
ajst-7806	177	13	1	1	NUM
ajst-7806	177	14	|	|	ADV
ajst-7806	177	15	|	|	ADV
ajst-7806	177	16	{	{	PUNCT
ajst-7806	177	17	,	,	PUNCT
ajst-7806	177	18	...	...	PUNCT
ajst-7806	177	19	,	,	PUNCT
ajst-7806	177	20	}	}	PUNCT
ajst-7806	177	21	rr	rr	NOUN
ajst-7806	177	22	r	r	NOUN
ajst-7806	177	23	r	r	NOUN
ajst-7806	177	24	and	and	CCONJ
ajst-7806	177	25	corresponding	correspond	VERB
ajst-7806	177	26	proposal	proposal	NOUN
ajst-7806	177	27	scores	score	NOUN
ajst-7806	177	28	rx	rx	VERB
ajst-7806	177	29	are	be	AUX
ajst-7806	177	30	generated	generate	VERB
ajst-7806	177	31	through	through	ADP
ajst-7806	177	32	the	the	DET
ajst-7806	177	33	above	above	ADV
ajst-7806	177	34	-	-	PUNCT
ajst-7806	177	35	mentioned	mention	VERB
ajst-7806	177	36	module	module	NOUN
ajst-7806	177	37	.	.	PUNCT
ajst-7806	178	1	each	each	DET
ajst-7806	178	2	proposal	proposal	NOUN
ajst-7806	178	3	in	in	ADP
ajst-7806	178	4	category	category	NOUN
ajst-7806	178	5	c	c	PROPN
ajst-7806	178	6	has	have	VERB
ajst-7806	178	7	an	an	DET
ajst-7806	178	8	objetness	objetness	ADJ
ajst-7806	178	9	score	score	NOUN
ajst-7806	179	1	[	[	X
ajst-7806	179	2	42	42	NUM
ajst-7806	179	3	]	]	PUNCT
ajst-7806	179	4	,	,	PUNCT
ajst-7806	179	5	marked	mark	VERB
ajst-7806	179	6	as	as	ADP
ajst-7806	179	7	(	(	PUNCT
ajst-7806	179	8	)	)	PUNCT
ajst-7806	179	9	buo	buo	NOUN
ajst-7806	179	10	r	r	NOUN
ajst-7806	179	11	,	,	PUNCT
ajst-7806	179	12	the	the	DET
ajst-7806	179	13	selection	selection	NOUN
ajst-7806	179	14	of	of	ADP
ajst-7806	179	15	each	each	DET
ajst-7806	179	16	proposal	proposal	NOUN
ajst-7806	179	17	is	be	AUX
ajst-7806	179	18	as	as	SCONJ
ajst-7806	179	19	follows	follow	VERB
ajst-7806	179	20	:	:	PUNCT
ajst-7806	179	21	1	1	X
ajst-7806	179	22	.	.	X
ajst-7806	180	1	if	if	SCONJ
ajst-7806	180	2	,	,	PUNCT
ajst-7806	180	3	it	it	PRON
ajst-7806	180	4	means	mean	VERB
ajst-7806	180	5	that	that	SCONJ
ajst-7806	180	6	the	the	DET
ajst-7806	180	7	image	image	NOUN
ajst-7806	180	8	contains	contain	VERB
ajst-7806	180	9	at	at	ADV
ajst-7806	180	10	least	least	ADV
ajst-7806	180	11	one	one	NUM
ajst-7806	180	12	object	object	NOUN
ajst-7806	180	13	whose	whose	DET
ajst-7806	180	14	target	target	NOUN
ajst-7806	180	15	is	be	AUX
ajst-7806	180	16	class	class	NOUN
ajst-7806	180	17	c	c	NOUN
ajst-7806	180	18	,	,	PUNCT
ajst-7806	180	19	then	then	ADV
ajst-7806	180	20	the	the	DET
ajst-7806	180	21	proposal	proposal	NOUN
ajst-7806	180	22	cj	cj	VERB
ajst-7806	180	23	with	with	ADP
ajst-7806	180	24	the	the	DET
ajst-7806	180	25	highest	high	ADJ
ajst-7806	180	26	score	score	NOUN
ajst-7806	180	27	is	be	AUX
ajst-7806	180	28	selected	select	VERB
ajst-7806	180	29	according	accord	VERB
ajst-7806	180	30	to	to	ADP
ajst-7806	180	31	the	the	DET
ajst-7806	180	32	following	follow	VERB
ajst-7806	180	33	formula	formula	NOUN
ajst-7806	180	34	,	,	PUNCT
ajst-7806	180	35	and	and	CCONJ
ajst-7806	180	36	it	it	PRON
ajst-7806	180	37	is	be	AUX
ajst-7806	180	38	marked	mark	VERB
ajst-7806	180	39	as	as	ADP
ajst-7806	180	40	a	a	DET
ajst-7806	180	41	pseudo	pseudo	NOUN
ajst-7806	180	42	-	-	ADJ
ajst-7806	180	43	label	label	NOUN
ajst-7806	180	44	class	class	NOUN
ajst-7806	180	45	c	c	NOUN
ajst-7806	180	46	,	,	PUNCT
ajst-7806	180	47	1	1	NUM
ajst-7806	180	48	ccj	ccj	NOUN
ajst-7806	180	49	y	y	NOUN
ajst-7806	180	50			ADV
ajst-7806	180	51	,	,	PUNCT
ajst-7806	180	52	as	as	SCONJ
ajst-7806	180	53	shown	show	VERB
ajst-7806	180	54	in	in	ADP
ajst-7806	180	55	the	the	DET
ajst-7806	180	56	following	follow	VERB
ajst-7806	180	57	formula	formula	NOUN
ajst-7806	180	58	:	:	PUNCT
ajst-7806	181	1	arg	arg	NOUN
ajst-7806	181	2	max	max	PROPN
ajst-7806	181	3	c	c	PROPN
ajst-7806	181	4	cr	cr	PROPN
ajst-7806	181	5	r	r	PROPN
ajst-7806	181	6	j	j	PROPN
ajst-7806	181	7	x	x	PROPN
ajst-7806	181	8	(	(	PUNCT
ajst-7806	181	9	14	14	NUM
ajst-7806	181	10	)	)	PUNCT
ajst-7806	181	11	2	2	NUM
ajst-7806	181	12	.	.	PUNCT
ajst-7806	182	1	if	if	SCONJ
ajst-7806	182	2	the	the	DET
ajst-7806	182	3	iou	iou	NOUN
ajst-7806	182	4	between	between	ADP
ajst-7806	182	5	the	the	DET
ajst-7806	182	6	proposal	proposal	NOUN
ajst-7806	182	7	box	box	NOUN
ajst-7806	182	8	and	and	CCONJ
ajst-7806	182	9	is	be	AUX
ajst-7806	182	10	higher	high	ADJ
ajst-7806	182	11	than	than	ADP
ajst-7806	182	12	the	the	DET
ajst-7806	182	13	value	value	NOUN
ajst-7806	182	14	we	we	PRON
ajst-7806	182	15	defined	define	VERB
ajst-7806	182	16	(	(	PUNCT
ajst-7806	182	17	iou=0.5	iou=0.5	NOUN
ajst-7806	182	18	)	)	PUNCT
ajst-7806	182	19	and	and	CCONJ
ajst-7806	182	20	it	it	PRON
ajst-7806	182	21	’s	’	VERB
ajst-7806	182	22	(	(	PUNCT
ajst-7806	182	23	)	)	PUNCT
ajst-7806	182	24	buo	buo	NOUN
ajst-7806	182	25	r	r	NOUN
ajst-7806	182	26	has	have	VERB
ajst-7806	182	27	the	the	DET
ajst-7806	182	28	highest	high	ADJ
ajst-7806	182	29	score	score	NOUN
ajst-7806	182	30	,	,	PUNCT
ajst-7806	182	31	this	this	DET
ajst-7806	182	32	paper	paper	NOUN
ajst-7806	182	33	marks	mark	VERB
ajst-7806	182	34	the	the	DET
ajst-7806	182	35	proposal	proposal	NOUN
ajst-7806	182	36	box	box	NOUN
ajst-7806	182	37	as	as	ADP
ajst-7806	182	38	category	category	NOUN
ajst-7806	182	39	c.	c.	NOUN
ajst-7806	182	40	3	3	NUM
ajst-7806	182	41	.	.	PUNCT
ajst-7806	183	1	this	this	DET
ajst-7806	183	2	paper	paper	NOUN
ajst-7806	183	3	continues	continue	VERB
ajst-7806	183	4	to	to	PART
ajst-7806	183	5	select	select	VERB
ajst-7806	183	6	the	the	DET
ajst-7806	183	7	highest	high	ADJ
ajst-7806	183	8	scoring	scoring	NOUN
ajst-7806	183	9	proposal	proposal	NOUN
ajst-7806	183	10	boxes	box	NOUN
ajst-7806	183	11	in	in	ADP
ajst-7806	183	12	addition	addition	NOUN
ajst-7806	183	13	to	to	ADP
ajst-7806	183	14	the	the	DET
ajst-7806	183	15	previously	previously	ADV
ajst-7806	183	16	selected	select	VERB
ajst-7806	183	17	ones	one	NOUN
ajst-7806	183	18	as	as	SCONJ
ajst-7806	183	19	described	describe	VERB
ajst-7806	183	20	above	above	ADV
ajst-7806	183	21	.	.	PUNCT
ajst-7806	184	1	4	4	X
ajst-7806	184	2	.	.	X
ajst-7806	184	3	repeat	repeat	VERB
ajst-7806	184	4	this	this	DET
ajst-7806	184	5	step	step	NOUN
ajst-7806	184	6	until	until	SCONJ
ajst-7806	184	7	the	the	DET
ajst-7806	184	8	iou	iou	NOUN
ajst-7806	184	9	of	of	ADP
ajst-7806	184	10	a	a	DET
ajst-7806	184	11	proposal	proposal	NOUN
ajst-7806	184	12	box	box	NOUN
ajst-7806	184	13	with	with	ADP
ajst-7806	184	14	the	the	DET
ajst-7806	184	15	highest	high	ADJ
ajst-7806	184	16	score	score	NOUN
ajst-7806	184	17	is	be	AUX
ajst-7806	184	18	higher	high	ADJ
ajst-7806	184	19	than	than	ADP
ajst-7806	184	20	0.5	0.5	NUM
ajst-7806	184	21	.	.	NOUN
ajst-7806	184	22	3.5	3.5	NUM
ajst-7806	184	23	.	.	PUNCT
ajst-7806	185	1	object	object	NOUN
ajst-7806	185	2	detector	detector	NOUN
ajst-7806	185	3	refinement	refinement	NOUN
ajst-7806	185	4	after	after	ADP
ajst-7806	185	5	the	the	DET
ajst-7806	185	6	base	base	NOUN
ajst-7806	185	7	multi	multi	ADJ
ajst-7806	185	8	-	-	ADJ
ajst-7806	185	9	instance	instance	ADJ
ajst-7806	185	10	learning	learning	NOUN
ajst-7806	185	11	detector	detector	NOUN
ajst-7806	185	12	,	,	PUNCT
ajst-7806	185	13	k	k	PROPN
ajst-7806	185	14	classifier	classifier	PROPN
ajst-7806	185	15	branches	branch	NOUN
ajst-7806	185	16	are	be	AUX
ajst-7806	185	17	iteratively	iteratively	ADV
ajst-7806	185	18	trained	train	VERB
ajst-7806	185	19	,	,	PUNCT
ajst-7806	185	20	and	and	CCONJ
ajst-7806	185	21	the	the	DET
ajst-7806	185	22	refined	refined	ADJ
ajst-7806	185	23	instance	instance	NOUN
ajst-7806	185	24	detector	detector	NOUN
ajst-7806	185	25	section	section	NOUN
ajst-7806	185	26	contains	contain	VERB
ajst-7806	185	27	k	k	PROPN
ajst-7806	185	28	classifier	classifier	NOUN
ajst-7806	185	29	branches	branch	NOUN
ajst-7806	185	30	from	from	ADP
ajst-7806	185	31	cls	cls	NOUN
ajst-7806	185	32	1	1	NUM
ajst-7806	185	33	to	to	ADP
ajst-7806	185	34	cls	cls	PROPN
ajst-7806	185	35	k.	k.	PROPN
ajst-7806	186	1	the	the	DET
ajst-7806	186	2	final	final	ADJ
ajst-7806	186	3	bbox	bbox	NOUN
ajst-7806	186	4	is	be	AUX
ajst-7806	186	5	obtained	obtain	VERB
ajst-7806	186	6	by	by	ADP
ajst-7806	186	7	box	box	NOUN
ajst-7806	186	8	regression	regression	NOUN
ajst-7806	186	9	after	after	ADP
ajst-7806	186	10	the	the	DET
ajst-7806	186	11	last	last	ADJ
ajst-7806	186	12	classifier	classifier	NOUN
ajst-7806	186	13	branch	branch	PROPN
ajst-7806	186	14	cls	cls	PROPN
ajst-7806	186	15	k.	k.	PROPN
ajst-7806	186	16	each	each	DET
ajst-7806	186	17	classifier	classifier	PROPN
ajst-7806	186	18	outputs	output	VERB
ajst-7806	186	19	a	a	DET
ajst-7806	186	20	pseudolabel	pseudolabel	NOUN
ajst-7806	186	21	as	as	ADP
ajst-7806	186	22	the	the	DET
ajst-7806	186	23	supervision	supervision	NOUN
ajst-7806	186	24	of	of	ADP
ajst-7806	186	25	the	the	DET
ajst-7806	186	26	next	next	ADJ
ajst-7806	186	27	classifier	classifier	NOUN
ajst-7806	186	28	,	,	PUNCT
ajst-7806	186	29	so	so	CCONJ
ajst-7806	186	30	the	the	DET
ajst-7806	186	31	whole	whole	ADJ
ajst-7806	186	32	process	process	NOUN
ajst-7806	186	33	only	only	ADV
ajst-7806	186	34	the	the	DET
ajst-7806	186	35	initial	initial	ADJ
ajst-7806	186	36	classifier	classifier	NOUN
ajst-7806	186	37	cls	cls	NOUN
ajst-7806	186	38	0	0	NUM
ajst-7806	186	39	,	,	PUNCT
ajst-7806	186	40	which	which	PRON
ajst-7806	186	41	is	be	AUX
ajst-7806	186	42	the	the	DET
ajst-7806	186	43	base	base	ADJ
ajst-7806	186	44	multi	multi	ADJ
ajst-7806	186	45	-	-	ADJ
ajst-7806	186	46	instance	instance	ADJ
ajst-7806	186	47	learning	learning	NOUN
ajst-7806	186	48	detector	detector	NOUN
ajst-7806	186	49	,	,	PUNCT
ajst-7806	186	50	uses	use	VERB
ajst-7806	186	51	the	the	DET
ajst-7806	186	52	real	real	ADJ
ajst-7806	186	53	image	image	NOUN
ajst-7806	186	54	labels	label	NOUN
ajst-7806	186	55	.	.	PUNCT
ajst-7806	187	1	the	the	DET
ajst-7806	187	2	subsequent	subsequent	ADJ
ajst-7806	187	3	k	k	PROPN
ajst-7806	187	4	classifiers	classifier	NOUN
ajst-7806	187	5	are	be	AUX
ajst-7806	187	6	trained	train	VERB
ajst-7806	187	7	with	with	ADP
ajst-7806	187	8	pseudo	pseudo	NOUN
ajst-7806	187	9	-	-	NOUN
ajst-7806	187	10	labels	label	NOUN
ajst-7806	187	11	,	,	PUNCT
ajst-7806	187	12	and	and	CCONJ
ajst-7806	187	13	for	for	ADP
ajst-7806	187	14	the	the	DET
ajst-7806	187	15	kth	kth	PROPN
ajst-7806	187	16	classifier	classifier	NOUN
ajst-7806	187	17	,	,	PUNCT
ajst-7806	187	18	its	its	PRON
ajst-7806	187	19	loss	loss	NOUN
ajst-7806	187	20	function	function	NOUN
ajst-7806	187	21	is	be	AUX
ajst-7806	187	22	as	as	ADP
ajst-7806	187	23	the	the	DET
ajst-7806	187	24	following	follow	VERB
ajst-7806	187	25	eq.15	eq.15	NOUN
ajst-7806	187	26	:	:	PUNCT
ajst-7806	187	27	1	1	NUM
ajst-7806	187	28	(	(	PUNCT
ajst-7806	187	29	(	(	PUNCT
ajst-7806	187	30	,	,	PUNCT
ajst-7806	187	31	)	)	PUNCT
ajst-7806	187	32	)	)	PUNCT
ajst-7806	188	1	|	|	ADV
ajst-7806	189	1	|	|	ADV
ajst-7806	189	2	k	k	PROPN
ajst-7806	190	1	k	k	PROPN
ajst-7806	190	2	k	k	PROPN
ajst-7806	190	3	k	k	PROPN
ajst-7806	190	4	ref	ref	NOUN
ajst-7806	191	1	r	r	NOUN
ajst-7806	191	2	r	r	NOUN
ajst-7806	191	3	r	r	NOUN
ajst-7806	191	4	r	r	NOUN
ajst-7806	191	5	r	r	NOUN
ajst-7806	191	6	l	l	NOUN
ajst-7806	191	7	w	w	NOUN
ajst-7806	191	8	ce	ce	PROPN
ajst-7806	192	1	p	p	X
ajst-7806	192	2	p	p	X
ajst-7806	192	3	r	r	NOUN
ajst-7806	192	4			PROPN
ajst-7806	192	5			NOUN
ajst-7806	192	6			NUM
ajst-7806	192	7			PROPN
ajst-7806	192	8			SYM
ajst-7806	192	9	(	(	PUNCT
ajst-7806	192	10	15	15	NUM
ajst-7806	192	11	)	)	PUNCT
ajst-7806	192	12	the	the	DET
ajst-7806	192	13	ce	ce	PROPN
ajst-7806	192	14	part	part	NOUN
ajst-7806	192	15	of	of	ADP
ajst-7806	192	16	the	the	DET
ajst-7806	192	17	equation	equation	NOUN
ajst-7806	192	18	is	be	AUX
ajst-7806	192	19	as	as	SCONJ
ajst-7806	192	20	follows	follow	VERB
ajst-7806	192	21	:	:	PUNCT
ajst-7806	192	22	0	0	PUNCT
ajst-7806	192	23	(	(	PUNCT
ajst-7806	192	24	,	,	PUNCT
ajst-7806	192	25	)	)	PUNCT
ajst-7806	192	26	log	log	NOUN
ajst-7806	192	27	(	(	PUNCT
ajst-7806	192	28	)	)	PUNCT
ajst-7806	192	29	ck	ck	PROPN
ajst-7806	193	1	k	k	PROPN
ajst-7806	193	2	k	k	PROPN
ajst-7806	193	3	k	k	NOUN
ajst-7806	193	4	r	r	NOUN
ajst-7806	193	5	r	r	PROPN
ajst-7806	193	6	rc	rc	PROPN
ajst-7806	193	7	rcc	rcc	PROPN
ajst-7806	193	8	ce	ce	PROPN
ajst-7806	194	1	p	p	PROPN
ajst-7806	195	1	p	p	X
ajst-7806	195	2	p	p	X
ajst-7806	195	3	p	p	X
ajst-7806	195	4			PROPN
ajst-7806	195	5			PROPN
ajst-7806	195	6			NUM
ajst-7806	195	7			NUM
ajst-7806	196	1			X
ajst-7806	196	2	(	(	PUNCT
ajst-7806	196	3	16	16	NUM
ajst-7806	196	4	)	)	PUNCT
ajst-7806	196	5	k	k	NOUN
ajst-7806	196	6	rcp	rcp	VERB
ajst-7806	196	7			PROPN
ajst-7806	196	8	and	and	CCONJ
ajst-7806	196	9	k	k	PROPN
ajst-7806	196	10	rcp	rcp	NOUN
ajst-7806	196	11	are	be	AUX
ajst-7806	196	12	respectively	respectively	ADV
ajst-7806	196	13	the	the	DET
ajst-7806	196	14	prediction	prediction	NOUN
ajst-7806	196	15	result	result	NOUN
ajst-7806	196	16	and	and	CCONJ
ajst-7806	196	17	label	label	NOUN
ajst-7806	196	18	of	of	ADP
ajst-7806	196	19	proposal	proposal	NOUN
ajst-7806	196	20	r	r	NOUN
ajst-7806	196	21	for	for	ADP
ajst-7806	196	22	the	the	DET
ajst-7806	196	23	c	c	NOUN
ajst-7806	196	24	-	-	PUNCT
ajst-7806	196	25	th	th	VERB
ajst-7806	196	26	category	category	NOUN
ajst-7806	196	27	,	,	PUNCT
ajst-7806	196	28	which	which	PRON
ajst-7806	196	29	is	be	AUX
ajst-7806	196	30	the	the	DET
ajst-7806	196	31	pseudolabel	pseudolabel	NOUN
ajst-7806	196	32	generated	generate	VERB
ajst-7806	196	33	by	by	ADP
ajst-7806	196	34	the	the	DET
ajst-7806	196	35	previous	previous	ADJ
ajst-7806	196	36	classifier	classifier	NOUN
ajst-7806	196	37	.	.	PUNCT
ajst-7806	197	1	the	the	DET
ajst-7806	197	2	focus	focus	NOUN
ajst-7806	197	3	is	be	AUX
ajst-7806	197	4	on	on	ADP
ajst-7806	197	5	this	this	DET
ajst-7806	197	6	weighting	weighting	NOUN
ajst-7806	197	7	factor	factor	NOUN
ajst-7806	197	8	,	,	PUNCT
ajst-7806	197	9	which	which	PRON
ajst-7806	197	10	is	be	AUX
ajst-7806	197	11	obtained	obtain	VERB
ajst-7806	197	12	from	from	ADP
ajst-7806	197	13	the	the	DET
ajst-7806	197	14	top	top	ADJ
ajst-7806	197	15	-	-	PUNCT
ajst-7806	197	16	down	down	ADP
ajst-7806	197	17	information	information	NOUN
ajst-7806	197	18	and	and	CCONJ
ajst-7806	197	19	the	the	DET
ajst-7806	197	20	bottom	bottom	ADJ
ajst-7806	197	21	-	-	PUNCT
ajst-7806	197	22	up	up	ADP
ajst-7806	197	23	information	information	NOUN
ajst-7806	197	24	,	,	PUNCT
ajst-7806	197	25	as	as	SCONJ
ajst-7806	197	26	shown	show	VERB
ajst-7806	197	27	in	in	ADP
ajst-7806	197	28	eq.17	eq.17	NOUN
ajst-7806	197	29	:	:	PUNCT
ajst-7806	197	30	(	(	PUNCT
ajst-7806	197	31	)	)	PUNCT
ajst-7806	197	32	(	(	PUNCT
ajst-7806	197	33	1	1	X
ajst-7806	197	34	)	)	PUNCT
ajst-7806	197	35	(	(	PUNCT
ajst-7806	197	36	)	)	PUNCT
ajst-7806	197	37	k	k	X
ajst-7806	198	1	k	k	NOUN
ajst-7806	198	2	r	r	NOUN
ajst-7806	198	3	bu	bu	INTJ
ajst-7806	198	4	tdw	tdw	NOUN
ajst-7806	198	5	o	o	NOUN
ajst-7806	198	6	r	r	NOUN
ajst-7806	198	7	o	o	NOUN
ajst-7806	198	8	r	r	NOUN
ajst-7806	198	9			NOUN
ajst-7806	198	10			VERB
ajst-7806	198	11			PROPN
ajst-7806	198	12	(	(	PUNCT
ajst-7806	198	13	17	17	NUM
ajst-7806	198	14	)	)	PUNCT
ajst-7806	198	15	the	the	DET
ajst-7806	198	16	bottom	bottom	ADJ
ajst-7806	198	17	-	-	PUNCT
ajst-7806	198	18	up	up	ADP
ajst-7806	198	19	object	object	NOUN
ajst-7806	198	20	evidence	evidence	NOUN
ajst-7806	198	21	is	be	AUX
ajst-7806	198	22	the	the	DET
ajst-7806	198	23	similarity	similarity	NOUN
ajst-7806	198	24	score	score	NOUN
ajst-7806	198	25	objectness	objectness	NOUN
ajst-7806	198	26	mentioned	mention	VERB
ajst-7806	198	27	before	before	ADV
ajst-7806	198	28	,	,	PUNCT
ajst-7806	198	29	which	which	PRON
ajst-7806	198	30	is	be	AUX
ajst-7806	198	31	the	the	DET
ajst-7806	198	32	four	four	NUM
ajst-7806	198	33	similarity	similarity	NOUN
ajst-7806	198	34	measures	measure	NOUN
ajst-7806	198	35	,	,	PUNCT
ajst-7806	198	36	namely	namely	ADV
ajst-7806	198	37	ms(multi	ms(multi	ADJ
ajst-7806	198	38	-	-	PUNCT
ajst-7806	198	39	scale	scale	NOUN
ajst-7806	198	40	saliency	saliency	NOUN
ajst-7806	198	41	)	)	PUNCT
ajst-7806	198	42	,	,	PUNCT
ajst-7806	198	43	cc(color	cc(color	NOUN
ajst-7806	198	44	constrast	constrast	NOUN
ajst-7806	198	45	)	)	PUNCT
ajst-7806	198	46	,	,	PUNCT
ajst-7806	198	47	ed(edge	ed(edge	VERB
ajst-7806	198	48	density	density	NOUN
ajst-7806	198	49	)	)	PUNCT
ajst-7806	198	50	and	and	CCONJ
ajst-7806	198	51	ss(superpixels	ss(superpixel	NOUN
ajst-7806	198	52	straddling	straddle	VERB
ajst-7806	198	53	)	)	PUNCT
ajst-7806	198	54	,	,	PUNCT
ajst-7806	198	55	which	which	PRON
ajst-7806	198	56	is	be	AUX
ajst-7806	198	57	the	the	DET
ajst-7806	198	58	classification	classification	NOUN
ajst-7806	198	59	score	score	NOUN
ajst-7806	198	60	calculated	calculate	VERB
ajst-7806	198	61	according	accord	VERB
ajst-7806	198	62	to	to	ADP
ajst-7806	198	63	the	the	DET
ajst-7806	198	64	classification	classification	NOUN
ajst-7806	198	65	result	result	NOUN
ajst-7806	198	66	obtained	obtain	VERB
ajst-7806	198	67	by	by	ADP
ajst-7806	198	68	the	the	DET
ajst-7806	198	69	previous	previous	ADJ
ajst-7806	198	70	one	one	NUM
ajst-7806	198	71	,	,	PUNCT
ajst-7806	198	72	namely	namely	ADV
ajst-7806	198	73	the	the	DET
ajst-7806	198	74	k-1st	k-1st	PROPN
ajst-7806	198	75	classifier	classifier	NOUN
ajst-7806	198	76	,	,	PUNCT
ajst-7806	198	77	as	as	SCONJ
ajst-7806	198	78	shown	show	VERB
ajst-7806	198	79	in	in	ADP
ajst-7806	198	80	eq.18	eq.18	NOUN
ajst-7806	198	81	:	:	PUNCT
ajst-7806	198	82	1	1	NUM
ajst-7806	198	83	0	0	NUM
ajst-7806	198	84	(	(	PUNCT
ajst-7806	198	85	)	)	PUNCT
ajst-7806	198	86	(	(	PUNCT
ajst-7806	198	87	)	)	PUNCT
ajst-7806	199	1	c	c	X
ajst-7806	199	2	k	k	PROPN
ajst-7806	199	3	k	k	PROPN
ajst-7806	199	4	k	k	PROPN
ajst-7806	199	5	td	td	PROPN
ajst-7806	199	6	rc	rc	PROPN
ajst-7806	199	7	rc	rc	PROPN
ajst-7806	200	1	c	c	NOUN
ajst-7806	200	2	o	o	NOUN
ajst-7806	200	3	r	r	NOUN
ajst-7806	201	1	p	p	X
ajst-7806	201	2	p	p	X
ajst-7806	201	3			PROPN
ajst-7806	201	4			PROPN
ajst-7806	201	5			NOUN
ajst-7806	201	6			PRON
ajst-7806	201	7			X
ajst-7806	201	8	(	(	PUNCT
ajst-7806	201	9	18	18	NUM
ajst-7806	201	10	)	)	PUNCT
ajst-7806	201	11			NOUN
ajst-7806	201	12	is	be	AUX
ajst-7806	201	13	a	a	DET
ajst-7806	201	14	balance	balance	NOUN
ajst-7806	201	15	factor	factor	NOUN
ajst-7806	201	16	set	set	VERB
ajst-7806	201	17	by	by	ADP
ajst-7806	201	18	itself	itself	PRON
ajst-7806	201	19	to	to	PART
ajst-7806	201	20	balance	balance	VERB
ajst-7806	201	21	the	the	DET
ajst-7806	201	22	weight	weight	NOUN
ajst-7806	201	23	of	of	ADP
ajst-7806	201	24	these	these	DET
ajst-7806	201	25	two	two	NUM
ajst-7806	201	26	information	information	NOUN
ajst-7806	201	27	.	.	PUNCT
ajst-7806	202	1	the	the	DET
ajst-7806	202	2	intuitive	intuitive	ADJ
ajst-7806	202	3	understanding	understanding	NOUN
ajst-7806	202	4	of	of	ADP
ajst-7806	202	5	this	this	DET
ajst-7806	202	6	loss	loss	NOUN
ajst-7806	202	7	function	function	NOUN
ajst-7806	202	8	is	be	AUX
ajst-7806	202	9	to	to	PART
ajst-7806	202	10	penalize	penalize	VERB
ajst-7806	202	11	the	the	DET
ajst-7806	202	12	classification	classification	NOUN
ajst-7806	202	13	result	result	NOUN
ajst-7806	202	14	of	of	ADP
ajst-7806	202	15	the	the	DET
ajst-7806	202	16	current	current	ADJ
ajst-7806	202	17	classifier	classifier	NOUN
ajst-7806	202	18	for	for	ADP
ajst-7806	202	19	each	each	DET
ajst-7806	202	20	proposal	proposal	NOUN
ajst-7806	202	21	with	with	ADP
ajst-7806	202	22	the	the	DET
ajst-7806	202	23	pseudo	pseudo	NOUN
ajst-7806	202	24	-	-	NOUN
ajst-7806	202	25	label	label	NOUN
ajst-7806	202	26	generated	generate	VERB
ajst-7806	202	27	by	by	ADP
ajst-7806	202	28	the	the	DET
ajst-7806	202	29	previous	previous	ADJ
ajst-7806	202	30	classifier	classifier	NOUN
ajst-7806	202	31	,	,	PUNCT
ajst-7806	202	32	and	and	CCONJ
ajst-7806	202	33	the	the	PRON
ajst-7806	202	34	higher	high	ADJ
ajst-7806	202	35	the	the	DET
ajst-7806	202	36	weight	weight	NOUN
ajst-7806	202	37	of	of	ADP
ajst-7806	202	38	the	the	DET
ajst-7806	202	39	proposal	proposal	NOUN
ajst-7806	202	40	the	the	PRON
ajst-7806	202	41	stronger	strong	ADJ
ajst-7806	202	42	the	the	DET
ajst-7806	202	43	penalty	penalty	NOUN
ajst-7806	202	44	.	.	PUNCT
ajst-7806	203	1	3.6	3.6	NUM
ajst-7806	203	2	.	.	X
ajst-7806	204	1	bounding	bound	VERB
ajst-7806	204	2	box	box	NOUN
ajst-7806	204	3	regression	regression	NOUN
ajst-7806	204	4	since	since	SCONJ
ajst-7806	204	5	it	it	PRON
ajst-7806	204	6	is	be	AUX
ajst-7806	204	7	weakly	weakly	ADV
ajst-7806	204	8	supervised	supervised	ADJ
ajst-7806	204	9	learning	learning	NOUN
ajst-7806	204	10	,	,	PUNCT
ajst-7806	204	11	there	there	PRON
ajst-7806	204	12	is	be	VERB
ajst-7806	204	13	no	no	DET
ajst-7806	204	14	strong	strong	ADJ
ajst-7806	204	15	supervised	supervised	ADJ
ajst-7806	204	16	information	information	NOUN
ajst-7806	204	17	in	in	ADP
ajst-7806	204	18	the	the	DET
ajst-7806	204	19	dataset	dataset	NOUN
ajst-7806	204	20	.	.	PUNCT
ajst-7806	205	1	in	in	ADP
ajst-7806	205	2	oicr	oicr	NOUN
ajst-7806	205	3	,	,	PUNCT
ajst-7806	205	4	it	it	PRON
ajst-7806	205	5	relies	rely	VERB
ajst-7806	205	6	on	on	ADP
ajst-7806	205	7	the	the	DET
ajst-7806	205	8	location	location	NOUN
ajst-7806	205	9	of	of	ADP
ajst-7806	205	10	the	the	DET
ajst-7806	205	11	highest	high	ADJ
ajst-7806	205	12	scoring	scoring	NOUN
ajst-7806	205	13	region	region	NOUN
ajst-7806	205	14	proposal	proposal	NOUN
ajst-7806	205	15	in	in	ADP
ajst-7806	205	16	the	the	DET
ajst-7806	205	17	multiinstance	multiinstance	NOUN
ajst-7806	205	18	learning	learn	VERB
ajst-7806	205	19	branch	branch	NOUN
ajst-7806	205	20	,	,	PUNCT
ajst-7806	205	21	but	but	CCONJ
ajst-7806	205	22	this	this	DET
ajst-7806	205	23	label	label	NOUN
ajst-7806	205	24	is	be	AUX
ajst-7806	205	25	a	a	DET
ajst-7806	205	26	coarse	coarse	ADJ
ajst-7806	205	27	label	label	NOUN
ajst-7806	205	28	,	,	PUNCT
ajst-7806	205	29	and	and	CCONJ
ajst-7806	205	30	this	this	DET
ajst-7806	205	31	coarse	coarse	ADJ
ajst-7806	205	32	prediction	prediction	NOUN
ajst-7806	205	33	result	result	NOUN
ajst-7806	205	34	will	will	AUX
ajst-7806	205	35	definitely	definitely	ADV
ajst-7806	205	36	give	give	VERB
ajst-7806	205	37	a	a	DET
ajst-7806	205	38	bad	bad	ADJ
ajst-7806	205	39	effect	effect	NOUN
ajst-7806	205	40	to	to	ADP
ajst-7806	205	41	the	the	DET
ajst-7806	205	42	detector	detector	NOUN
ajst-7806	205	43	.	.	PUNCT
ajst-7806	206	1	so	so	ADV
ajst-7806	206	2	this	this	DET
ajst-7806	206	3	paper	paper	NOUN
ajst-7806	206	4	adds	add	VERB
ajst-7806	206	5	a	a	DET
ajst-7806	206	6	regression	regression	NOUN
ajst-7806	206	7	branch	branch	NOUN
ajst-7806	206	8	to	to	ADP
ajst-7806	206	9	the	the	DET
ajst-7806	206	10	previous	previous	ADJ
ajst-7806	206	11	module	module	NOUN
ajst-7806	206	12	.	.	PUNCT
ajst-7806	207	1	although	although	SCONJ
ajst-7806	207	2	convolutional	convolutional	ADJ
ajst-7806	207	3	neural	neural	ADJ
ajst-7806	207	4	networks	network	NOUN
ajst-7806	207	5	can	can	AUX
ajst-7806	207	6	learn	learn	VERB
ajst-7806	207	7	features	feature	NOUN
ajst-7806	207	8	well	well	ADV
ajst-7806	207	9	,	,	PUNCT
ajst-7806	207	10	they	they	PRON
ajst-7806	207	11	have	have	VERB
ajst-7806	207	12	shortcomings	shortcoming	NOUN
ajst-7806	207	13	in	in	ADP
ajst-7806	207	14	discovering	discover	VERB
ajst-7806	207	15	boundaries	boundary	NOUN
ajst-7806	207	16	,	,	PUNCT
ajst-7806	207	17	so	so	ADV
ajst-7806	207	18	during	during	ADP
ajst-7806	207	19	training	training	NOUN
ajst-7806	207	20	,	,	PUNCT
ajst-7806	207	21	we	we	PRON
ajst-7806	207	22	explore	explore	VERB
ajst-7806	207	23	how	how	SCONJ
ajst-7806	207	24	to	to	PART
ajst-7806	207	25	use	use	VERB
ajst-7806	207	26	bottom	bottom	ADJ
ajst-7806	207	27	-	-	PUNCT
ajst-7806	207	28	up	up	ADP
ajst-7806	207	29	object	object	NOUN
ajst-7806	207	30	evidence	evidence	NOUN
ajst-7806	207	31	to	to	PART
ajst-7806	207	32	guide	guide	VERB
ajst-7806	207	33	the	the	DET
ajst-7806	207	34	target	target	NOUN
ajst-7806	207	35	's	's	PART
ajst-7806	207	36	bounding	bounding	NOUN
ajst-7806	207	37	box	box	NOUN
ajst-7806	207	38	for	for	ADP
ajst-7806	207	39	updating	update	VERB
ajst-7806	207	40	.	.	PUNCT
ajst-7806	208	1	an	an	DET
ajst-7806	208	2	objective	objective	ADJ
ajst-7806	208	3	detector	detector	NOUN
ajst-7806	208	4	is	be	AUX
ajst-7806	208	5	actually	actually	ADV
ajst-7806	208	6	a	a	DET
ajst-7806	208	7	bounding	bounding	NOUN
ajst-7806	208	8	box	box	NOUN
ajst-7806	208	9	sorting	sorting	NOUN
ajst-7806	208	10	function	function	NOUN
ajst-7806	208	11	,	,	PUNCT
ajst-7806	208	12	where	where	SCONJ
ajst-7806	208	13	an	an	DET
ajst-7806	208	14	important	important	ADJ
ajst-7806	208	15	factor	factor	NOUN
ajst-7806	208	16	is	be	AUX
ajst-7806	208	17	the	the	DET
ajst-7806	208	18	objective	objective	ADJ
ajst-7806	208	19	metric	metric	NOUN
ajst-7806	208	20	.	.	PUNCT
ajst-7806	209	1	in	in	ADP
ajst-7806	209	2	weakly	weakly	ADJ
ajst-7806	209	3	supervised	supervised	ADJ
ajst-7806	209	4	target	target	NOUN
ajst-7806	209	5	detection	detection	NOUN
ajst-7806	209	6	,	,	PUNCT
ajst-7806	209	7	if	if	SCONJ
ajst-7806	209	8	the	the	DET
ajst-7806	209	9	classification	classification	NOUN
ajst-7806	209	10	confidence	confidence	NOUN
ajst-7806	209	11	is	be	AUX
ajst-7806	209	12	considered	consider	VERB
ajst-7806	209	13	as	as	ADP
ajst-7806	209	14	an	an	DET
ajst-7806	209	15	objective	objective	ADJ
ajst-7806	209	16	score	score	NOUN
ajst-7806	209	17	,	,	PUNCT
ajst-7806	209	18	the	the	DET
ajst-7806	209	19	shortcoming	shortcoming	NOUN
ajst-7806	209	20	is	be	AUX
ajst-7806	209	21	that	that	SCONJ
ajst-7806	209	22	even	even	ADV
ajst-7806	209	23	very	very	ADV
ajst-7806	209	24	good	good	ADJ
ajst-7806	209	25	detectors	detector	NOUN
ajst-7806	209	26	have	have	VERB
ajst-7806	209	27	difficulty	difficulty	NOUN
ajst-7806	209	28	in	in	ADP
ajst-7806	209	29	distinguishing	distinguish	VERB
ajst-7806	209	30	complete	complete	ADJ
ajst-7806	209	31	objects	object	NOUN
ajst-7806	209	32	from	from	ADP
ajst-7806	209	33	obviousness	obviousness	ADJ
ajst-7806	209	34	object	object	NOUN
ajst-7806	209	35	parts	part	NOUN
ajst-7806	209	36	or	or	CCONJ
ajst-7806	209	37	irrelevant	irrelevant	ADJ
ajst-7806	209	38	backgrounds	background	NOUN
ajst-7806	209	39	.	.	PUNCT
ajst-7806	210	1	in	in	ADP
ajst-7806	210	2	target	target	NOUN
ajst-7806	210	3	detection	detection	NOUN
ajst-7806	210	4	,	,	PUNCT
ajst-7806	210	5	the	the	DET
ajst-7806	210	6	most	most	ADV
ajst-7806	210	7	important	important	ADJ
ajst-7806	210	8	thing	thing	NOUN
ajst-7806	210	9	is	be	AUX
ajst-7806	210	10	said	say	VERB
ajst-7806	210	11	to	to	PART
ajst-7806	210	12	be	be	AUX
ajst-7806	210	13	clear	clear	ADJ
ajst-7806	210	14	boundaries	boundary	NOUN
ajst-7806	210	15	and	and	CCONJ
ajst-7806	210	16	centers	center	NOUN
ajst-7806	210	17	.	.	PUNCT
ajst-7806	211	1	therefore	therefore	ADV
ajst-7806	211	2	we	we	PRON
ajst-7806	211	3	expect	expect	VERB
ajst-7806	211	4	to	to	PART
ajst-7806	211	5	eventually	eventually	ADV
ajst-7806	211	6	find	find	VERB
ajst-7806	211	7	a	a	DET
ajst-7806	211	8	bounding	bounding	NOUN
ajst-7806	211	9	box	box	NOUN
ajst-7806	211	10	that	that	PRON
ajst-7806	211	11	completely	completely	ADV
ajst-7806	211	12	encloses	enclose	VERB
ajst-7806	211	13	the	the	DET
ajst-7806	211	14	complete	complete	ADJ
ajst-7806	211	15	object	object	NOUN
ajst-7806	211	16	,	,	PUNCT
ajst-7806	211	17	and	and	CCONJ
ajst-7806	211	18	the	the	DET
ajst-7806	211	19	abovementioned	abovementione	VERB
ajst-7806	211	20	(	(	PUNCT
ajst-7806	211	21	bottom	bottom	ADJ
ajst-7806	211	22	-	-	PUNCT
ajst-7806	211	23	up	up	ADP
ajst-7806	211	24	object	object	NOUN
ajst-7806	211	25	)	)	PUNCT
ajst-7806	211	26	features	feature	VERB
ajst-7806	211	27	with	with	ADP
ajst-7806	211	28	object	object	NOUN
ajst-7806	211	29	boundaries	boundary	NOUN
ajst-7806	211	30	can	can	AUX
ajst-7806	211	31	exactly	exactly	ADV
ajst-7806	211	32	compensate	compensate	VERB
ajst-7806	211	33	for	for	ADP
ajst-7806	211	34	cnn	cnn	PROPN
ajst-7806	211	35	's	's	PART
ajst-7806	211	36	deficiency	deficiency	NOUN
ajst-7806	211	37	in	in	ADP
ajst-7806	211	38	its	its	PRON
ajst-7806	211	39	aspect	aspect	NOUN
ajst-7806	211	40	.	.	PUNCT
ajst-7806	212	1	the	the	DET
ajst-7806	212	2	position	position	NOUN
ajst-7806	212	3	loss	loss	NOUN
ajst-7806	212	4	function	function	NOUN
ajst-7806	212	5	uses	use	VERB
ajst-7806	212	6	l1	l1	PROPN
ajst-7806	212	7	,	,	PUNCT
ajst-7806	212	8	l2	l2	NOUN
ajst-7806	212	9	or	or	CCONJ
ajst-7806	212	10	smooth	smooth	ADJ
ajst-7806	212	11	loss	loss	NOUN
ajst-7806	212	12	functions	function	NOUN
ajst-7806	212	13	to	to	PART
ajst-7806	212	14	regress	regress	VERB
ajst-7806	212	15	the	the	DET
ajst-7806	212	16	four	four	NUM
ajst-7806	212	17	coordinate	coordinate	NOUN
ajst-7806	212	18	values	value	NOUN
ajst-7806	212	19	.	.	PUNCT
ajst-7806	213	1	the	the	DET
ajst-7806	213	2	goal	goal	NOUN
ajst-7806	213	3	of	of	ADP
ajst-7806	213	4	the	the	DET
ajst-7806	213	5	regressor	regressor	NOUN
ajst-7806	213	6	is	be	AUX
ajst-7806	213	7	to	to	PART
ajst-7806	213	8	output	output	VERB
ajst-7806	213	9	a	a	DET
ajst-7806	213	10	correction	correction	NOUN
ajst-7806	213	11	for	for	ADP
ajst-7806	213	12	each	each	DET
ajst-7806	213	13	box	box	NOUN
ajst-7806	213	14	for	for	ADP
ajst-7806	213	15	each	each	PRON
ajst-7806	213	16	of	of	ADP
ajst-7806	213	17	the	the	DET
ajst-7806	213	18	four	four	NUM
ajst-7806	213	19	parameters	parameter	NOUN
ajst-7806	213	20	x	x	X
ajst-7806	213	21	,	,	PUNCT
ajst-7806	213	22	y	y	PROPN
ajst-7806	213	23	,	,	PUNCT
ajst-7806	213	24	w	w	PROPN
ajst-7806	213	25	,	,	PUNCT
ajst-7806	213	26	h	h	NOUN
ajst-7806	213	27	:	:	PUNCT
ajst-7806	213	28	(	(	PUNCT
ajst-7806	213	29	,	,	PUNCT
ajst-7806	213	30	,	,	PUNCT
ajst-7806	213	31	,	,	PUNCT
ajst-7806	213	32	)	)	PUNCT
ajst-7806	213	33	x	x	PROPN
ajst-7806	213	34	y	y	PROPN
ajst-7806	213	35	w	w	NOUN
ajst-7806	213	36	h	h	NOUN
ajst-7806	213	37	r	r	NOUN
ajst-7806	213	38	r	r	NOUN
ajst-7806	213	39	r	r	NOUN
ajst-7806	213	40	r	r	NOUN
ajst-7806	213	41	rt	rt	PROPN
ajst-7806	213	42	t	t	PROPN
ajst-7806	213	43	t	t	PROPN
ajst-7806	213	44	t	t	PROPN
ajst-7806	213	45	t	t	PROPN
ajst-7806	213	46	(	(	PUNCT
ajst-7806	213	47	19	19	NUM
ajst-7806	213	48	)	)	PUNCT
ajst-7806	213	49	a	a	DET
ajst-7806	213	50	total	total	NOUN
ajst-7806	213	51	of	of	ADP
ajst-7806	213	52	k	k	PROPN
ajst-7806	213	53	classifier	classifier	PROPN
ajst-7806	213	54	branches	branch	NOUN
ajst-7806	213	55	containing	contain	VERB
ajst-7806	213	56	cls	cls	NOUN
ajst-7806	213	57	1	1	NUM
ajst-7806	213	58	to	to	ADP
ajst-7806	213	59	cls	cls	NOUN
ajst-7806	213	60	k	k	PROPN
ajst-7806	213	61	are	be	AUX
ajst-7806	213	62	divided	divide	VERB
ajst-7806	213	63	,	,	PUNCT
ajst-7806	213	64	and	and	CCONJ
ajst-7806	213	65	the	the	DET
ajst-7806	213	66	final	final	ADJ
ajst-7806	213	67	bbox	bbox	NOUN
ajst-7806	213	68	is	be	AUX
ajst-7806	213	69	obtained	obtain	VERB
ajst-7806	213	70	by	by	ADP
ajst-7806	213	71	bounding	bound	VERB
ajst-7806	213	72	box	box	NOUN
ajst-7806	213	73	regression	regression	NOUN
ajst-7806	213	74	after	after	ADP
ajst-7806	213	75	the	the	DET
ajst-7806	213	76	last	last	ADJ
ajst-7806	213	77	classifier	classifier	NOUN
ajst-7806	213	78	branch	branch	PROPN
ajst-7806	213	79	cls	cls	PROPN
ajst-7806	213	80	k	k	PROPN
ajst-7806	213	81	,	,	PUNCT
ajst-7806	213	82	the	the	DET
ajst-7806	213	83	formula	formula	NOUN
ajst-7806	213	84	as	as	SCONJ
ajst-7806	213	85	shown	show	VERB
ajst-7806	213	86	in	in	ADP
ajst-7806	213	87	eq.20	eq.20	PROPN
ajst-7806	213	88	:	:	PUNCT
ajst-7806	213	89	144	144	NUM
ajst-7806	213	90	|	|	ADV
ajst-7806	213	91	|	|	ADV
ajst-7806	213	92	1	1	NUM
ajst-7806	213	93	1	1	NUM
ajst-7806	213	94	1	1	NUM
ajst-7806	213	95	(	(	PUNCT
ajst-7806	213	96	(	(	PUNCT
ajst-7806	213	97	,	,	PUNCT
ajst-7806	213	98	)	)	PUNCT
ajst-7806	213	99	)	)	PUNCT
ajst-7806	214	1	|	|	ADV
ajst-7806	214	2	|	|	ADV
ajst-7806	214	3	posr	posr	VERB
ajst-7806	214	4	k	k	PROPN
ajst-7806	214	5	box	box	PROPN
ajst-7806	214	6	r	r	NOUN
ajst-7806	214	7	l	l	NOUN
ajst-7806	214	8	r	r	NOUN
ajst-7806	214	9	r	r	NOUN
ajst-7806	214	10	rpos	rpos	NOUN
ajst-7806	214	11	l	l	NOUN
ajst-7806	214	12	w	w	PROPN
ajst-7806	214	13	smooth	smooth	PROPN
ajst-7806	215	1	t	t	PROPN
ajst-7806	215	2	t	t	NOUN
ajst-7806	215	3	r	r	NOUN
ajst-7806	215	4			PROPN
ajst-7806	215	5			NUM
ajst-7806	215	6			PROPN
ajst-7806	215	7			X
ajst-7806	215	8	(	(	PUNCT
ajst-7806	215	9	20	20	NUM
ajst-7806	215	10	)	)	PUNCT
ajst-7806	215	11	the	the	DET
ajst-7806	215	12	final	final	ADJ
ajst-7806	215	13	loss	loss	NOUN
ajst-7806	215	14	function	function	NOUN
ajst-7806	215	15	for	for	ADP
ajst-7806	215	16	all	all	DET
ajst-7806	215	17	modules	module	NOUN
ajst-7806	215	18	combined	combine	VERB
ajst-7806	215	19	is	be	AUX
ajst-7806	215	20	:	:	PUNCT
ajst-7806	215	21	1	1	NUM
ajst-7806	215	22	2	2	NUM
ajst-7806	215	23	1	1	NUM
ajst-7806	215	24	k	k	NOUN
ajst-7806	215	25	k	k	PROPN
ajst-7806	215	26	base	base	PROPN
ajst-7806	215	27	ref	ref	PROPN
ajst-7806	215	28	box	box	PROPN
ajst-7806	215	29	k	k	PROPN
ajst-7806	215	30	l	l	PROPN
ajst-7806	215	31	l	l	NOUN
ajst-7806	215	32	l	l	NOUN
ajst-7806	215	33	l	l	PROPN
ajst-7806	215	34			ADJ
ajst-7806	215	35			PROPN
ajst-7806	215	36			PROPN
ajst-7806	215	37			ADJ
ajst-7806	215	38			NOUN
ajst-7806	215	39	(	(	PUNCT
ajst-7806	215	40	21	21	NUM
ajst-7806	215	41	)	)	PUNCT
ajst-7806	215	42	3.7	3.7	NUM
ajst-7806	215	43	.	.	PUNCT
ajst-7806	216	1	overall	overall	ADJ
ajst-7806	216	2	training	training	NOUN
ajst-7806	216	3	framework	framework	NOUN
ajst-7806	216	4	firstly	firstly	ADV
ajst-7806	216	5	,	,	PUNCT
ajst-7806	216	6	given	give	VERB
ajst-7806	216	7	an	an	DET
ajst-7806	216	8	image	image	NOUN
ajst-7806	216	9	,	,	PUNCT
ajst-7806	216	10	a	a	DET
ajst-7806	216	11	region	region	NOUN
ajst-7806	216	12	proposal	proposal	NOUN
ajst-7806	216	13	r	r	NOUN
ajst-7806	216	14	is	be	AUX
ajst-7806	216	15	generated	generate	VERB
ajst-7806	216	16	by	by	ADP
ajst-7806	216	17	selective	selective	ADJ
ajst-7806	216	18	search	search	NOUN
ajst-7806	216	19	combined	combine	VERB
ajst-7806	216	20	with	with	ADP
ajst-7806	216	21	grad	grad	NOUN
ajst-7806	216	22	-	-	PUNCT
ajst-7806	216	23	cam++	cam++	PROPN
ajst-7806	216	24	,	,	PUNCT
ajst-7806	216	25	and	and	CCONJ
ajst-7806	216	26	then	then	ADV
ajst-7806	216	27	the	the	DET
ajst-7806	216	28	region	region	NOUN
ajst-7806	216	29	features	feature	NOUN
ajst-7806	216	30	are	be	AUX
ajst-7806	216	31	extracted	extract	VERB
ajst-7806	216	32	by	by	ADP
ajst-7806	216	33	cnn	cnn	PROPN
ajst-7806	216	34	and	and	CCONJ
ajst-7806	216	35	roi	roi	NOUN
ajst-7806	216	36	pooling	pool	VERB
ajst-7806	216	37	layers	layer	NOUN
ajst-7806	216	38	and	and	CCONJ
ajst-7806	216	39	two	two	NUM
ajst-7806	216	40	fully	fully	ADV
ajst-7806	216	41	connected	connected	ADJ
ajst-7806	216	42	layers	layer	NOUN
ajst-7806	216	43	.	.	PUNCT
ajst-7806	217	1	then	then	ADV
ajst-7806	217	2	,	,	PUNCT
ajst-7806	217	3	the	the	DET
ajst-7806	217	4	region	region	NOUN
ajst-7806	217	5	features	feature	VERB
ajst-7806	217	6	enter	enter	VERB
ajst-7806	217	7	two	two	NUM
ajst-7806	217	8	streams	stream	NOUN
ajst-7806	217	9	through	through	ADP
ajst-7806	217	10	two	two	NUM
ajst-7806	217	11	full	full	ADJ
ajst-7806	217	12	connections	connection	NOUN
ajst-7806	217	13	,	,	PUNCT
ajst-7806	217	14	one	one	NUM
ajst-7806	217	15	classification	classification	NOUN
ajst-7806	217	16	stream	stream	NOUN
ajst-7806	217	17	and	and	CCONJ
ajst-7806	217	18	one	one	NUM
ajst-7806	217	19	localization	localization	NOUN
ajst-7806	217	20	stream	stream	NOUN
ajst-7806	217	21	,	,	PUNCT
ajst-7806	217	22	and	and	CCONJ
ajst-7806	217	23	the	the	DET
ajst-7806	217	24	region	region	NOUN
ajst-7806	217	25	proposal	proposal	NOUN
ajst-7806	217	26	score	score	NOUN
ajst-7806	217	27	is	be	AUX
ajst-7806	217	28	obtained	obtain	VERB
ajst-7806	217	29	by	by	ADP
ajst-7806	217	30	multiplying	multiply	VERB
ajst-7806	217	31	the	the	DET
ajst-7806	217	32	corresponding	correspond	VERB
ajst-7806	217	33	elements	element	NOUN
ajst-7806	217	34	according	accord	VERB
ajst-7806	217	35	to	to	ADP
ajst-7806	217	36	the	the	DET
ajst-7806	217	37	formula	formula	NOUN
ajst-7806	217	38	.	.	PUNCT
ajst-7806	218	1	next	next	ADV
ajst-7806	218	2	,	,	PUNCT
ajst-7806	218	3	the	the	DET
ajst-7806	218	4	dimensions	dimension	NOUN
ajst-7806	218	5	of	of	ADP
ajst-7806	218	6	region	region	NOUN
ajst-7806	218	7	r	r	NOUN
ajst-7806	218	8	are	be	AUX
ajst-7806	218	9	aggregated	aggregate	VERB
ajst-7806	218	10	to	to	PART
ajst-7806	218	11	obtain	obtain	VERB
ajst-7806	218	12	the	the	DET
ajst-7806	218	13	image	image	NOUN
ajst-7806	218	14	-	-	PUNCT
ajst-7806	218	15	level	level	NOUN
ajst-7806	218	16	classification	classification	NOUN
ajst-7806	218	17	vector	vector	NOUN
ajst-7806	218	18	,	,	PUNCT
ajst-7806	218	19	and	and	CCONJ
ajst-7806	218	20	the	the	DET
ajst-7806	218	21	image	image	NOUN
ajst-7806	218	22	-	-	PUNCT
ajst-7806	218	23	level	level	NOUN
ajst-7806	218	24	labels	label	NOUN
ajst-7806	218	25	are	be	AUX
ajst-7806	218	26	used	use	VERB
ajst-7806	218	27	as	as	ADP
ajst-7806	218	28	supervision	supervision	NOUN
ajst-7806	218	29	to	to	PART
ajst-7806	218	30	guide	guide	VERB
ajst-7806	218	31	the	the	DET
ajst-7806	218	32	training	training	NOUN
ajst-7806	218	33	network	network	NOUN
ajst-7806	218	34	training	training	NOUN
ajst-7806	218	35	by	by	ADP
ajst-7806	218	36	applying	apply	VERB
ajst-7806	218	37	a	a	DET
ajst-7806	218	38	binary	binary	ADJ
ajst-7806	218	39	cross	cross	NOUN
ajst-7806	218	40	-	-	ADJ
ajst-7806	218	41	entropy	entropy	ADJ
ajst-7806	218	42	loss	loss	NOUN
ajst-7806	218	43	function	function	NOUN
ajst-7806	218	44	optimization	optimization	NOUN
ajst-7806	218	45	and	and	CCONJ
ajst-7806	218	46	summarizing	summarize	VERB
ajst-7806	218	47	its	its	PRON
ajst-7806	218	48	boundary	boundary	ADJ
ajst-7806	218	49	features	feature	NOUN
ajst-7806	218	50	with	with	ADP
ajst-7806	218	51	bottom	bottom	ADJ
ajst-7806	218	52	-	-	PUNCT
ajst-7806	218	53	up	up	ADP
ajst-7806	218	54	objects	object	NOUN
ajst-7806	218	55	.	.	PUNCT
ajst-7806	219	1	4	4	X
ajst-7806	219	2	.	.	X
ajst-7806	219	3	experimental	experimental	ADJ
ajst-7806	219	4	results	result	NOUN
ajst-7806	219	5	and	and	CCONJ
ajst-7806	219	6	analysis	analysis	NOUN
ajst-7806	219	7	4.1	4.1	NUM
ajst-7806	219	8	.	.	PUNCT
ajst-7806	220	1	experimental	experimental	ADJ
ajst-7806	220	2	setup	setup	NOUN
ajst-7806	220	3	this	this	DET
ajst-7806	220	4	paper	paper	NOUN
ajst-7806	220	5	evaluates	evaluate	VERB
ajst-7806	220	6	the	the	DET
ajst-7806	220	7	method	method	NOUN
ajst-7806	220	8	proposed	propose	VERB
ajst-7806	220	9	in	in	ADP
ajst-7806	220	10	this	this	DET
ajst-7806	220	11	paper	paper	NOUN
ajst-7806	220	12	on	on	ADP
ajst-7806	220	13	three	three	NUM
ajst-7806	220	14	target	target	NOUN
ajst-7806	220	15	detection	detection	NOUN
ajst-7806	220	16	benchmarks	benchmark	NOUN
ajst-7806	220	17	:	:	PUNCT
ajst-7806	220	18	pascal	pascal	ADJ
ajst-7806	220	19	voc2007	voc2007	NOUN
ajst-7806	220	20	and	and	CCONJ
ajst-7806	220	21	pascal	pascal	ADJ
ajst-7806	220	22	voc2012	voc2012	NOUN
ajst-7806	220	23	.	.	PUNCT
ajst-7806	221	1	after	after	ADP
ajst-7806	221	2	removing	remove	VERB
ajst-7806	221	3	these	these	DET
ajst-7806	221	4	bounding	bounding	NOUN
ajst-7806	221	5	box	box	NOUN
ajst-7806	221	6	annotations	annotation	NOUN
ajst-7806	221	7	provided	provide	VERB
ajst-7806	221	8	by	by	ADP
ajst-7806	221	9	the	the	DET
ajst-7806	221	10	data	datum	NOUN
ajst-7806	221	11	set	set	VERB
ajst-7806	221	12	,	,	PUNCT
ajst-7806	221	13	only	only	ADV
ajst-7806	221	14	the	the	DET
ajst-7806	221	15	image	image	NOUN
ajst-7806	221	16	and	and	CCONJ
ajst-7806	221	17	its	its	PRON
ajst-7806	221	18	classification	classification	NOUN
ajst-7806	221	19	label	label	NOUN
ajst-7806	221	20	information	information	NOUN
ajst-7806	221	21	are	be	AUX
ajst-7806	221	22	used	use	VERB
ajst-7806	221	23	for	for	ADP
ajst-7806	221	24	training	training	NOUN
ajst-7806	221	25	.	.	PUNCT
ajst-7806	222	1	two	two	NUM
ajst-7806	222	2	data	data	NOUN
ajst-7806	222	3	sets	set	NOUN
ajst-7806	222	4	like	like	ADP
ajst-7806	222	5	pascal	pascal	ADJ
ajst-7806	222	6	voc2007	voc2007	NOUN
ajst-7806	222	7	and	and	CCONJ
ajst-7806	222	8	pascal	pascal	ADJ
ajst-7806	222	9	voc2012	voc2012	NOUN
ajst-7806	222	10	are	be	AUX
ajst-7806	222	11	the	the	DET
ajst-7806	222	12	most	most	ADV
ajst-7806	222	13	widely	widely	ADV
ajst-7806	222	14	used	use	VERB
ajst-7806	222	15	benchmarks	benchmark	NOUN
ajst-7806	222	16	for	for	ADP
ajst-7806	222	17	weak	weak	ADJ
ajst-7806	222	18	supervised	supervised	ADJ
ajst-7806	222	19	target	target	NOUN
ajst-7806	222	20	detection	detection	NOUN
ajst-7806	222	21	.	.	PUNCT
ajst-7806	223	1	performance	performance	NOUN
ajst-7806	223	2	is	be	AUX
ajst-7806	223	3	measured	measure	VERB
ajst-7806	223	4	by	by	ADP
ajst-7806	223	5	the	the	DET
ajst-7806	223	6	average	average	ADJ
ajst-7806	223	7	accuracy	accuracy	NOUN
ajst-7806	223	8	(	(	PUNCT
ajst-7806	223	9	ap	ap	PROPN
ajst-7806	223	10	)	)	PUNCT
ajst-7806	223	11	of	of	ADP
ajst-7806	223	12	the	the	DET
ajst-7806	223	13	maps	map	NOUN
ajst-7806	223	14	of	of	ADP
ajst-7806	223	15	all	all	DET
ajst-7806	223	16	object	object	NOUN
ajst-7806	223	17	classes	class	NOUN
ajst-7806	223	18	,	,	PUNCT
ajst-7806	223	19	and	and	CCONJ
ajst-7806	223	20	corloc	corloc	NOUN
ajst-7806	223	21	,	,	PUNCT
ajst-7806	223	22	a	a	DET
ajst-7806	223	23	widely	widely	ADV
ajst-7806	223	24	used	use	VERB
ajst-7806	223	25	wsod	wsod	ADJ
ajst-7806	223	26	evaluation	evaluation	NOUN
ajst-7806	223	27	,	,	PUNCT
ajst-7806	223	28	is	be	AUX
ajst-7806	223	29	also	also	ADV
ajst-7806	223	30	reported	report	VERB
ajst-7806	223	31	.	.	PUNCT
ajst-7806	224	1	accuracy	accuracy	NOUN
ajst-7806	224	2	,	,	PUNCT
ajst-7806	224	3	recall	recall	VERB
ajst-7806	224	4	and	and	CCONJ
ajst-7806	224	5	mean	mean	ADJ
ajst-7806	224	6	average	average	ADJ
ajst-7806	224	7	precision	precision	NOUN
ajst-7806	224	8	(	(	PUNCT
ajst-7806	224	9	map	map	NOUN
ajst-7806	224	10	)	)	PUNCT
ajst-7806	224	11	can	can	AUX
ajst-7806	224	12	all	all	PRON
ajst-7806	224	13	be	be	AUX
ajst-7806	224	14	used	use	VERB
ajst-7806	224	15	to	to	PART
ajst-7806	224	16	evaluate	evaluate	VERB
ajst-7806	224	17	the	the	DET
ajst-7806	224	18	performance	performance	NOUN
ajst-7806	224	19	of	of	ADP
ajst-7806	224	20	the	the	DET
ajst-7806	224	21	target	target	NOUN
ajst-7806	224	22	detection	detection	NOUN
ajst-7806	224	23	algorithm	algorithm	NOUN
ajst-7806	224	24	.	.	PUNCT
ajst-7806	225	1	among	among	ADP
ajst-7806	225	2	them	they	PRON
ajst-7806	225	3	,	,	PUNCT
ajst-7806	225	4	the	the	DET
ajst-7806	225	5	map	map	NOUN
ajst-7806	225	6	and	and	CCONJ
ajst-7806	225	7	corloc	corloc	NOUN
ajst-7806	225	8	obtained	obtain	VERB
ajst-7806	225	9	in	in	ADP
ajst-7806	225	10	the	the	DET
ajst-7806	225	11	experiment	experiment	NOUN
ajst-7806	225	12	of	of	ADP
ajst-7806	225	13	this	this	DET
ajst-7806	225	14	paper	paper	NOUN
ajst-7806	225	15	all	all	PRON
ajst-7806	225	16	follow	follow	VERB
ajst-7806	225	17	the	the	DET
ajst-7806	225	18	calculation	calculation	NOUN
ajst-7806	225	19	standard	standard	NOUN
ajst-7806	225	20	stipulated	stipulate	VERB
ajst-7806	225	21	by	by	ADP
ajst-7806	225	22	pascal	pascal	PROPN
ajst-7806	225	23	voc	voc	PROPN
ajst-7806	225	24	,	,	PUNCT
ajst-7806	225	25	that	that	ADV
ajst-7806	225	26	is	is	ADV
ajst-7806	225	27	,	,	PUNCT
ajst-7806	225	28	the	the	DET
ajst-7806	225	29	iou	iou	NOUN
ajst-7806	225	30	between	between	ADP
ajst-7806	225	31	the	the	DET
ajst-7806	225	32	prediction	prediction	NOUN
ajst-7806	225	33	result	result	NOUN
ajst-7806	225	34	frame	frame	NOUN
ajst-7806	225	35	and	and	CCONJ
ajst-7806	225	36	the	the	DET
ajst-7806	225	37	real	real	ADJ
ajst-7806	225	38	frame	frame	NOUN
ajst-7806	225	39	is	be	AUX
ajst-7806	225	40	greater	great	ADJ
ajst-7806	225	41	than	than	ADP
ajst-7806	225	42	0.5	0.5	NUM
ajst-7806	225	43	.	.	PUNCT
ajst-7806	226	1	this	this	DET
ajst-7806	226	2	paper	paper	NOUN
ajst-7806	226	3	generates	generate	VERB
ajst-7806	226	4	region	region	NOUN
ajst-7806	226	5	proposals	proposal	NOUN
ajst-7806	226	6	by	by	ADP
ajst-7806	226	7	combining	combine	VERB
ajst-7806	226	8	a	a	DET
ajst-7806	226	9	selective	selective	ADJ
ajst-7806	226	10	search	search	NOUN
ajst-7806	226	11	algorithm	algorithm	NOUN
ajst-7806	226	12	and	and	CCONJ
ajst-7806	226	13	grad	grad	ADJ
ajst-7806	226	14	cam++	cam++	PROPN
ajst-7806	226	15	,	,	PUNCT
ajst-7806	226	16	and	and	CCONJ
ajst-7806	226	17	the	the	DET
ajst-7806	226	18	proposed	propose	VERB
ajst-7806	226	19	features	feature	NOUN
ajst-7806	226	20	are	be	AUX
ajst-7806	226	21	fed	feed	VERB
ajst-7806	226	22	into	into	ADP
ajst-7806	226	23	a	a	DET
ajst-7806	226	24	modified	modify	VERB
ajst-7806	226	25	cbam	cbam	NOUN
ajst-7806	226	26	attention	attention	NOUN
ajst-7806	226	27	module	module	NOUN
ajst-7806	226	28	.	.	PUNCT
ajst-7806	227	1	this	this	DET
ajst-7806	227	2	paper	paper	NOUN
ajst-7806	227	3	uses	use	VERB
ajst-7806	227	4	the	the	DET
ajst-7806	227	5	vgg16	vgg16	NOUN
ajst-7806	227	6	network	network	NOUN
ajst-7806	227	7	as	as	ADP
ajst-7806	227	8	the	the	DET
ajst-7806	227	9	base	base	NOUN
ajst-7806	227	10	network	network	NOUN
ajst-7806	227	11	,	,	PUNCT
ajst-7806	227	12	and	and	CCONJ
ajst-7806	227	13	uses	use	VERB
ajst-7806	227	14	the	the	DET
ajst-7806	227	15	stochastic	stochastic	ADJ
ajst-7806	227	16	gradient	gradient	ADJ
ajst-7806	227	17	descent	descent	NOUN
ajst-7806	227	18	sgd	sgd	VERB
ajst-7806	227	19	with	with	ADP
ajst-7806	227	20	an	an	DET
ajst-7806	227	21	initial	initial	ADJ
ajst-7806	227	22	learning	learning	NOUN
ajst-7806	227	23	rate	rate	NOUN
ajst-7806	227	24	set	set	VERB
ajst-7806	227	25	to	to	ADP
ajst-7806	227	26	0.001	0.001	NUM
ajst-7806	227	27	,	,	PUNCT
ajst-7806	227	28	weight	weight	NOUN
ajst-7806	227	29	decay	decay	NOUN
ajst-7806	227	30	set	set	VERB
ajst-7806	227	31	to	to	ADP
ajst-7806	227	32	0.0005	0.0005	NUM
ajst-7806	227	33	and	and	CCONJ
ajst-7806	227	34	momentum	momentum	NOUN
ajst-7806	227	35	set	set	VERB
ajst-7806	227	36	to	to	ADP
ajst-7806	227	37	0.9	0.9	NUM
ajst-7806	227	38	.	.	PUNCT
ajst-7806	228	1	on	on	ADP
ajst-7806	228	2	the	the	DET
ajst-7806	228	3	voc2007	voc2007	NOUN
ajst-7806	228	4	dataset	dataset	NOUN
ajst-7806	228	5	,	,	PUNCT
ajst-7806	228	6	the	the	DET
ajst-7806	228	7	total	total	ADJ
ajst-7806	228	8	number	number	NOUN
ajst-7806	228	9	of	of	ADP
ajst-7806	228	10	iteration	iteration	NOUN
ajst-7806	228	11	steps	step	NOUN
ajst-7806	228	12	is	be	AUX
ajst-7806	228	13	set	set	VERB
ajst-7806	228	14	to	to	ADP
ajst-7806	228	15	80,000	80,000	NUM
ajst-7806	228	16	,	,	PUNCT
ajst-7806	228	17	and	and	CCONJ
ajst-7806	228	18	the	the	DET
ajst-7806	228	19	learning	learning	NOUN
ajst-7806	228	20	rate	rate	NOUN
ajst-7806	228	21	is	be	AUX
ajst-7806	228	22	reduced	reduce	VERB
ajst-7806	228	23	to	to	ADP
ajst-7806	228	24	0.0001	0.0001	NUM
ajst-7806	228	25	at	at	ADP
ajst-7806	228	26	the	the	DET
ajst-7806	228	27	40,000th	40,000th	NUM
ajst-7806	228	28	step	step	NOUN
ajst-7806	228	29	.	.	PUNCT
ajst-7806	229	1	dataset	dataset	NOUN
ajst-7806	229	2	,	,	PUNCT
ajst-7806	229	3	we	we	PRON
ajst-7806	229	4	double	double	VERB
ajst-7806	229	5	the	the	DET
ajst-7806	229	6	number	number	NOUN
ajst-7806	229	7	of	of	ADP
ajst-7806	229	8	iteration	iteration	NOUN
ajst-7806	229	9	steps	step	NOUN
ajst-7806	229	10	and	and	CCONJ
ajst-7806	229	11	the	the	DET
ajst-7806	229	12	learning	learn	VERB
ajst-7806	229	13	rate	rate	NOUN
ajst-7806	229	14	decay	decay	NOUN
ajst-7806	229	15	step	step	NOUN
ajst-7806	229	16	to	to	ADP
ajst-7806	229	17	the	the	DET
ajst-7806	229	18	80,000th	80,000th	NUM
ajst-7806	229	19	step	step	NOUN
ajst-7806	229	20	.	.	PUNCT
ajst-7806	230	1	this	this	DET
ajst-7806	230	2	paper	paper	NOUN
ajst-7806	230	3	follows	follow	VERB
ajst-7806	230	4	the	the	DET
ajst-7806	230	5	multi	multi	ADJ
ajst-7806	230	6	-	-	ADJ
ajst-7806	230	7	scale	scale	ADJ
ajst-7806	230	8	settings	setting	NOUN
ajst-7806	230	9	of	of	ADP
ajst-7806	230	10	pcl	pcl	PROPN
ajst-7806	230	11	and	and	CCONJ
ajst-7806	230	12	oicr	oicr	NOUN
ajst-7806	230	13	in	in	ADP
ajst-7806	230	14	training	training	NOUN
ajst-7806	230	15	,	,	PUNCT
ajst-7806	230	16	specifically	specifically	ADV
ajst-7806	230	17	,	,	PUNCT
ajst-7806	230	18	the	the	DET
ajst-7806	230	19	short	short	ADJ
ajst-7806	230	20	edges	edge	NOUN
ajst-7806	230	21	of	of	ADP
ajst-7806	230	22	the	the	DET
ajst-7806	230	23	input	input	NOUN
ajst-7806	230	24	image	image	NOUN
ajst-7806	230	25	are	be	AUX
ajst-7806	230	26	randomly	randomly	ADV
ajst-7806	230	27	rescaled	rescale	VERB
ajst-7806	230	28	to	to	ADP
ajst-7806	230	29	a	a	DET
ajst-7806	230	30	scale	scale	NOUN
ajst-7806	230	31	of	of	ADP
ajst-7806	230	32	{	{	PUNCT
ajst-7806	230	33	480,576,588,864,1280	480,576,588,864,1280	NOUN
ajst-7806	230	34	}	}	PUNCT
ajst-7806	230	35	,	,	PUNCT
ajst-7806	230	36	and	and	CCONJ
ajst-7806	230	37	the	the	DET
ajst-7806	230	38	length	length	NOUN
ajst-7806	230	39	of	of	ADP
ajst-7806	230	40	the	the	DET
ajst-7806	230	41	long	long	ADJ
ajst-7806	230	42	edges	edge	NOUN
ajst-7806	230	43	is	be	AUX
ajst-7806	230	44	restricted	restrict	VERB
ajst-7806	230	45	to	to	ADP
ajst-7806	230	46	no	no	DET
ajst-7806	230	47	more	more	ADJ
ajst-7806	230	48	than	than	ADP
ajst-7806	230	49	2000	2000	NUM
ajst-7806	230	50	.	.	PUNCT
ajst-7806	231	1	4.2	4.2	NUM
ajst-7806	231	2	.	.	PUNCT
ajst-7806	231	3	ablation	ablation	NOUN
ajst-7806	231	4	experiments	experiment	NOUN
ajst-7806	231	5	in	in	ADP
ajst-7806	231	6	order	order	NOUN
ajst-7806	231	7	to	to	PART
ajst-7806	231	8	prove	prove	VERB
ajst-7806	231	9	the	the	DET
ajst-7806	231	10	effectiveness	effectiveness	NOUN
ajst-7806	231	11	of	of	ADP
ajst-7806	231	12	the	the	DET
ajst-7806	231	13	three	three	NUM
ajst-7806	231	14	modules	module	NOUN
ajst-7806	231	15	,	,	PUNCT
ajst-7806	231	16	namely	namely	ADV
ajst-7806	231	17	,	,	PUNCT
ajst-7806	231	18	regional	regional	ADJ
ajst-7806	231	19	suggestion	suggestion	NOUN
ajst-7806	231	20	generation	generation	NOUN
ajst-7806	231	21	(	(	PUNCT
ajst-7806	231	22	pg	pg	NOUN
ajst-7806	231	23	)	)	PUNCT
ajst-7806	231	24	,	,	PUNCT
ajst-7806	231	25	regional	regional	ADJ
ajst-7806	231	26	suggestion	suggestion	NOUN
ajst-7806	231	27	selection	selection	NOUN
ajst-7806	231	28	(	(	PUNCT
ajst-7806	231	29	ps	ps	NOUN
ajst-7806	231	30	)	)	PUNCT
ajst-7806	231	31	and	and	CCONJ
ajst-7806	231	32	cbam	cbam	NOUN
ajst-7806	231	33	module	module	NOUN
ajst-7806	231	34	,	,	PUNCT
ajst-7806	231	35	the	the	DET
ajst-7806	231	36	improved	improve	VERB
ajst-7806	231	37	weakly	weakly	ADJ
ajst-7806	231	38	supervised	supervised	ADJ
ajst-7806	231	39	target	target	NOUN
ajst-7806	231	40	detection	detection	NOUN
ajst-7806	231	41	network	network	NOUN
ajst-7806	231	42	is	be	AUX
ajst-7806	231	43	ablated	ablate	VERB
ajst-7806	231	44	in	in	ADP
ajst-7806	231	45	the	the	DET
ajst-7806	231	46	test	test	NOUN
ajst-7806	231	47	set	set	VERB
ajst-7806	231	48	based	base	VERB
ajst-7806	231	49	on	on	ADP
ajst-7806	231	50	pascal	pascal	ADJ
ajst-7806	231	51	voc2007	voc2007	NOUN
ajst-7806	231	52	data	datum	NOUN
ajst-7806	231	53	set	set	NOUN
ajst-7806	231	54	,	,	PUNCT
ajst-7806	231	55	and	and	CCONJ
ajst-7806	231	56	the	the	DET
ajst-7806	231	57	best	good	ADJ
ajst-7806	231	58	detection	detection	NOUN
ajst-7806	231	59	results	result	NOUN
ajst-7806	231	60	are	be	AUX
ajst-7806	231	61	displayed	display	VERB
ajst-7806	231	62	in	in	ADP
ajst-7806	231	63	bold	bold	ADJ
ajst-7806	231	64	,	,	PUNCT
ajst-7806	231	65	so	so	SCONJ
ajst-7806	231	66	we	we	PRON
ajst-7806	231	67	can	can	AUX
ajst-7806	231	68	see	see	VERB
ajst-7806	231	69	the	the	DET
ajst-7806	231	70	performance	performance	NOUN
ajst-7806	231	71	of	of	ADP
ajst-7806	231	72	the	the	DET
ajst-7806	231	73	three	three	NUM
ajst-7806	231	74	module	module	NOUN
ajst-7806	231	75	methods	method	NOUN
ajst-7806	231	76	introduced	introduce	VERB
ajst-7806	231	77	in	in	ADP
ajst-7806	231	78	this	this	DET
ajst-7806	231	79	chapter	chapter	NOUN
ajst-7806	231	80	on	on	ADP
ajst-7806	231	81	weakly	weakly	ADJ
ajst-7806	231	82	supervised	supervised	ADJ
ajst-7806	231	83	target	target	NOUN
ajst-7806	231	84	detection	detection	NOUN
ajst-7806	231	85	in	in	ADP
ajst-7806	231	86	these	these	DET
ajst-7806	231	87	20	20	NUM
ajst-7806	231	88	categories	category	NOUN
ajst-7806	231	89	,	,	PUNCT
ajst-7806	231	90	as	as	SCONJ
ajst-7806	231	91	shown	show	VERB
ajst-7806	231	92	in	in	ADP
ajst-7806	231	93	table	table	NOUN
ajst-7806	231	94	1	1	NUM
ajst-7806	231	95	:	:	PUNCT
ajst-7806	231	96	table	table	NOUN
ajst-7806	231	97	1	1	NUM
ajst-7806	231	98	.	.	PUNCT
ajst-7806	231	99	performance	performance	NOUN
ajst-7806	231	100	of	of	ADP
ajst-7806	231	101	different	different	ADJ
ajst-7806	231	102	methods	method	NOUN
ajst-7806	231	103	and	and	CCONJ
ajst-7806	231	104	modules	module	NOUN
ajst-7806	231	105	on	on	ADP
ajst-7806	231	106	20	20	NUM
ajst-7806	231	107	classes	class	NOUN
ajst-7806	231	108	of	of	ADP
ajst-7806	231	109	voc	voc	NOUN
ajst-7806	231	110	2007	2007	NUM
ajst-7806	231	111	test	test	NOUN
ajst-7806	231	112	data	datum	NOUN
ajst-7806	231	113	set	set	VERB
ajst-7806	231	114	method	method	NOUN
ajst-7806	231	115	mil	mil	PROPN
ajst-7806	231	116	mil+cbam	mil+cbam	PROPN
ajst-7806	231	117	mil+pg	mil+pg	PROPN
ajst-7806	231	118	-	-	PUNCT
ajst-7806	231	119	ps	ps	NOUN
ajst-7806	231	120	mil+cbam+pg	mil+cbam+pg	NOUN
ajst-7806	231	121	-	-	NOUN
ajst-7806	231	122	ps	ps	ADJ
ajst-7806	231	123	aero	aero	PROPN
ajst-7806	231	124	56.2	56.2	NUM
ajst-7806	231	125	55.2	55.2	NUM
ajst-7806	231	126	60.3	60.3	NUM
ajst-7806	231	127	66.0	66.0	NUM
ajst-7806	231	128	bicycle	bicycle	NOUN
ajst-7806	231	129	62.1	62.1	NUM
ajst-7806	231	130	62.5	62.5	NUM
ajst-7806	231	131	61.4	61.4	NUM
ajst-7806	231	132	65.2	65.2	NUM
ajst-7806	231	133	bird	bird	NOUN
ajst-7806	231	134	boat	boat	NOUN
ajst-7806	231	135	bottle	bottle	NOUN
ajst-7806	231	136	bus	bus	NOUN
ajst-7806	231	137	car	car	NOUN
ajst-7806	231	138	cat	cat	NOUN
ajst-7806	231	139	chair	chair	NOUN
ajst-7806	231	140	cow	cow	NOUN
ajst-7806	231	141	table	table	NOUN
ajst-7806	231	142	dog	dog	NOUN
ajst-7806	231	143	horse	horse	NOUN
ajst-7806	231	144	mbike	mbike	ADJ
ajst-7806	231	145	person	person	NOUN
ajst-7806	231	146	plant	plant	NOUN
ajst-7806	231	147	sheep	sheep	NOUN
ajst-7806	231	148	sofa	sofa	NOUN
ajst-7806	231	149	train	train	NOUN
ajst-7806	231	150	tv	tv	NOUN
ajst-7806	231	151	39.4	39.4	NUM
ajst-7806	231	152	21.8	21.8	NUM
ajst-7806	231	153	10.3	10.3	NUM
ajst-7806	231	154	63.6	63.6	NUM
ajst-7806	231	155	60.6	60.6	NUM
ajst-7806	231	156	31.8	31.8	NUM
ajst-7806	231	157	24.8	24.8	NUM
ajst-7806	231	158	45.9	45.9	NUM
ajst-7806	231	159	35.3	35.3	NUM
ajst-7806	231	160	24.1	24.1	NUM
ajst-7806	231	161	36.7	36.7	NUM
ajst-7806	231	162	63.3	63.3	NUM
ajst-7806	231	163	13.1	13.1	NUM
ajst-7806	231	164	23.1	23.1	NUM
ajst-7806	231	165	39.4	39.4	NUM
ajst-7806	231	166	49.1	49.1	NUM
ajst-7806	231	167	64.7	64.7	NUM
ajst-7806	231	168	60.3	60.3	NUM
ajst-7806	231	169	43.0	43.0	NUM
ajst-7806	231	170	22.1	22.1	NUM
ajst-7806	231	171	12.7	12.7	NUM
ajst-7806	231	172	66.1	66.1	NUM
ajst-7806	231	173	62.0	62.0	NUM
ajst-7806	231	174	38.2	38.2	NUM
ajst-7806	231	175	26.3	26.3	NUM
ajst-7806	231	176	48.9	48.9	NUM
ajst-7806	231	177	37.7	37.7	NUM
ajst-7806	231	178	26.1	26.1	NUM
ajst-7806	231	179	45.5	45.5	NUM
ajst-7806	231	180	64.3	64.3	NUM
ajst-7806	231	181	12.4	12.4	NUM
ajst-7806	231	182	24.6	24.6	NUM
ajst-7806	231	183	42.1	42.1	NUM
ajst-7806	231	184	46.6	46.6	NUM
ajst-7806	231	185	65.8	65.8	NUM
ajst-7806	231	186	62.3	62.3	NUM
ajst-7806	231	187	47.1	47.1	NUM
ajst-7806	231	188	24.5	24.5	NUM
ajst-7806	231	189	14.1	14.1	NUM
ajst-7806	231	190	67.6	67.6	NUM
ajst-7806	231	191	63.0	63.0	NUM
ajst-7806	231	192	68.1	68.1	NUM
ajst-7806	231	193	22.8	22.8	NUM
ajst-7806	231	194	52.9	52.9	NUM
ajst-7806	231	195	40.2	40.2	NUM
ajst-7806	231	196	59.7	59.7	NUM
ajst-7806	231	197	62.9	62.9	NUM
ajst-7806	231	198	61.2	61.2	NUM
ajst-7806	231	199	10.1	10.1	NUM
ajst-7806	231	200	22.3	22.3	NUM
ajst-7806	231	201	45.1	45.1	NUM
ajst-7806	231	202	50.8	50.8	NUM
ajst-7806	231	203	69.2	69.2	NUM
ajst-7806	231	204	64.4	64.4	NUM
ajst-7806	231	205	58.3	58.3	NUM
ajst-7806	231	206	39.1	39.1	NUM
ajst-7806	231	207	22.3	22.3	NUM
ajst-7806	231	208	66.7	66.7	NUM
ajst-7806	231	209	68.9	68.9	NUM
ajst-7806	231	210	63.4	63.4	NUM
ajst-7806	231	211	31.7	31.7	NUM
ajst-7806	231	212	67.8	67.8	NUM
ajst-7806	231	213	43.3	43.3	NUM
ajst-7806	231	214	62.2	62.2	NUM
ajst-7806	231	215	72.0	72.0	NUM
ajst-7806	231	216	69.5	69.5	NUM
ajst-7806	231	217	20.4	20.4	NUM
ajst-7806	231	218	27.8	27.8	NUM
ajst-7806	231	219	59.1	59.1	NUM
ajst-7806	231	220	59.8	59.8	NUM
ajst-7806	231	221	52.6	52.6	NUM
ajst-7806	231	222	64.9	64.9	NUM
ajst-7806	231	223	it	it	PRON
ajst-7806	231	224	can	can	AUX
ajst-7806	231	225	be	be	AUX
ajst-7806	231	226	clearly	clearly	ADV
ajst-7806	231	227	seen	see	VERB
ajst-7806	231	228	from	from	ADP
ajst-7806	231	229	table	table	NOUN
ajst-7806	231	230	1	1	NUM
ajst-7806	231	231	that	that	SCONJ
ajst-7806	231	232	in	in	ADP
ajst-7806	231	233	the	the	DET
ajst-7806	231	234	weak	weak	ADJ
ajst-7806	231	235	supervised	supervised	ADJ
ajst-7806	231	236	object	object	NOUN
ajst-7806	231	237	detection	detection	NOUN
ajst-7806	231	238	algorithm	algorithm	NOUN
ajst-7806	231	239	model	model	NOUN
ajst-7806	231	240	proposed	propose	VERB
ajst-7806	231	241	in	in	ADP
ajst-7806	231	242	this	this	DET
ajst-7806	231	243	chapter	chapter	NOUN
ajst-7806	231	244	,	,	PUNCT
ajst-7806	231	245	the	the	DET
ajst-7806	231	246	addition	addition	NOUN
ajst-7806	231	247	of	of	ADP
ajst-7806	231	248	each	each	DET
ajst-7806	231	249	sub	sub	NOUN
ajst-7806	231	250	-	-	NOUN
ajst-7806	231	251	module	module	NOUN
ajst-7806	231	252	improves	improve	VERB
ajst-7806	231	253	the	the	DET
ajst-7806	231	254	performance	performance	NOUN
ajst-7806	231	255	of	of	ADP
ajst-7806	231	256	the	the	DET
ajst-7806	231	257	model	model	NOUN
ajst-7806	231	258	to	to	ADP
ajst-7806	231	259	a	a	DET
ajst-7806	231	260	certain	certain	ADJ
ajst-7806	231	261	extent	extent	NOUN
ajst-7806	231	262	.	.	PUNCT
ajst-7806	232	1	in	in	ADP
ajst-7806	232	2	the	the	DET
ajst-7806	232	3	mil	mil	NOUN
ajst-7806	232	4	baseline	baseline	NOUN
ajst-7806	232	5	,	,	PUNCT
ajst-7806	232	6	after	after	ADP
ajst-7806	232	7	adding	add	VERB
ajst-7806	232	8	pg	pg	PRON
ajst-7806	232	9	and	and	CCONJ
ajst-7806	232	10	ps	ps	PROPN
ajst-7806	232	11	modules	module	NOUN
ajst-7806	232	12	,	,	PUNCT
ajst-7806	232	13	the	the	DET
ajst-7806	232	14	model	model	NOUN
ajst-7806	232	15	has	have	AUX
ajst-7806	232	16	been	be	AUX
ajst-7806	232	17	improved	improve	VERB
ajst-7806	232	18	obviously	obviously	ADV
ajst-7806	232	19	,	,	PUNCT
ajst-7806	232	20	and	and	CCONJ
ajst-7806	232	21	after	after	ADP
ajst-7806	232	22	adding	add	VERB
ajst-7806	232	23	the	the	DET
ajst-7806	232	24	improved	improved	ADJ
ajst-7806	232	25	cbam	cbam	NOUN
ajst-7806	232	26	145	145	NUM
ajst-7806	232	27	module	module	NOUN
ajst-7806	232	28	on	on	ADP
ajst-7806	232	29	this	this	DET
ajst-7806	232	30	basis	basis	NOUN
ajst-7806	232	31	,	,	PUNCT
ajst-7806	232	32	the	the	DET
ajst-7806	232	33	performance	performance	NOUN
ajst-7806	232	34	has	have	AUX
ajst-7806	232	35	been	be	AUX
ajst-7806	232	36	improved	improve	VERB
ajst-7806	232	37	relatively	relatively	ADV
ajst-7806	232	38	obviously	obviously	ADV
ajst-7806	232	39	,	,	PUNCT
ajst-7806	232	40	which	which	PRON
ajst-7806	232	41	shows	show	VERB
ajst-7806	232	42	that	that	SCONJ
ajst-7806	232	43	it	it	PRON
ajst-7806	232	44	is	be	AUX
ajst-7806	232	45	useful	useful	ADJ
ajst-7806	232	46	for	for	ADP
ajst-7806	232	47	improving	improve	VERB
ajst-7806	232	48	cbam	cbam	NOUN
ajst-7806	232	49	.	.	PUNCT
ajst-7806	233	1	as	as	SCONJ
ajst-7806	233	2	shown	show	VERB
ajst-7806	233	3	in	in	ADP
ajst-7806	233	4	table	table	NOUN
ajst-7806	233	5	2	2	NUM
ajst-7806	233	6	,	,	PUNCT
ajst-7806	233	7	the	the	DET
ajst-7806	233	8	first	first	ADJ
ajst-7806	233	9	column	column	NOUN
ajst-7806	233	10	represents	represent	VERB
ajst-7806	233	11	the	the	DET
ajst-7806	233	12	final	final	ADJ
ajst-7806	233	13	effect	effect	NOUN
ajst-7806	233	14	of	of	ADP
ajst-7806	233	15	the	the	DET
ajst-7806	233	16	image	image	NOUN
ajst-7806	233	17	directly	directly	ADV
ajst-7806	233	18	generated	generate	VERB
ajst-7806	233	19	by	by	ADP
ajst-7806	233	20	the	the	DET
ajst-7806	233	21	selective	selective	ADJ
ajst-7806	233	22	search	search	NOUN
ajst-7806	233	23	algorithm	algorithm	NOUN
ajst-7806	233	24	and	and	CCONJ
ajst-7806	233	25	sent	send	VERB
ajst-7806	233	26	to	to	ADP
ajst-7806	233	27	the	the	DET
ajst-7806	233	28	basic	basic	ADJ
ajst-7806	233	29	multi	multi	ADJ
ajst-7806	233	30	-	-	ADJ
ajst-7806	233	31	instance	instance	NOUN
ajst-7806	233	32	detector	detector	NOUN
ajst-7806	233	33	for	for	ADP
ajst-7806	233	34	multi	multi	ADJ
ajst-7806	233	35	-	-	ADJ
ajst-7806	233	36	instance	instance	NOUN
ajst-7806	233	37	detection	detection	NOUN
ajst-7806	233	38	;	;	PUNCT
ajst-7806	233	39	the	the	DET
ajst-7806	233	40	second	second	ADJ
ajst-7806	233	41	column	column	NOUN
ajst-7806	233	42	represents	represent	VERB
ajst-7806	233	43	the	the	DET
ajst-7806	233	44	introduction	introduction	NOUN
ajst-7806	233	45	of	of	ADP
ajst-7806	233	46	pg(proposal	pg(proposal	ADJ
ajst-7806	233	47	generation	generation	NOUN
ajst-7806	233	48	)	)	PUNCT
ajst-7806	233	49	and	and	CCONJ
ajst-7806	233	50	ps(proposals	ps(proposal	NOUN
ajst-7806	233	51	selection	selection	NOUN
ajst-7806	233	52	)	)	PUNCT
ajst-7806	233	53	modules	module	NOUN
ajst-7806	233	54	on	on	ADP
ajst-7806	233	55	the	the	DET
ajst-7806	233	56	basis	basis	NOUN
ajst-7806	233	57	of	of	ADP
ajst-7806	233	58	mil	mil	NOUN
ajst-7806	233	59	detector	detector	NOUN
ajst-7806	233	60	;	;	PUNCT
ajst-7806	233	61	the	the	DET
ajst-7806	233	62	third	third	ADJ
ajst-7806	233	63	column	column	NOUN
ajst-7806	233	64	represents	represent	VERB
ajst-7806	233	65	that	that	SCONJ
ajst-7806	233	66	an	an	DET
ajst-7806	233	67	improved	improved	ADJ
ajst-7806	233	68	cbam	cbam	NOUN
ajst-7806	233	69	module	module	NOUN
ajst-7806	233	70	is	be	AUX
ajst-7806	233	71	added	add	VERB
ajst-7806	233	72	after	after	SCONJ
ajst-7806	233	73	the	the	DET
ajst-7806	233	74	candidate	candidate	NOUN
ajst-7806	233	75	box	box	PROPN
ajst-7806	233	76	is	be	AUX
ajst-7806	233	77	generated	generate	VERB
ajst-7806	233	78	to	to	PART
ajst-7806	233	79	enhance	enhance	VERB
ajst-7806	233	80	the	the	DET
ajst-7806	233	81	feature	feature	NOUN
ajst-7806	233	82	map	map	NOUN
ajst-7806	233	83	.	.	PUNCT
ajst-7806	234	1	the	the	DET
ajst-7806	234	2	third	third	ADJ
ajst-7806	234	3	column	column	NOUN
ajst-7806	234	4	represents	represent	VERB
ajst-7806	234	5	adding	add	VERB
ajst-7806	234	6	pg	pg	PROPN
ajst-7806	234	7	-	-	PUNCT
ajst-7806	234	8	ps	ps	NOUN
ajst-7806	234	9	module	module	NOUN
ajst-7806	234	10	and	and	CCONJ
ajst-7806	234	11	cbam	cbam	NOUN
ajst-7806	234	12	module	module	NOUN
ajst-7806	234	13	on	on	ADP
ajst-7806	234	14	the	the	DET
ajst-7806	234	15	basis	basis	NOUN
ajst-7806	234	16	of	of	ADP
ajst-7806	234	17	mil	mil	NOUN
ajst-7806	234	18	detector	detector	NOUN
ajst-7806	234	19	,	,	PUNCT
ajst-7806	234	20	which	which	PRON
ajst-7806	234	21	is	be	AUX
ajst-7806	234	22	the	the	DET
ajst-7806	234	23	improved	improved	ADJ
ajst-7806	234	24	method	method	NOUN
ajst-7806	234	25	proposed	propose	VERB
ajst-7806	234	26	in	in	ADP
ajst-7806	234	27	this	this	DET
ajst-7806	234	28	chapter	chapter	NOUN
ajst-7806	234	29	,	,	PUNCT
ajst-7806	234	30	and	and	CCONJ
ajst-7806	234	31	it	it	PRON
ajst-7806	234	32	can	can	AUX
ajst-7806	234	33	be	be	AUX
ajst-7806	234	34	seen	see	VERB
ajst-7806	234	35	that	that	SCONJ
ajst-7806	234	36	it	it	PRON
ajst-7806	234	37	is	be	AUX
ajst-7806	234	38	obviously	obviously	ADV
ajst-7806	234	39	improved	improve	VERB
ajst-7806	234	40	compared	compare	VERB
ajst-7806	234	41	with	with	ADP
ajst-7806	234	42	the	the	DET
ajst-7806	234	43	previous	previous	ADJ
ajst-7806	234	44	method	method	NOUN
ajst-7806	234	45	.	.	PUNCT
ajst-7806	235	1	table	table	NOUN
ajst-7806	235	2	2	2	NUM
ajst-7806	235	3	.	.	X
ajst-7806	235	4	map	map	NOUN
ajst-7806	235	5	of	of	ADP
ajst-7806	235	6	different	different	ADJ
ajst-7806	235	7	method	method	NOUN
ajst-7806	235	8	modules	module	NOUN
ajst-7806	235	9	in	in	ADP
ajst-7806	235	10	voc	voc	PROPN
ajst-7806	235	11	2007	2007	NUM
ajst-7806	235	12	test	test	NOUN
ajst-7806	235	13	data	datum	NOUN
ajst-7806	235	14	set	set	VERB
ajst-7806	235	15	mil+cbam	mil+cbam	PROPN
ajst-7806	235	16	mil+pg	mil+pg	PROPN
ajst-7806	235	17	-	-	PUNCT
ajst-7806	235	18	ps	ps	NOUN
ajst-7806	235	19	mil+cbam+pg	mil+cbam+pg	NOUN
ajst-7806	235	20	-	-	NOUN
ajst-7806	235	21	ps	ps	NOUN
ajst-7806	235	22	map(%	map(%	NOUN
ajst-7806	235	23	)	)	PUNCT
ajst-7806	235	24	47.0	47.0	NUM
ajst-7806	236	1			PROPN
ajst-7806	236	2			PROPN
ajst-7806	236	3			NUM
ajst-7806	236	4	49.2	49.2	NUM
ajst-7806	236	5	51.1	51.1	NUM
ajst-7806	236	6	54.0	54.0	NUM
ajst-7806	236	7	from	from	ADP
ajst-7806	236	8	table	table	NOUN
ajst-7806	236	9	2	2	NUM
ajst-7806	236	10	,	,	PUNCT
ajst-7806	236	11	we	we	PRON
ajst-7806	236	12	can	can	AUX
ajst-7806	236	13	clearly	clearly	ADV
ajst-7806	236	14	see	see	VERB
ajst-7806	236	15	that	that	SCONJ
ajst-7806	236	16	the	the	DET
ajst-7806	236	17	addition	addition	NOUN
ajst-7806	236	18	of	of	ADP
ajst-7806	236	19	each	each	DET
ajst-7806	236	20	sub	sub	NOUN
ajst-7806	236	21	-	-	NOUN
ajst-7806	236	22	module	module	NOUN
ajst-7806	236	23	in	in	ADP
ajst-7806	236	24	the	the	DET
ajst-7806	236	25	weak	weak	ADJ
ajst-7806	236	26	supervised	supervised	ADJ
ajst-7806	236	27	object	object	NOUN
ajst-7806	236	28	detection	detection	NOUN
ajst-7806	236	29	algorithm	algorithm	NOUN
ajst-7806	236	30	model	model	NOUN
ajst-7806	236	31	we	we	PRON
ajst-7806	236	32	proposed	propose	VERB
ajst-7806	236	33	improves	improve	VERB
ajst-7806	236	34	the	the	DET
ajst-7806	236	35	performance	performance	NOUN
ajst-7806	236	36	of	of	ADP
ajst-7806	236	37	the	the	DET
ajst-7806	236	38	model	model	NOUN
ajst-7806	236	39	to	to	ADP
ajst-7806	236	40	a	a	DET
ajst-7806	236	41	certain	certain	ADJ
ajst-7806	236	42	extent	extent	NOUN
ajst-7806	236	43	.	.	PUNCT
ajst-7806	237	1	we	we	PRON
ajst-7806	237	2	can	can	AUX
ajst-7806	237	3	find	find	VERB
ajst-7806	237	4	that	that	SCONJ
ajst-7806	237	5	in	in	ADP
ajst-7806	237	6	the	the	DET
ajst-7806	237	7	oicr	oicr	NOUN
ajst-7806	237	8	network	network	PROPN
ajst-7806	237	9	baseline	baseline	PROPN
ajst-7806	237	10	,	,	PUNCT
ajst-7806	237	11	the	the	DET
ajst-7806	237	12	model	model	NOUN
ajst-7806	237	13	has	have	AUX
ajst-7806	237	14	been	be	AUX
ajst-7806	237	15	significantly	significantly	ADV
ajst-7806	237	16	improved	improve	VERB
ajst-7806	237	17	after	after	ADP
ajst-7806	237	18	adding	add	VERB
ajst-7806	237	19	pg	pg	PRON
ajst-7806	237	20	and	and	CCONJ
ajst-7806	237	21	ps	ps	VERB
ajst-7806	237	22	modules	module	NOUN
ajst-7806	237	23	,	,	PUNCT
ajst-7806	237	24	and	and	CCONJ
ajst-7806	237	25	on	on	ADP
ajst-7806	237	26	this	this	DET
ajst-7806	237	27	basis	basis	NOUN
ajst-7806	237	28	,	,	PUNCT
ajst-7806	237	29	the	the	DET
ajst-7806	237	30	performance	performance	NOUN
ajst-7806	237	31	has	have	AUX
ajst-7806	237	32	also	also	ADV
ajst-7806	237	33	been	be	AUX
ajst-7806	237	34	significantly	significantly	ADV
ajst-7806	237	35	improved	improve	VERB
ajst-7806	237	36	after	after	ADP
ajst-7806	237	37	adding	add	VERB
ajst-7806	237	38	the	the	DET
ajst-7806	237	39	improved	improved	ADJ
ajst-7806	237	40	cbam	cbam	NOUN
ajst-7806	237	41	module	module	NOUN
ajst-7806	237	42	,	,	PUNCT
ajst-7806	237	43	indicating	indicate	VERB
ajst-7806	237	44	that	that	SCONJ
ajst-7806	237	45	we	we	PRON
ajst-7806	237	46	have	have	AUX
ajst-7806	237	47	played	play	VERB
ajst-7806	237	48	a	a	DET
ajst-7806	237	49	role	role	NOUN
ajst-7806	237	50	in	in	ADP
ajst-7806	237	51	the	the	DET
ajst-7806	237	52	improvement	improvement	NOUN
ajst-7806	237	53	of	of	ADP
ajst-7806	237	54	cbam	cbam	NOUN
ajst-7806	237	55	.	.	PUNCT
ajst-7806	238	1	4.3	4.3	NUM
ajst-7806	238	2	.	.	PUNCT
ajst-7806	238	3	comparison	comparison	NOUN
ajst-7806	238	4	with	with	ADP
ajst-7806	238	5	other	other	ADJ
ajst-7806	238	6	methods	method	NOUN
ajst-7806	238	7	table	table	NOUN
ajst-7806	238	8	3	3	NUM
ajst-7806	238	9	and	and	CCONJ
ajst-7806	238	10	table	table	NOUN
ajst-7806	238	11	4	4	NUM
ajst-7806	238	12	respectively	respectively	ADV
ajst-7806	238	13	show	show	VERB
ajst-7806	238	14	the	the	DET
ajst-7806	238	15	detection	detection	NOUN
ajst-7806	238	16	performance	performance	NOUN
ajst-7806	238	17	and	and	CCONJ
ajst-7806	238	18	positioning	positioning	NOUN
ajst-7806	238	19	performance	performance	NOUN
ajst-7806	238	20	of	of	ADP
ajst-7806	238	21	the	the	DET
ajst-7806	238	22	weakly	weakly	ADJ
ajst-7806	238	23	supervised	supervised	ADJ
ajst-7806	238	24	target	target	NOUN
ajst-7806	238	25	detection	detection	NOUN
ajst-7806	238	26	model	model	NOUN
ajst-7806	238	27	proposed	propose	VERB
ajst-7806	238	28	in	in	ADP
ajst-7806	238	29	this	this	DET
ajst-7806	238	30	paper	paper	NOUN
ajst-7806	238	31	and	and	CCONJ
ajst-7806	238	32	other	other	ADJ
ajst-7806	238	33	weakly	weakly	ADJ
ajst-7806	238	34	supervised	supervised	ADJ
ajst-7806	238	35	target	target	NOUN
ajst-7806	238	36	detection	detection	NOUN
ajst-7806	238	37	models	model	NOUN
ajst-7806	238	38	in	in	ADP
ajst-7806	238	39	20	20	NUM
ajst-7806	238	40	categories	category	NOUN
ajst-7806	238	41	of	of	ADP
ajst-7806	238	42	voc	voc	NOUN
ajst-7806	238	43	2007	2007	NUM
ajst-7806	238	44	data	datum	NOUN
ajst-7806	238	45	set	set	VERB
ajst-7806	238	46	.	.	PUNCT
ajst-7806	239	1	the	the	DET
ajst-7806	239	2	bold	bold	ADJ
ajst-7806	239	3	mark	mark	NOUN
ajst-7806	239	4	is	be	AUX
ajst-7806	239	5	the	the	DET
ajst-7806	239	6	highest	high	ADJ
ajst-7806	239	7	accuracy	accuracy	NOUN
ajst-7806	239	8	in	in	ADP
ajst-7806	239	9	this	this	DET
ajst-7806	239	10	category	category	NOUN
ajst-7806	239	11	.	.	PUNCT
ajst-7806	240	1	we	we	PRON
ajst-7806	240	2	can	can	AUX
ajst-7806	240	3	see	see	VERB
ajst-7806	240	4	from	from	ADP
ajst-7806	240	5	the	the	DET
ajst-7806	240	6	table	table	NOUN
ajst-7806	240	7	that	that	PRON
ajst-7806	240	8	our	our	PRON
ajst-7806	240	9	model	model	NOUN
ajst-7806	240	10	has	have	AUX
ajst-7806	240	11	achieved	achieve	VERB
ajst-7806	240	12	the	the	DET
ajst-7806	240	13	highest	high	ADJ
ajst-7806	240	14	accuracy	accuracy	NOUN
ajst-7806	240	15	in	in	ADP
ajst-7806	240	16	11	11	NUM
ajst-7806	240	17	categories	category	NOUN
ajst-7806	240	18	of	of	ADP
ajst-7806	240	19	aircraft	aircraft	NOUN
ajst-7806	240	20	,	,	PUNCT
ajst-7806	240	21	birds	bird	NOUN
ajst-7806	240	22	,	,	PUNCT
ajst-7806	240	23	cars	car	NOUN
ajst-7806	240	24	,	,	PUNCT
ajst-7806	240	25	cats	cat	NOUN
ajst-7806	240	26	,	,	PUNCT
ajst-7806	240	27	chairs	chair	NOUN
ajst-7806	240	28	,	,	PUNCT
ajst-7806	240	29	cows	cow	NOUN
ajst-7806	240	30	,	,	PUNCT
ajst-7806	240	31	dogs	dog	NOUN
ajst-7806	240	32	,	,	PUNCT
ajst-7806	240	33	horses	horse	NOUN
ajst-7806	240	34	,	,	PUNCT
ajst-7806	240	35	sheep	sheep	NOUN
ajst-7806	240	36	,	,	PUNCT
ajst-7806	240	37	sofas	sofas	NOUN
ajst-7806	240	38	and	and	CCONJ
ajst-7806	240	39	tvs	tv	NOUN
ajst-7806	240	40	,	,	PUNCT
ajst-7806	240	41	and	and	CCONJ
ajst-7806	240	42	the	the	DET
ajst-7806	240	43	highest	high	ADJ
ajst-7806	240	44	positioning	positioning	NOUN
ajst-7806	240	45	accuracy	accuracy	NOUN
ajst-7806	240	46	in	in	ADP
ajst-7806	240	47	7	7	NUM
ajst-7806	240	48	categories	category	NOUN
ajst-7806	240	49	of	of	ADP
ajst-7806	240	50	birds	bird	NOUN
ajst-7806	240	51	,	,	PUNCT
ajst-7806	240	52	cats	cat	NOUN
ajst-7806	240	53	,	,	PUNCT
ajst-7806	240	54	chairs	chair	NOUN
ajst-7806	240	55	,	,	PUNCT
ajst-7806	240	56	cows	cow	NOUN
ajst-7806	240	57	,	,	PUNCT
ajst-7806	240	58	dogs	dog	NOUN
ajst-7806	240	59	,	,	PUNCT
ajst-7806	240	60	horses	horse	NOUN
ajst-7806	240	61	and	and	CCONJ
ajst-7806	240	62	sheep	sheep	NOUN
ajst-7806	240	63	,	,	PUNCT
ajst-7806	240	64	significantly	significantly	ADV
ajst-7806	240	65	improving	improve	VERB
ajst-7806	240	66	the	the	DET
ajst-7806	240	67	local	local	ADJ
ajst-7806	240	68	positioning	positioning	NOUN
ajst-7806	240	69	problem	problem	NOUN
ajst-7806	240	70	that	that	PRON
ajst-7806	240	71	is	be	AUX
ajst-7806	240	72	very	very	ADV
ajst-7806	240	73	prone	prone	ADJ
ajst-7806	240	74	to	to	PART
ajst-7806	240	75	occur	occur	VERB
ajst-7806	240	76	in	in	ADP
ajst-7806	240	77	animal	animal	NOUN
ajst-7806	240	78	categories	category	NOUN
ajst-7806	240	79	.	.	PUNCT
ajst-7806	241	1	that	that	PRON
ajst-7806	241	2	is	be	AUX
ajst-7806	241	3	,	,	PUNCT
ajst-7806	241	4	only	only	ADV
ajst-7806	241	5	the	the	DET
ajst-7806	241	6	head	head	NOUN
ajst-7806	241	7	of	of	ADP
ajst-7806	241	8	the	the	DET
ajst-7806	241	9	animal	animal	NOUN
ajst-7806	241	10	was	be	AUX
ajst-7806	241	11	detected	detect	VERB
ajst-7806	241	12	,	,	PUNCT
ajst-7806	241	13	and	and	CCONJ
ajst-7806	241	14	the	the	DET
ajst-7806	241	15	whole	whole	ADJ
ajst-7806	241	16	animal	animal	NOUN
ajst-7806	241	17	was	be	AUX
ajst-7806	241	18	ignored	ignore	VERB
ajst-7806	241	19	.	.	PUNCT
ajst-7806	242	1	table	table	NOUN
ajst-7806	242	2	3	3	NUM
ajst-7806	242	3	.	.	PUNCT
ajst-7806	242	4	comparison	comparison	NOUN
ajst-7806	242	5	of	of	ADP
ajst-7806	242	6	detection	detection	NOUN
ajst-7806	242	7	performance	performance	NOUN
ajst-7806	242	8	of	of	ADP
ajst-7806	242	9	each	each	DET
ajst-7806	242	10	category	category	NOUN
ajst-7806	242	11	method	method	VERB
ajst-7806	242	12	wsddn	wsddn	ADJ
ajst-7806	242	13	context	context	NOUN
ajst-7806	242	14	-	-	PUNCT
ajst-7806	242	15	locnet	locnet	NOUN
ajst-7806	242	16	oicr	oicr	NOUN
ajst-7806	242	17	pcl	pcl	PROPN
ajst-7806	242	18	c	c	PROPN
ajst-7806	242	19	-	-	PUNCT
ajst-7806	242	20	wsl	wsl	PROPN
ajst-7806	242	21	wscdn	wscdn	PROPN
ajst-7806	242	22	wsod2	wsod2	VERB
ajst-7806	242	23	ours	ours	PRON
ajst-7806	242	24	aero	aero	PROPN
ajst-7806	242	25	39.4	39.4	NUM
ajst-7806	242	26	57.1	57.1	NUM
ajst-7806	242	27	58.0	58.0	NUM
ajst-7806	242	28	54.4	54.4	NUM
ajst-7806	242	29	62.9	62.9	NUM
ajst-7806	242	30	61.2	61.2	NUM
ajst-7806	242	31	65.1	65.1	NUM
ajst-7806	242	32	66.0	66.0	NUM
ajst-7806	242	33	bicycle	bicycle	NOUN
ajst-7806	242	34	50.1	50.1	NUM
ajst-7806	242	35	52.0	52.0	NUM
ajst-7806	242	36	62.4	62.4	NUM
ajst-7806	242	37	69.0	69.0	NUM
ajst-7806	242	38	64.8	64.8	NUM
ajst-7806	242	39	66.6	66.6	NUM
ajst-7806	242	40	64.8	64.8	NUM
ajst-7806	242	41	65.2	65.2	NUM
ajst-7806	242	42	bird	bird	NOUN
ajst-7806	242	43	boat	boat	NOUN
ajst-7806	242	44	bottle	bottle	NOUN
ajst-7806	242	45	bus	bus	NOUN
ajst-7806	242	46	car	car	NOUN
ajst-7806	242	47	cat	cat	NOUN
ajst-7806	242	48	chair	chair	NOUN
ajst-7806	242	49	cow	cow	NOUN
ajst-7806	242	50	table	table	NOUN
ajst-7806	242	51	dog	dog	NOUN
ajst-7806	242	52	horse	horse	NOUN
ajst-7806	242	53	mbike	mbike	ADJ
ajst-7806	242	54	person	person	NOUN
ajst-7806	242	55	plant	plant	NOUN
ajst-7806	242	56	sheep	sheep	NOUN
ajst-7806	242	57	sofa	sofa	NOUN
ajst-7806	242	58	train	train	NOUN
ajst-7806	242	59	tv	tv	NOUN
ajst-7806	242	60	map	map	NOUN
ajst-7806	242	61	31.5	31.5	NUM
ajst-7806	242	62	16.3	16.3	NUM
ajst-7806	242	63	12.6	12.6	NUM
ajst-7806	242	64	64.5	64.5	NUM
ajst-7806	242	65	42.8	42.8	NUM
ajst-7806	242	66	42.6	42.6	NUM
ajst-7806	242	67	10.1	10.1	NUM
ajst-7806	242	68	35.7	35.7	NUM
ajst-7806	242	69	24.9	24.9	NUM
ajst-7806	242	70	38.2	38.2	NUM
ajst-7806	242	71	34.4	34.4	NUM
ajst-7806	242	72	55.6	55.6	NUM
ajst-7806	242	73	9.4	9.4	NUM
ajst-7806	242	74	14.7	14.7	NUM
ajst-7806	242	75	30.2	30.2	NUM
ajst-7806	242	76	40.7	40.7	NUM
ajst-7806	242	77	54.7	54.7	NUM
ajst-7806	242	78	46.9	46.9	NUM
ajst-7806	242	79	34.8	34.8	NUM
ajst-7806	242	80	31.5	31.5	NUM
ajst-7806	242	81	7.6	7.6	NUM
ajst-7806	242	82	11.5	11.5	NUM
ajst-7806	242	83	55.0	55.0	NUM
ajst-7806	242	84	53.1	53.1	NUM
ajst-7806	242	85	34.1	34.1	NUM
ajst-7806	242	86	1.7	1.7	NUM
ajst-7806	242	87	33.1	33.1	NUM
ajst-7806	242	88	49.2	49.2	NUM
ajst-7806	242	89	42.0	42.0	NUM
ajst-7806	242	90	47.3	47.3	NUM
ajst-7806	242	91	56.6	56.6	NUM
ajst-7806	242	92	15.3	15.3	NUM
ajst-7806	242	93	12.8	12.8	NUM
ajst-7806	242	94	24.8	24.8	NUM
ajst-7806	242	95	48.9	48.9	NUM
ajst-7806	242	96	44.4	44.4	NUM
ajst-7806	242	97	47.8	47.8	NUM
ajst-7806	242	98	36.3	36.3	NUM
ajst-7806	242	99	31.1	31.1	NUM
ajst-7806	242	100	19.4	19.4	NUM
ajst-7806	242	101	13.0	13.0	NUM
ajst-7806	242	102	65.1	65.1	NUM
ajst-7806	242	103	62.2	62.2	NUM
ajst-7806	242	104	28.4	28.4	NUM
ajst-7806	242	105	24.8	24.8	NUM
ajst-7806	242	106	44.7	44.7	NUM
ajst-7806	242	107	30.6	30.6	NUM
ajst-7806	242	108	25.3	25.3	NUM
ajst-7806	242	109	37.8	37.8	NUM
ajst-7806	242	110	65.5	65.5	NUM
ajst-7806	242	111	15.7	15.7	NUM
ajst-7806	242	112	24.1	24.1	NUM
ajst-7806	242	113	41.7	41.7	NUM
ajst-7806	242	114	46.9	46.9	NUM
ajst-7806	242	115	64.3	64.3	NUM
ajst-7806	242	116	62.6	62.6	NUM
ajst-7806	242	117	41.2	41.2	NUM
ajst-7806	242	118	39.3	39.3	NUM
ajst-7806	242	119	19.2	19.2	NUM
ajst-7806	242	120	15.7	15.7	NUM
ajst-7806	242	121	62.9	62.9	NUM
ajst-7806	242	122	64.4	64.4	NUM
ajst-7806	242	123	30.0	30.0	NUM
ajst-7806	242	124	25.1	25.1	NUM
ajst-7806	242	125	52.5	52.5	NUM
ajst-7806	242	126	44.4	44.4	NUM
ajst-7806	242	127	19.6	19.6	NUM
ajst-7806	242	128	39.3	39.3	NUM
ajst-7806	242	129	67.7	67.7	NUM
ajst-7806	242	130	17.8	17.8	NUM
ajst-7806	242	131	22.9	22.9	NUM
ajst-7806	242	132	46.6	46.6	NUM
ajst-7806	242	133	57.5	57.5	NUM
ajst-7806	242	134	58.6	58.6	NUM
ajst-7806	242	135	63.0	63.0	NUM
ajst-7806	242	136	43.5	43.5	NUM
ajst-7806	242	137	39.8	39.8	NUM
ajst-7806	242	138	28.1	28.1	NUM
ajst-7806	242	139	16.4	16.4	NUM
ajst-7806	242	140	69.5	69.5	NUM
ajst-7806	242	141	68.2	68.2	NUM
ajst-7806	242	142	47.0	47.0	NUM
ajst-7806	242	143	27.9	27.9	NUM
ajst-7806	242	144	55.8	55.8	NUM
ajst-7806	242	145	43.7	43.7	NUM
ajst-7806	242	146	31.2	31.2	NUM
ajst-7806	242	147	43.8	43.8	NUM
ajst-7806	242	148	65.0	65.0	NUM
ajst-7806	242	149	10.9	10.9	NUM
ajst-7806	242	150	26.1	26.1	NUM
ajst-7806	242	151	52.7	52.7	NUM
ajst-7806	242	152	55.3	55.3	NUM
ajst-7806	242	153	60.2	60.2	NUM
ajst-7806	242	154	66.6	66.6	NUM
ajst-7806	242	155	46.8	46.8	NUM
ajst-7806	242	156	48.3	48.3	NUM
ajst-7806	242	157	26.0	26.0	NUM
ajst-7806	242	158	15.8	15.8	NUM
ajst-7806	242	159	66.5	66.5	NUM
ajst-7806	242	160	65.4	65.4	NUM
ajst-7806	242	161	53.9	53.9	NUM
ajst-7806	242	162	24.7	24.7	NUM
ajst-7806	242	163	61.2	61.2	NUM
ajst-7806	242	164	46.2	46.2	NUM
ajst-7806	242	165	53.5	53.5	NUM
ajst-7806	242	166	48.5	48.5	NUM
ajst-7806	242	167	66.1	66.1	NUM
ajst-7806	242	168	12.1	12.1	NUM
ajst-7806	242	169	22.0	22.0	NUM
ajst-7806	242	170	49.2	49.2	NUM
ajst-7806	242	171	53.2	53.2	NUM
ajst-7806	242	172	66.2	66.2	NUM
ajst-7806	242	173	59.4	59.4	NUM
ajst-7806	242	174	48.3	48.3	NUM
ajst-7806	242	175	57.2	57.2	NUM
ajst-7806	242	176	39.2	39.2	NUM
ajst-7806	242	177	24.3	24.3	NUM
ajst-7806	242	178	69.8	69.8	NUM
ajst-7806	242	179	66.2	66.2	NUM
ajst-7806	242	180	61.0	61.0	NUM
ajst-7806	242	181	29.8	29.8	NUM
ajst-7806	242	182	64.6	64.6	NUM
ajst-7806	242	183	42.5	42.5	NUM
ajst-7806	242	184	60.1	60.1	NUM
ajst-7806	242	185	71.2	71.2	NUM
ajst-7806	242	186	70.7	70.7	NUM
ajst-7806	242	187	21.9	21.9	NUM
ajst-7806	242	188	28.1	28.1	NUM
ajst-7806	242	189	58.6	58.6	NUM
ajst-7806	242	190	59.7	59.7	NUM
ajst-7806	242	191	52.2	52.2	NUM
ajst-7806	242	192	64.8	64.8	NUM
ajst-7806	242	193	53.6	53.6	NUM
ajst-7806	242	194	58.3	58.3	NUM
ajst-7806	242	195	39.1	39.1	NUM
ajst-7806	242	196	22.3	22.3	NUM
ajst-7806	242	197	66.7	66.7	NUM
ajst-7806	242	198	68.9	68.9	NUM
ajst-7806	242	199	63.4	63.4	NUM
ajst-7806	242	200	31.7	31.7	NUM
ajst-7806	242	201	67.8	67.8	NUM
ajst-7806	242	202	43.3	43.3	NUM
ajst-7806	242	203	62.2	62.2	NUM
ajst-7806	242	204	72.0	72.0	NUM
ajst-7806	242	205	69.5	69.5	NUM
ajst-7806	242	206	20.4	20.4	NUM
ajst-7806	242	207	27.8	27.8	NUM
ajst-7806	242	208	59.1	59.1	NUM
ajst-7806	242	209	59.8	59.8	NUM
ajst-7806	242	210	52.6	52.6	NUM
ajst-7806	242	211	64.9	64.9	NUM
ajst-7806	242	212	54.0	54.0	NUM
ajst-7806	242	213	as	as	SCONJ
ajst-7806	242	214	shown	show	VERB
ajst-7806	242	215	in	in	ADP
ajst-7806	242	216	table	table	NOUN
ajst-7806	242	217	4	4	NUM
ajst-7806	242	218	,	,	PUNCT
ajst-7806	242	219	we	we	PRON
ajst-7806	242	220	can	can	AUX
ajst-7806	242	221	intuitively	intuitively	ADV
ajst-7806	242	222	see	see	VERB
ajst-7806	242	223	the	the	DET
ajst-7806	242	224	performance	performance	NOUN
ajst-7806	242	225	of	of	ADP
ajst-7806	242	226	the	the	DET
ajst-7806	242	227	positioning	positioning	NOUN
ajst-7806	242	228	performance	performance	NOUN
ajst-7806	242	229	of	of	ADP
ajst-7806	242	230	the	the	DET
ajst-7806	242	231	method	method	NOUN
ajst-7806	242	232	146	146	NUM
ajst-7806	242	233	proposed	propose	VERB
ajst-7806	242	234	in	in	ADP
ajst-7806	242	235	this	this	DET
ajst-7806	242	236	chapter	chapter	NOUN
ajst-7806	242	237	and	and	CCONJ
ajst-7806	242	238	other	other	ADJ
ajst-7806	242	239	methods	method	NOUN
ajst-7806	242	240	in	in	ADP
ajst-7806	242	241	different	different	ADJ
ajst-7806	242	242	categories	category	NOUN
ajst-7806	242	243	of	of	ADP
ajst-7806	242	244	voc	voc	PROPN
ajst-7806	242	245	2007	2007	NUM
ajst-7806	242	246	data	datum	NOUN
ajst-7806	242	247	sets	set	NOUN
ajst-7806	242	248	.	.	PUNCT
ajst-7806	243	1	compared	compare	VERB
ajst-7806	243	2	with	with	ADP
ajst-7806	243	3	other	other	ADJ
ajst-7806	243	4	methods	method	NOUN
ajst-7806	243	5	,	,	PUNCT
ajst-7806	243	6	the	the	DET
ajst-7806	243	7	positioning	positioning	NOUN
ajst-7806	243	8	performance	performance	NOUN
ajst-7806	243	9	in	in	ADP
ajst-7806	243	10	eight	eight	NUM
ajst-7806	243	11	categories	category	NOUN
ajst-7806	243	12	such	such	ADJ
ajst-7806	243	13	as	as	ADP
ajst-7806	243	14	birds	bird	NOUN
ajst-7806	243	15	and	and	CCONJ
ajst-7806	243	16	cats	cat	NOUN
ajst-7806	243	17	has	have	AUX
ajst-7806	243	18	been	be	AUX
ajst-7806	243	19	improved	improve	VERB
ajst-7806	243	20	to	to	ADP
ajst-7806	243	21	some	some	DET
ajst-7806	243	22	extent	extent	NOUN
ajst-7806	243	23	,	,	PUNCT
ajst-7806	243	24	which	which	PRON
ajst-7806	243	25	proves	prove	VERB
ajst-7806	243	26	the	the	DET
ajst-7806	243	27	effectiveness	effectiveness	NOUN
ajst-7806	243	28	of	of	ADP
ajst-7806	243	29	the	the	DET
ajst-7806	243	30	method	method	NOUN
ajst-7806	243	31	in	in	ADP
ajst-7806	243	32	this	this	DET
ajst-7806	243	33	chapter	chapter	NOUN
ajst-7806	243	34	.	.	PUNCT
ajst-7806	244	1	as	as	SCONJ
ajst-7806	244	2	shown	show	VERB
ajst-7806	244	3	in	in	ADP
ajst-7806	244	4	table	table	NOUN
ajst-7806	244	5	5	5	NUM
ajst-7806	244	6	,	,	PUNCT
ajst-7806	244	7	it	it	PRON
ajst-7806	244	8	shows	show	VERB
ajst-7806	244	9	the	the	DET
ajst-7806	244	10	comparison	comparison	NOUN
ajst-7806	244	11	of	of	ADP
ajst-7806	244	12	the	the	DET
ajst-7806	244	13	accuracy	accuracy	NOUN
ajst-7806	244	14	and	and	CCONJ
ajst-7806	244	15	positioning	positioning	NOUN
ajst-7806	244	16	performance	performance	NOUN
ajst-7806	244	17	of	of	ADP
ajst-7806	244	18	the	the	DET
ajst-7806	244	19	weakly	weakly	ADJ
ajst-7806	244	20	supervised	supervised	ADJ
ajst-7806	244	21	target	target	NOUN
ajst-7806	244	22	detection	detection	NOUN
ajst-7806	244	23	model	model	NOUN
ajst-7806	244	24	proposed	propose	VERB
ajst-7806	244	25	in	in	ADP
ajst-7806	244	26	this	this	DET
ajst-7806	244	27	chapter	chapter	NOUN
ajst-7806	244	28	with	with	ADP
ajst-7806	244	29	other	other	ADJ
ajst-7806	244	30	weakly	weakly	ADJ
ajst-7806	244	31	supervised	supervised	ADJ
ajst-7806	244	32	target	target	NOUN
ajst-7806	244	33	detection	detection	NOUN
ajst-7806	244	34	models	model	NOUN
ajst-7806	244	35	on	on	ADP
ajst-7806	244	36	voc	voc	PROPN
ajst-7806	244	37	2012	2012	NUM
ajst-7806	244	38	data	datum	NOUN
ajst-7806	244	39	sets	set	NOUN
ajst-7806	244	40	.	.	PUNCT
ajst-7806	245	1	it	it	PRON
ajst-7806	245	2	can	can	AUX
ajst-7806	245	3	be	be	AUX
ajst-7806	245	4	seen	see	VERB
ajst-7806	245	5	that	that	SCONJ
ajst-7806	245	6	the	the	DET
ajst-7806	245	7	method	method	NOUN
ajst-7806	245	8	proposed	propose	VERB
ajst-7806	245	9	in	in	ADP
ajst-7806	245	10	this	this	DET
ajst-7806	245	11	chapter	chapter	NOUN
ajst-7806	245	12	has	have	AUX
ajst-7806	245	13	improved	improve	VERB
ajst-7806	245	14	to	to	ADP
ajst-7806	245	15	some	some	DET
ajst-7806	245	16	extent	extent	NOUN
ajst-7806	245	17	compared	compare	VERB
ajst-7806	245	18	with	with	ADP
ajst-7806	245	19	other	other	ADJ
ajst-7806	245	20	methods	method	NOUN
ajst-7806	245	21	,	,	PUNCT
ajst-7806	245	22	which	which	PRON
ajst-7806	245	23	fully	fully	ADV
ajst-7806	245	24	proves	prove	VERB
ajst-7806	245	25	the	the	DET
ajst-7806	245	26	effectiveness	effectiveness	NOUN
ajst-7806	245	27	of	of	ADP
ajst-7806	245	28	this	this	DET
ajst-7806	245	29	work	work	NOUN
ajst-7806	245	30	.	.	PUNCT
ajst-7806	246	1	compared	compare	VERB
ajst-7806	246	2	with	with	ADP
ajst-7806	246	3	the	the	DET
ajst-7806	246	4	wsod2	wsod2	NOUN
ajst-7806	246	5	algorithm	algorithm	NOUN
ajst-7806	246	6	model	model	NOUN
ajst-7806	246	7	,	,	PUNCT
ajst-7806	246	8	the	the	DET
ajst-7806	246	9	method	method	NOUN
ajst-7806	246	10	proposed	propose	VERB
ajst-7806	246	11	in	in	ADP
ajst-7806	246	12	this	this	DET
ajst-7806	246	13	chapter	chapter	NOUN
ajst-7806	246	14	has	have	AUX
ajst-7806	246	15	been	be	AUX
ajst-7806	246	16	improved	improve	VERB
ajst-7806	246	17	to	to	ADP
ajst-7806	246	18	some	some	DET
ajst-7806	246	19	extent	extent	NOUN
ajst-7806	246	20	from	from	ADP
ajst-7806	246	21	the	the	DET
ajst-7806	246	22	aspect	aspect	NOUN
ajst-7806	246	23	of	of	ADP
ajst-7806	246	24	improving	improve	VERB
ajst-7806	246	25	the	the	DET
ajst-7806	246	26	quality	quality	NOUN
ajst-7806	246	27	of	of	ADP
ajst-7806	246	28	proposal	proposal	NOUN
ajst-7806	246	29	box	box	NOUN
ajst-7806	246	30	,	,	PUNCT
ajst-7806	246	31	and	and	CCONJ
ajst-7806	246	32	the	the	DET
ajst-7806	246	33	quality	quality	NOUN
ajst-7806	246	34	of	of	ADP
ajst-7806	246	35	proposal	proposal	NOUN
ajst-7806	246	36	box	box	PROPN
ajst-7806	246	37	has	have	AUX
ajst-7806	246	38	been	be	AUX
ajst-7806	246	39	effectively	effectively	ADV
ajst-7806	246	40	improved	improve	VERB
ajst-7806	246	41	from	from	ADP
ajst-7806	246	42	the	the	DET
ajst-7806	246	43	aspects	aspect	NOUN
ajst-7806	246	44	of	of	ADP
ajst-7806	246	45	proposal	proposal	NOUN
ajst-7806	246	46	generation	generation	NOUN
ajst-7806	246	47	and	and	CCONJ
ajst-7806	246	48	proposal	proposal	NOUN
ajst-7806	246	49	selection	selection	NOUN
ajst-7806	246	50	.	.	PUNCT
ajst-7806	247	1	the	the	DET
ajst-7806	247	2	improved	improve	VERB
ajst-7806	247	3	attention	attention	NOUN
ajst-7806	247	4	module	module	NOUN
ajst-7806	247	5	has	have	VERB
ajst-7806	247	6	a	a	DET
ajst-7806	247	7	good	good	ADJ
ajst-7806	247	8	impact	impact	NOUN
ajst-7806	247	9	on	on	ADP
ajst-7806	247	10	the	the	DET
ajst-7806	247	11	follow	follow	VERB
ajst-7806	247	12	-	-	PUNCT
ajst-7806	247	13	up	up	NOUN
ajst-7806	247	14	training	training	NOUN
ajst-7806	247	15	and	and	CCONJ
ajst-7806	247	16	improved	improve	VERB
ajst-7806	247	17	the	the	DET
ajst-7806	247	18	performance	performance	NOUN
ajst-7806	247	19	of	of	ADP
ajst-7806	247	20	the	the	DET
ajst-7806	247	21	weakly	weakly	ADJ
ajst-7806	247	22	supervised	supervised	ADJ
ajst-7806	247	23	target	target	NOUN
ajst-7806	247	24	detection	detection	NOUN
ajst-7806	247	25	model	model	NOUN
ajst-7806	247	26	.	.	PUNCT
ajst-7806	248	1	in	in	ADP
ajst-7806	248	2	pcl	pcl	PROPN
ajst-7806	248	3	algorithm	algorithm	PROPN
ajst-7806	248	4	,	,	PUNCT
ajst-7806	248	5	candidate	candidate	NOUN
ajst-7806	248	6	frame	frame	NOUN
ajst-7806	248	7	clustering	clustering	NOUN
ajst-7806	248	8	is	be	AUX
ajst-7806	248	9	used	use	VERB
ajst-7806	248	10	to	to	PART
ajst-7806	248	11	solve	solve	VERB
ajst-7806	248	12	the	the	DET
ajst-7806	248	13	multi	multi	ADJ
ajst-7806	248	14	-	-	ADJ
ajst-7806	248	15	instance	instance	NOUN
ajst-7806	248	16	problem	problem	NOUN
ajst-7806	248	17	of	of	ADP
ajst-7806	248	18	weakly	weakly	ADJ
ajst-7806	248	19	supervised	supervised	ADJ
ajst-7806	248	20	target	target	NOUN
ajst-7806	248	21	detection	detection	NOUN
ajst-7806	248	22	according	accord	VERB
ajst-7806	248	23	to	to	ADP
ajst-7806	248	24	the	the	DET
ajst-7806	248	25	standard	standard	NOUN
ajst-7806	248	26	of	of	ADP
ajst-7806	248	27	whether	whether	SCONJ
ajst-7806	248	28	candidate	candidate	NOUN
ajst-7806	248	29	frames	frame	NOUN
ajst-7806	248	30	overlap	overlap	VERB
ajst-7806	248	31	or	or	CCONJ
ajst-7806	248	32	not	not	PART
ajst-7806	248	33	,	,	PUNCT
ajst-7806	248	34	but	but	CCONJ
ajst-7806	248	35	the	the	DET
ajst-7806	248	36	method	method	NOUN
ajst-7806	248	37	in	in	ADP
ajst-7806	248	38	this	this	DET
ajst-7806	248	39	chapter	chapter	NOUN
ajst-7806	248	40	has	have	AUX
ajst-7806	248	41	made	make	VERB
ajst-7806	248	42	many	many	ADJ
ajst-7806	248	43	improvements	improvement	NOUN
ajst-7806	248	44	from	from	ADP
ajst-7806	248	45	the	the	DET
ajst-7806	248	46	proposal	proposal	NOUN
ajst-7806	248	47	generation	generation	NOUN
ajst-7806	248	48	part	part	NOUN
ajst-7806	248	49	and	and	CCONJ
ajst-7806	248	50	used	use	VERB
ajst-7806	248	51	a	a	DET
ajst-7806	248	52	simple	simple	ADJ
ajst-7806	248	53	and	and	CCONJ
ajst-7806	248	54	effective	effective	ADJ
ajst-7806	248	55	proposal	proposal	NOUN
ajst-7806	248	56	selection	selection	NOUN
ajst-7806	248	57	strategy	strategy	NOUN
ajst-7806	248	58	,	,	PUNCT
ajst-7806	248	59	which	which	PRON
ajst-7806	248	60	is	be	AUX
ajst-7806	248	61	obviously	obviously	ADV
ajst-7806	248	62	more	more	ADV
ajst-7806	248	63	advantageous	advantageous	ADJ
ajst-7806	248	64	.	.	PUNCT
ajst-7806	249	1	by	by	ADP
ajst-7806	249	2	comparing	compare	VERB
ajst-7806	249	3	the	the	DET
ajst-7806	249	4	existing	exist	VERB
ajst-7806	249	5	methods	method	NOUN
ajst-7806	249	6	,	,	PUNCT
ajst-7806	249	7	the	the	DET
ajst-7806	249	8	effectiveness	effectiveness	NOUN
ajst-7806	249	9	of	of	ADP
ajst-7806	249	10	the	the	DET
ajst-7806	249	11	algorithm	algorithm	NOUN
ajst-7806	249	12	in	in	ADP
ajst-7806	249	13	this	this	DET
ajst-7806	249	14	chapter	chapter	NOUN
ajst-7806	249	15	is	be	AUX
ajst-7806	249	16	fully	fully	ADV
ajst-7806	249	17	verified	verify	VERB
ajst-7806	249	18	,	,	PUNCT
ajst-7806	249	19	and	and	CCONJ
ajst-7806	249	20	its	its	PRON
ajst-7806	249	21	performance	performance	NOUN
ajst-7806	249	22	improvement	improvement	NOUN
ajst-7806	249	23	is	be	AUX
ajst-7806	249	24	mainly	mainly	ADV
ajst-7806	249	25	due	due	ADJ
ajst-7806	249	26	to	to	ADP
ajst-7806	249	27	the	the	DET
ajst-7806	249	28	improvement	improvement	NOUN
ajst-7806	249	29	of	of	ADP
ajst-7806	249	30	the	the	DET
ajst-7806	249	31	candidate	candidate	NOUN
ajst-7806	249	32	box	box	PROPN
ajst-7806	249	33	.	.	PUNCT
ajst-7806	250	1	table	table	NOUN
ajst-7806	250	2	4	4	NUM
ajst-7806	250	3	.	.	PUNCT
ajst-7806	251	1	localization	localization	NOUN
ajst-7806	251	2	performance	performance	NOUN
ajst-7806	251	3	of	of	ADP
ajst-7806	251	4	each	each	DET
ajst-7806	251	5	model	model	NOUN
ajst-7806	251	6	on	on	ADP
ajst-7806	251	7	pascal	pascal	PROPN
ajst-7806	251	8	voc	voc	PROPN
ajst-7806	251	9	2007	2007	NUM
ajst-7806	251	10	training	training	NOUN
ajst-7806	251	11	verification	verification	NOUN
ajst-7806	251	12	set	set	NOUN
ajst-7806	251	13	method	method	NOUN
ajst-7806	251	14	wsddn	wsddn	ADJ
ajst-7806	251	15	contextlocnet	contextlocnet	PROPN
ajst-7806	251	16	oicr	oicr	PROPN
ajst-7806	251	17	pcl	pcl	PROPN
ajst-7806	251	18	c	c	PROPN
ajst-7806	251	19	-	-	PUNCT
ajst-7806	251	20	wsl	wsl	PROPN
ajst-7806	251	21	wscdn	wscdn	PROPN
ajst-7806	251	22	wsod2	wsod2	VERB
ajst-7806	251	23	ours	ours	PRON
ajst-7806	251	24	aero	aero	PROPN
ajst-7806	251	25	65.1	65.1	NUM
ajst-7806	251	26	83.3	83.3	NUM
ajst-7806	251	27	81.7	81.7	NUM
ajst-7806	251	28	79.6	79.6	NUM
ajst-7806	251	29	85.8	85.8	NUM
ajst-7806	251	30	85.8	85.8	NUM
ajst-7806	251	31	87.1	87.1	NUM
ajst-7806	251	32	86.8	86.8	NUM
ajst-7806	251	33	bicycle	bicycle	NOUN
ajst-7806	251	34	58.8	58.8	NUM
ajst-7806	251	35	68.6	68.6	NUM
ajst-7806	251	36	80.4	80.4	NUM
ajst-7806	251	37	85.5	85.5	NUM
ajst-7806	251	38	81.2	81.2	NUM
ajst-7806	251	39	80.4	80.4	NUM
ajst-7806	251	40	80.0	80.0	NUM
ajst-7806	251	41	81.2	81.2	NUM
ajst-7806	251	42	bird	bird	NOUN
ajst-7806	251	43	boat	boat	NOUN
ajst-7806	251	44	bottle	bottle	NOUN
ajst-7806	251	45	bus	bus	NOUN
ajst-7806	251	46	car	car	NOUN
ajst-7806	251	47	cat	cat	NOUN
ajst-7806	251	48	chair	chair	NOUN
ajst-7806	251	49	cow	cow	NOUN
ajst-7806	251	50	table	table	NOUN
ajst-7806	251	51	dog	dog	NOUN
ajst-7806	251	52	horse	horse	NOUN
ajst-7806	251	53	mbike	mbike	ADJ
ajst-7806	251	54	person	person	NOUN
ajst-7806	251	55	plant	plant	NOUN
ajst-7806	251	56	sheep	sheep	NOUN
ajst-7806	251	57	sofa	sofa	NOUN
ajst-7806	251	58	train	train	NOUN
ajst-7806	251	59	tv	tv	NOUN
ajst-7806	251	60	corloc	corloc	NOUN
ajst-7806	251	61	58.5	58.5	NUM
ajst-7806	251	62	33.1	33.1	NUM
ajst-7806	251	63	39.8	39.8	NUM
ajst-7806	251	64	68.3	68.3	NUM
ajst-7806	251	65	60.2	60.2	NUM
ajst-7806	251	66	59.6	59.6	NUM
ajst-7806	251	67	34.8	34.8	NUM
ajst-7806	251	68	64.5	64.5	NUM
ajst-7806	251	69	30.5	30.5	NUM
ajst-7806	251	70	43.0	43.0	NUM
ajst-7806	251	71	56.8	56.8	NUM
ajst-7806	251	72	82.4	82.4	NUM
ajst-7806	251	73	25.5	25.5	NUM
ajst-7806	251	74	41.6	41.6	NUM
ajst-7806	251	75	61.5	61.5	NUM
ajst-7806	251	76	55.9	55.9	NUM
ajst-7806	251	77	65.9	65.9	NUM
ajst-7806	251	78	63.7	63.7	NUM
ajst-7806	251	79	53.5	53.5	NUM
ajst-7806	251	80	54.7	54.7	NUM
ajst-7806	251	81	23.4	23.4	NUM
ajst-7806	251	82	18.3	18.3	NUM
ajst-7806	251	83	73.6	73.6	NUM
ajst-7806	251	84	74.1	74.1	NUM
ajst-7806	251	85	54.1	54.1	NUM
ajst-7806	251	86	8.6	8.6	NUM
ajst-7806	251	87	65.1	65.1	NUM
ajst-7806	251	88	47.1	47.1	NUM
ajst-7806	251	89	59.5	59.5	NUM
ajst-7806	251	90	67.0	67.0	NUM
ajst-7806	251	91	83.5	83.5	NUM
ajst-7806	251	92	35.3	35.3	NUM
ajst-7806	251	93	39.9	39.9	NUM
ajst-7806	251	94	67.0	67.0	NUM
ajst-7806	251	95	49.7	49.7	NUM
ajst-7806	251	96	63.5	63.5	NUM
ajst-7806	251	97	65.2	65.2	NUM
ajst-7806	251	98	55.1	55.1	NUM
ajst-7806	251	99	48.7	48.7	NUM
ajst-7806	251	100	49.5	49.5	NUM
ajst-7806	251	101	32.8	32.8	NUM
ajst-7806	251	102	81.7	81.7	NUM
ajst-7806	251	103	85.4	85.4	NUM
ajst-7806	251	104	40.1	40.1	NUM
ajst-7806	251	105	40.6	40.6	NUM
ajst-7806	252	1	79.5	79.5	NUM
ajst-7806	252	2	35.7	35.7	NUM
ajst-7806	252	3	33.7	33.7	NUM
ajst-7806	252	4	60.5	60.5	NUM
ajst-7806	252	5	88.8	88.8	NUM
ajst-7806	252	6	21.8	21.8	NUM
ajst-7806	252	7	57.9	57.9	NUM
ajst-7806	252	8	76.3	76.3	NUM
ajst-7806	252	9	59.9	59.9	NUM
ajst-7806	252	10	75.3	75.3	NUM
ajst-7806	252	11	81.4	81.4	NUM
ajst-7806	252	12	60.6	60.6	NUM
ajst-7806	252	13	62.2	62.2	NUM
ajst-7806	252	14	47.9	47.9	NUM
ajst-7806	252	15	37.0	37.0	NUM
ajst-7806	252	16	83.8	83.8	NUM
ajst-7806	252	17	83.4	83.4	NUM
ajst-7806	252	18	43.0	43.0	NUM
ajst-7806	252	19	38.3	38.3	NUM
ajst-7806	252	20	80.1	80.1	NUM
ajst-7806	252	21	50.6	50.6	NUM
ajst-7806	252	22	30.9	30.9	NUM
ajst-7806	252	23	57.8	57.8	NUM
ajst-7806	252	24	90.8	90.8	NUM
ajst-7806	252	25	27.0	27.0	NUM
ajst-7806	252	26	58.2	58.2	NUM
ajst-7806	252	27	75.3	75.3	NUM
ajst-7806	252	28	68.5	68.5	NUM
ajst-7806	252	29	75.7	75.7	NUM
ajst-7806	252	30	78.9	78.9	NUM
ajst-7806	252	31	62.7	62.7	NUM
ajst-7806	252	32	64.9	64.9	NUM
ajst-7806	252	33	50.5	50.5	NUM
ajst-7806	252	34	32.1	32.1	NUM
ajst-7806	252	35	84.3	84.3	NUM
ajst-7806	252	36	85.9	85.9	NUM
ajst-7806	252	37	54.7	54.7	NUM
ajst-7806	252	38	43.4	43.4	NUM
ajst-7806	252	39	80.1	80.1	NUM
ajst-7806	252	40	42.2	42.2	NUM
ajst-7806	252	41	42.6	42.6	NUM
ajst-7806	252	42	60.5	60.5	NUM
ajst-7806	252	43	90.4	90.4	NUM
ajst-7806	252	44	13.7	13.7	NUM
ajst-7806	252	45	57.5	57.5	NUM
ajst-7806	252	46	82.5	82.5	NUM
ajst-7806	252	47	61.8	61.8	NUM
ajst-7806	252	48	74.1	74.1	NUM
ajst-7806	252	49	82.4	82.4	NUM
ajst-7806	252	50	63.5	63.5	NUM
ajst-7806	252	51	73.0	73.0	NUM
ajst-7806	252	52	42.6	42.6	NUM
ajst-7806	252	53	36.6	36.6	NUM
ajst-7806	252	54	79.7	79.7	NUM
ajst-7806	252	55	82.8	82.8	NUM
ajst-7806	252	56	66.0	66.0	NUM
ajst-7806	252	57	34.1	34.1	NUM
ajst-7806	252	58	78.1	78.1	NUM
ajst-7806	252	59	36.9	36.9	NUM
ajst-7806	252	60	68.6	68.6	NUM
ajst-7806	252	61	72.4	72.4	NUM
ajst-7806	252	62	91.6	91.6	NUM
ajst-7806	252	63	22.2	22.2	NUM
ajst-7806	252	64	51.3	51.3	NUM
ajst-7806	252	65	79.4	79.4	NUM
ajst-7806	252	66	63.7	63.7	NUM
ajst-7806	252	67	74.5	74.5	NUM
ajst-7806	252	68	74.6	74.6	NUM
ajst-7806	252	69	64.7	64.7	NUM
ajst-7806	252	70	74.8	74.8	NUM
ajst-7806	252	71	60.1	60.1	NUM
ajst-7806	252	72	36.6	36.6	NUM
ajst-7806	252	73	79.2	79.2	NUM
ajst-7806	252	74	83.8	83.8	NUM
ajst-7806	252	75	70.6	70.6	NUM
ajst-7806	252	76	43.5	43.5	NUM
ajst-7806	252	77	88.4	88.4	NUM
ajst-7806	252	78	46.0	46.0	NUM
ajst-7806	252	79	74.7	74.7	NUM
ajst-7806	252	80	87.4	87.4	NUM
ajst-7806	252	81	90.8	90.8	NUM
ajst-7806	252	82	44.2	44.2	NUM
ajst-7806	252	83	52.4	52.4	NUM
ajst-7806	252	84	81.4	81.4	NUM
ajst-7806	252	85	61.8	61.8	NUM
ajst-7806	252	86	67.7	67.7	NUM
ajst-7806	252	87	79.9	79.9	NUM
ajst-7806	252	88	69.5	69.5	NUM
ajst-7806	252	89	74.9	74.9	NUM
ajst-7806	252	90	55.9	55.9	NUM
ajst-7806	252	91	36.8	36.8	NUM
ajst-7806	252	92	79.6	79.6	NUM
ajst-7806	252	93	84.8	84.8	NUM
ajst-7806	252	94	71.3	71.3	NUM
ajst-7806	252	95	44.4	44.4	NUM
ajst-7806	252	96	88.9	88.9	NUM
ajst-7806	252	97	46.5	46.5	NUM
ajst-7806	252	98	76.3	76.3	NUM
ajst-7806	252	99	88.0	88.0	NUM
ajst-7806	252	100	90.5	90.5	NUM
ajst-7806	252	101	42.6	42.6	NUM
ajst-7806	252	102	51.1	51.1	NUM
ajst-7806	252	103	82.5	82.5	NUM
ajst-7806	252	104	62.7	62.7	NUM
ajst-7806	252	105	68.3	68.3	NUM
ajst-7806	252	106	80.1	80.1	NUM
ajst-7806	252	107	69.7	69.7	NUM
ajst-7806	252	108	table	table	NOUN
ajst-7806	252	109	5	5	NUM
ajst-7806	252	110	.	.	PUNCT
ajst-7806	252	111	comparison	comparison	NOUN
ajst-7806	252	112	of	of	ADP
ajst-7806	252	113	various	various	ADJ
ajst-7806	252	114	models	model	NOUN
ajst-7806	252	115	on	on	ADP
ajst-7806	252	116	pascal	pascal	PROPN
ajst-7806	252	117	voc	voc	PROPN
ajst-7806	252	118	2012	2012	NUM
ajst-7806	252	119	data	datum	NOUN
ajst-7806	252	120	set	set	VERB
ajst-7806	252	121	method	method	NOUN
ajst-7806	252	122	map	map	NOUN
ajst-7806	252	123	corloc	corloc	PROPN
ajst-7806	252	124	oicr	oicr	NOUN
ajst-7806	252	125	37.9	37.9	NUM
ajst-7806	252	126	62.1	62.1	NUM
ajst-7806	252	127	pcl	pcl	PROPN
ajst-7806	252	128	40.6	40.6	NUM
ajst-7806	252	129	63.2	63.2	NUM
ajst-7806	252	130	wscdn	wscdn	NOUN
ajst-7806	252	131	43.3	43.3	NUM
ajst-7806	252	132	65.2	65.2	NUM
ajst-7806	252	133	wsod2	wsod2	NOUN
ajst-7806	252	134	47.2	47.2	NUM
ajst-7806	252	135	71.9	71.9	NUM
ajst-7806	252	136	ours	ours	PRON
ajst-7806	252	137	47.9	47.9	NUM
ajst-7806	252	138	72.5	72.5	NUM
ajst-7806	252	139	4.4	4.4	NUM
ajst-7806	252	140	.	.	PUNCT
ajst-7806	253	1	experimental	experimental	ADJ
ajst-7806	253	2	results	result	NOUN
ajst-7806	253	3	fig	fig	NOUN
ajst-7806	253	4	.	.	PUNCT
ajst-7806	254	1	7	7	NUM
ajst-7806	254	2	shows	show	VERB
ajst-7806	254	3	some	some	DET
ajst-7806	254	4	detection	detection	NOUN
ajst-7806	254	5	results	result	NOUN
ajst-7806	254	6	of	of	ADP
ajst-7806	254	7	the	the	DET
ajst-7806	254	8	algorithm	algorithm	NOUN
ajst-7806	254	9	model	model	NOUN
ajst-7806	254	10	proposed	propose	VERB
ajst-7806	254	11	in	in	ADP
ajst-7806	254	12	this	this	DET
ajst-7806	254	13	paper	paper	NOUN
ajst-7806	254	14	on	on	ADP
ajst-7806	254	15	pascal	pascal	ADJ
ajst-7806	254	16	voc2007	voc2007	NOUN
ajst-7806	254	17	data	datum	NOUN
ajst-7806	254	18	set	set	NOUN
ajst-7806	254	19	,	,	PUNCT
ajst-7806	254	20	where	where	SCONJ
ajst-7806	254	21	the	the	DET
ajst-7806	254	22	green	green	PROPN
ajst-7806	254	23	box	box	PROPN
ajst-7806	254	24	is	be	AUX
ajst-7806	254	25	the	the	DET
ajst-7806	254	26	real	real	ADJ
ajst-7806	254	27	label	label	NOUN
ajst-7806	254	28	of	of	ADP
ajst-7806	254	29	the	the	DET
ajst-7806	254	30	image	image	NOUN
ajst-7806	254	31	and	and	CCONJ
ajst-7806	254	32	the	the	DET
ajst-7806	254	33	red	red	PROPN
ajst-7806	254	34	box	box	PROPN
ajst-7806	254	35	is	be	AUX
ajst-7806	254	36	the	the	DET
ajst-7806	254	37	detection	detection	NOUN
ajst-7806	254	38	result	result	NOUN
ajst-7806	254	39	of	of	ADP
ajst-7806	254	40	the	the	DET
ajst-7806	254	41	algorithm	algorithm	NOUN
ajst-7806	254	42	proposed	propose	VERB
ajst-7806	254	43	in	in	ADP
ajst-7806	254	44	this	this	DET
ajst-7806	254	45	paper	paper	NOUN
ajst-7806	254	46	.	.	PUNCT
ajst-7806	255	1	it	it	PRON
ajst-7806	255	2	can	can	AUX
ajst-7806	255	3	be	be	AUX
ajst-7806	255	4	seen	see	VERB
ajst-7806	255	5	that	that	SCONJ
ajst-7806	255	6	the	the	DET
ajst-7806	255	7	prediction	prediction	NOUN
ajst-7806	255	8	results	result	NOUN
ajst-7806	255	9	of	of	ADP
ajst-7806	255	10	the	the	DET
ajst-7806	255	11	algorithm	algorithm	NOUN
ajst-7806	255	12	model	model	NOUN
ajst-7806	255	13	proposed	propose	VERB
ajst-7806	255	14	in	in	ADP
ajst-7806	255	15	this	this	DET
ajst-7806	255	16	paper	paper	NOUN
ajst-7806	255	17	are	be	AUX
ajst-7806	255	18	basically	basically	ADV
ajst-7806	255	19	close	close	ADJ
ajst-7806	255	20	to	to	ADP
ajst-7806	255	21	the	the	DET
ajst-7806	255	22	real	real	ADJ
ajst-7806	255	23	tags	tag	NOUN
ajst-7806	255	24	,	,	PUNCT
ajst-7806	255	25	but	but	CCONJ
ajst-7806	255	26	there	there	PRON
ajst-7806	255	27	may	may	AUX
ajst-7806	255	28	be	be	AUX
ajst-7806	255	29	local	local	ADJ
ajst-7806	255	30	optimization	optimization	NOUN
ajst-7806	255	31	problems	problem	NOUN
ajst-7806	255	32	for	for	ADP
ajst-7806	255	33	human	human	ADJ
ajst-7806	255	34	detection	detection	NOUN
ajst-7806	255	35	,	,	PUNCT
ajst-7806	255	36	in	in	ADP
ajst-7806	255	37	which	which	PRON
ajst-7806	255	38	the	the	DET
ajst-7806	255	39	detection	detection	NOUN
ajst-7806	255	40	frame	frame	NOUN
ajst-7806	255	41	is	be	AUX
ajst-7806	255	42	too	too	ADV
ajst-7806	255	43	small	small	ADJ
ajst-7806	255	44	,	,	PUNCT
ajst-7806	255	45	but	but	CCONJ
ajst-7806	255	46	this	this	PRON
ajst-7806	255	47	will	will	AUX
ajst-7806	255	48	not	not	PART
ajst-7806	255	49	happen	happen	VERB
ajst-7806	255	50	for	for	ADP
ajst-7806	255	51	other	other	ADJ
ajst-7806	255	52	objects	object	NOUN
ajst-7806	255	53	recognition	recognition	NOUN
ajst-7806	255	54	,	,	PUNCT
ajst-7806	255	55	which	which	PRON
ajst-7806	255	56	is	be	AUX
ajst-7806	255	57	an	an	DET
ajst-7806	255	58	improvement	improvement	NOUN
ajst-7806	255	59	compared	compare	VERB
ajst-7806	255	60	with	with	ADP
ajst-7806	255	61	oicr	oicr	NOUN
ajst-7806	255	62	and	and	CCONJ
ajst-7806	255	63	wsod2	wsod2	NOUN
ajst-7806	255	64	.	.	PUNCT
ajst-7806	256	1	147	147	NUM
ajst-7806	256	2	figure	figure	NOUN
ajst-7806	256	3	7	7	NUM
ajst-7806	256	4	.	.	PUNCT
ajst-7806	256	5	experimental	experimental	ADJ
ajst-7806	256	6	part	part	NOUN
ajst-7806	256	7	of	of	ADP
ajst-7806	256	8	the	the	DET
ajst-7806	256	9	results	result	NOUN
ajst-7806	256	10	show	show	VERB
ajst-7806	256	11	5	5	NUM
ajst-7806	256	12	.	.	PUNCT
ajst-7806	256	13	conclusion	conclusion	NOUN
ajst-7806	256	14	in	in	ADP
ajst-7806	256	15	this	this	DET
ajst-7806	256	16	paper	paper	NOUN
ajst-7806	256	17	,	,	PUNCT
ajst-7806	256	18	firstly	firstly	ADV
ajst-7806	256	19	,	,	PUNCT
ajst-7806	256	20	a	a	DET
ajst-7806	256	21	detection	detection	NOUN
ajst-7806	256	22	method	method	NOUN
ajst-7806	256	23	based	base	VERB
ajst-7806	256	24	on	on	ADP
ajst-7806	256	25	multiinstance	multiinstance	NOUN
ajst-7806	256	26	learning	learn	VERB
ajst-7806	256	27	idea	idea	NOUN
ajst-7806	256	28	is	be	AUX
ajst-7806	256	29	used	use	VERB
ajst-7806	256	30	to	to	PART
ajst-7806	256	31	obtain	obtain	VERB
ajst-7806	256	32	the	the	DET
ajst-7806	256	33	initial	initial	ADJ
ajst-7806	256	34	object	object	NOUN
ajst-7806	256	35	bounding	bounding	NOUN
ajst-7806	256	36	box	box	NOUN
ajst-7806	256	37	,	,	PUNCT
ajst-7806	256	38	and	and	CCONJ
ajst-7806	256	39	the	the	DET
ajst-7806	256	40	weakly	weakly	ADJ
ajst-7806	256	41	supervised	supervised	ADJ
ajst-7806	256	42	object	object	NOUN
ajst-7806	256	43	detection	detection	NOUN
ajst-7806	256	44	problem	problem	NOUN
ajst-7806	256	45	is	be	AUX
ajst-7806	256	46	understood	understand	VERB
ajst-7806	256	47	as	as	ADP
ajst-7806	256	48	a	a	DET
ajst-7806	256	49	multi	multi	ADJ
ajst-7806	256	50	-	-	ADJ
ajst-7806	256	51	instance	instance	ADJ
ajst-7806	256	52	learning	learning	NOUN
ajst-7806	256	53	problem	problem	NOUN
ajst-7806	256	54	,	,	PUNCT
ajst-7806	256	55	in	in	ADP
ajst-7806	256	56	which	which	PRON
ajst-7806	256	57	the	the	DET
ajst-7806	256	58	input	input	NOUN
ajst-7806	256	59	image	image	NOUN
ajst-7806	256	60	is	be	AUX
ajst-7806	256	61	equivalent	equivalent	ADJ
ajst-7806	256	62	to	to	ADP
ajst-7806	256	63	a	a	DET
ajst-7806	256	64	set	set	NOUN
ajst-7806	256	65	of	of	ADP
ajst-7806	256	66	object	object	NOUN
ajst-7806	256	67	proposals	proposal	NOUN
ajst-7806	256	68	.	.	PUNCT
ajst-7806	257	1	in	in	ADP
ajst-7806	257	2	this	this	DET
ajst-7806	257	3	paper	paper	NOUN
ajst-7806	257	4	,	,	PUNCT
ajst-7806	257	5	three	three	NUM
ajst-7806	257	6	modules	module	NOUN
ajst-7806	257	7	can	can	AUX
ajst-7806	257	8	be	be	AUX
ajst-7806	257	9	embedded	embed	VERB
ajst-7806	257	10	in	in	ADP
ajst-7806	257	11	the	the	DET
ajst-7806	257	12	framework	framework	NOUN
ajst-7806	257	13	of	of	ADP
ajst-7806	257	14	weakly	weakly	ADJ
ajst-7806	257	15	supervised	supervised	ADJ
ajst-7806	257	16	target	target	NOUN
ajst-7806	257	17	detection	detection	NOUN
ajst-7806	257	18	,	,	PUNCT
ajst-7806	257	19	which	which	PRON
ajst-7806	257	20	are	be	AUX
ajst-7806	257	21	used	use	VERB
ajst-7806	257	22	to	to	PART
ajst-7806	257	23	generate	generate	VERB
ajst-7806	257	24	high	high	ADJ
ajst-7806	257	25	-	-	PUNCT
ajst-7806	257	26	quality	quality	NOUN
ajst-7806	257	27	proposals	proposal	NOUN
ajst-7806	257	28	and	and	CCONJ
ajst-7806	257	29	filter	filter	VERB
ajst-7806	257	30	them	they	PRON
ajst-7806	257	31	.	.	PUNCT
ajst-7806	258	1	finally	finally	ADV
ajst-7806	258	2	,	,	PUNCT
ajst-7806	258	3	more	more	ADV
ajst-7806	258	4	accurate	accurate	ADJ
ajst-7806	258	5	proposals	proposal	NOUN
ajst-7806	258	6	that	that	PRON
ajst-7806	258	7	are	be	AUX
ajst-7806	258	8	beneficial	beneficial	ADJ
ajst-7806	258	9	to	to	ADP
ajst-7806	258	10	subsequent	subsequent	ADJ
ajst-7806	258	11	training	training	NOUN
ajst-7806	258	12	are	be	AUX
ajst-7806	258	13	selected	select	VERB
ajst-7806	258	14	,	,	PUNCT
ajst-7806	258	15	and	and	CCONJ
ajst-7806	258	16	their	their	PRON
ajst-7806	258	17	effectiveness	effectiveness	NOUN
ajst-7806	258	18	is	be	AUX
ajst-7806	258	19	demonstrated	demonstrate	VERB
ajst-7806	258	20	on	on	ADP
ajst-7806	258	21	pascal	pascal	PROPN
ajst-7806	258	22	voc	voc	PROPN
ajst-7806	258	23	2007	2007	NUM
ajst-7806	258	24	and	and	CCONJ
ajst-7806	258	25	pascal	pascal	PROPN
ajst-7806	258	26	voc	voc	PROPN
ajst-7806	258	27	2012	2012	NUM
ajst-7806	258	28	data	datum	NOUN
ajst-7806	258	29	sets	set	NOUN
ajst-7806	258	30	,	,	PUNCT
ajst-7806	258	31	and	and	CCONJ
ajst-7806	258	32	the	the	DET
ajst-7806	258	33	existing	exist	VERB
ajst-7806	258	34	weakly	weakly	ADJ
ajst-7806	258	35	supervised	supervised	ADJ
ajst-7806	258	36	target	target	NOUN
ajst-7806	258	37	detection	detection	NOUN
ajst-7806	258	38	algorithms	algorithm	NOUN
ajst-7806	258	39	are	be	AUX
ajst-7806	258	40	significantly	significantly	ADV
ajst-7806	258	41	improved	improve	VERB
ajst-7806	258	42	.	.	PUNCT
ajst-7806	259	1	references	reference	NOUN
ajst-7806	259	2	[	[	X
ajst-7806	259	3	1	1	NUM
ajst-7806	259	4	]	]	PUNCT
ajst-7806	259	5	tang	tang	NOUN
ajst-7806	259	6	p	p	X
ajst-7806	259	7	,	,	PUNCT
ajst-7806	259	8	wang	wang	PROPN
ajst-7806	259	9	x	x	PROPN
ajst-7806	259	10	,	,	PUNCT
ajst-7806	259	11	bai	bai	PROPN
ajst-7806	259	12	s	s	PART
ajst-7806	259	13	,	,	PUNCT
ajst-7806	259	14	et	et	PROPN
ajst-7806	259	15	al	al	PROPN
ajst-7806	259	16	.	.	PROPN
ajst-7806	259	17	pcl	pcl	PROPN
ajst-7806	259	18	:	:	PUNCT
ajst-7806	259	19	proposal	proposal	NOUN
ajst-7806	259	20	cluster	cluster	NOUN
ajst-7806	259	21	learning	learn	VERB
ajst-7806	259	22	for	for	ADP
ajst-7806	259	23	weakly	weakly	ADJ
ajst-7806	259	24	supervised	supervised	ADJ
ajst-7806	259	25	object	object	NOUN
ajst-7806	259	26	detection[j	detection[j	PROPN
ajst-7806	259	27	]	]	PUNCT
ajst-7806	259	28	.	.	PUNCT
ajst-7806	260	1	ieee	ieee	NOUN
ajst-7806	260	2	transactions	transaction	NOUN
ajst-7806	260	3	on	on	ADP
ajst-7806	260	4	pattern	pattern	NOUN
ajst-7806	260	5	analysis	analysis	NOUN
ajst-7806	260	6	and	and	CCONJ
ajst-7806	260	7	machine	machine	NOUN
ajst-7806	260	8	intelligence	intelligence	NOUN
ajst-7806	260	9	,	,	PUNCT
ajst-7806	260	10	2018	2018	NUM
ajst-7806	260	11	,	,	PUNCT
ajst-7806	260	12	42(1	42(1	NOUN
ajst-7806	260	13	):	):	PUNCT
ajst-7806	260	14	176191	176191	NUM
ajst-7806	260	15	.	.	PUNCT
ajst-7806	261	1	[	[	X
ajst-7806	261	2	2	2	NUM
ajst-7806	261	3	]	]	X
ajst-7806	261	4	tang	tang	NOUN
ajst-7806	261	5	p	p	X
ajst-7806	261	6	,	,	PUNCT
ajst-7806	261	7	wang	wang	PROPN
ajst-7806	261	8	x	x	PROPN
ajst-7806	261	9	,	,	PUNCT
ajst-7806	261	10	bai	bai	PROPN
ajst-7806	261	11	x	x	SYM
ajst-7806	261	12	,	,	PUNCT
ajst-7806	261	13	et	et	PROPN
ajst-7806	261	14	al	al	PROPN
ajst-7806	261	15	.	.	PROPN
ajst-7806	261	16	multiple	multiple	PROPN
ajst-7806	261	17	instance	instance	NOUN
ajst-7806	261	18	detection	detection	NOUN
ajst-7806	261	19	network	network	NOUN
ajst-7806	261	20	with	with	ADP
ajst-7806	261	21	online	online	PROPN
ajst-7806	261	22	instance	instance	NOUN
ajst-7806	261	23	classifier	classifier	NOUN
ajst-7806	261	24	refinement	refinement	NOUN
ajst-7806	262	1	[	[	X
ajst-7806	262	2	c]//	c]//	ADJ
ajst-7806	262	3	proceedings	proceeding	NOUN
ajst-7806	262	4	of	of	ADP
ajst-7806	262	5	the	the	DET
ajst-7806	262	6	ieee	ieee	NOUN
ajst-7806	262	7	conference	conference	NOUN
ajst-7806	262	8	on	on	ADP
ajst-7806	262	9	computer	computer	NOUN
ajst-7806	262	10	vision	vision	NOUN
ajst-7806	262	11	and	and	CCONJ
ajst-7806	262	12	pattern	pattern	NOUN
ajst-7806	262	13	recognition	recognition	NOUN
ajst-7806	262	14	.	.	PUNCT
ajst-7806	263	1	2017	2017	NUM
ajst-7806	263	2	:	:	PUNCT
ajst-7806	263	3	2843	2843	NUM
ajst-7806	263	4	-	-	SYM
ajst-7806	263	5	2851	2851	NUM
ajst-7806	263	6	.	.	PUNCT
ajst-7806	264	1	[	[	X
ajst-7806	264	2	3	3	X
ajst-7806	264	3	]	]	X
ajst-7806	264	4	jifeng	jifeng	PROPN
ajst-7806	264	5	dai	dai	PROPN
ajst-7806	264	6	,	,	PUNCT
ajst-7806	264	7	yi	yi	PROPN
ajst-7806	264	8	li	li	PROPN
ajst-7806	264	9	,	,	PUNCT
ajst-7806	264	10	kaiming	kaime	VERB
ajst-7806	264	11	he	he	PRON
ajst-7806	264	12	,	,	PUNCT
ajst-7806	264	13	and	and	CCONJ
ajst-7806	264	14	jian	jian	PROPN
ajst-7806	264	15	sun	sun	PROPN
ajst-7806	264	16	.	.	PUNCT
ajst-7806	265	1	r	r	X
ajst-7806	265	2	-	-	PUNCT
ajst-7806	265	3	fcn	fcn	NOUN
ajst-7806	265	4	:	:	PUNCT
ajst-7806	265	5	object	object	VERB
ajst-7806	265	6	detection	detection	NOUN
ajst-7806	265	7	via	via	ADP
ajst-7806	265	8	region	region	NOUN
ajst-7806	265	9	-	-	PUNCT
ajst-7806	265	10	based	base	VERB
ajst-7806	265	11	fully	fully	ADV
ajst-7806	265	12	convolutional	convolutional	ADJ
ajst-7806	265	13	networks	network	NOUN
ajst-7806	265	14	.	.	PUNCT
ajst-7806	266	1	in	in	ADP
ajst-7806	266	2	nips	nip	NOUN
ajst-7806	266	3	,	,	PUNCT
ajst-7806	266	4	pages	page	NOUN
ajst-7806	266	5	379–387	379–387	NUM
ajst-7806	266	6	,	,	PUNCT
ajst-7806	266	7	2016	2016	NUM
ajst-7806	266	8	.	.	PUNCT
ajst-7806	267	1	[	[	X
ajst-7806	267	2	4	4	X
ajst-7806	267	3	]	]	X
ajst-7806	267	4	jifeng	jifeng	PROPN
ajst-7806	267	5	dai	dai	PROPN
ajst-7806	267	6	,	,	PUNCT
ajst-7806	267	7	haozhi	haozhi	PROPN
ajst-7806	267	8	qi	qi	PROPN
ajst-7806	267	9	,	,	PUNCT
ajst-7806	267	10	yuwen	yuwen	PROPN
ajst-7806	267	11	xiong	xiong	PROPN
ajst-7806	267	12	,	,	PUNCT
ajst-7806	267	13	yi	yi	PROPN
ajst-7806	267	14	li	li	PROPN
ajst-7806	267	15	,	,	PUNCT
ajst-7806	267	16	guodong	guodong	PROPN
ajst-7806	267	17	zhang	zhang	PROPN
ajst-7806	267	18	,	,	PUNCT
ajst-7806	267	19	han	han	PROPN
ajst-7806	267	20	hu	hu	PROPN
ajst-7806	267	21	,	,	PUNCT
ajst-7806	267	22	and	and	CCONJ
ajst-7806	267	23	yichen	yichen	PROPN
ajst-7806	267	24	wei	wei	PROPN
ajst-7806	267	25	.	.	PUNCT
ajst-7806	267	26	deformable	deformable	ADJ
ajst-7806	267	27	convolutional	convolutional	ADJ
ajst-7806	267	28	networks	network	NOUN
ajst-7806	267	29	.	.	PUNCT
ajst-7806	268	1	in	in	ADP
ajst-7806	268	2	iccv	iccv	PROPN
ajst-7806	268	3	,	,	PUNCT
ajst-7806	268	4	pages	page	NOUN
ajst-7806	268	5	764–773	764–773	NUM
ajst-7806	268	6	,	,	PUNCT
ajst-7806	268	7	2017	2017	NUM
ajst-7806	268	8	.	.	PUNCT
ajst-7806	269	1	[	[	X
ajst-7806	269	2	5	5	X
ajst-7806	269	3	]	]	X
ajst-7806	269	4	h.	h.	NOUN
ajst-7806	269	5	bilen	bilen	PROPN
ajst-7806	269	6	and	and	CCONJ
ajst-7806	269	7	a.	a.	NOUN
ajst-7806	269	8	vedaldi	vedaldi	NOUN
ajst-7806	269	9	.	.	PUNCT
ajst-7806	270	1	weakly	weakly	ADV
ajst-7806	270	2	supervised	supervise	VERB
ajst-7806	270	3	deep	deep	ADJ
ajst-7806	270	4	detection	detection	NOUN
ajst-7806	270	5	networks	network	NOUN
ajst-7806	270	6	.	.	PUNCT
ajst-7806	271	1	in	in	ADP
ajst-7806	271	2	proc	proc	NOUN
ajst-7806	271	3	.	.	PUNCT
ajst-7806	272	1	cvpr	cvpr	NOUN
ajst-7806	272	2	,	,	PUNCT
ajst-7806	272	3	2016	2016	NUM
ajst-7806	272	4	.	.	PUNCT
ajst-7806	273	1	[	[	X
ajst-7806	273	2	6	6	NUM
ajst-7806	273	3	]	]	PUNCT
ajst-7806	273	4	mark	mark	PROPN
ajst-7806	273	5	everingham	everingham	PROPN
ajst-7806	273	6	,	,	PUNCT
ajst-7806	273	7	sm	sm	PROPN
ajst-7806	273	8	ali	ali	PROPN
ajst-7806	273	9	eslami	eslami	PROPN
ajst-7806	273	10	,	,	PUNCT
ajst-7806	273	11	luc	luc	PROPN
ajst-7806	273	12	van	van	PROPN
ajst-7806	273	13	gool	gool	PROPN
ajst-7806	273	14	,	,	PUNCT
ajst-7806	273	15	christopher	christopher	PROPN
ajst-7806	273	16	ki	ki	PROPN
ajst-7806	273	17	williams	williams	PROPN
ajst-7806	273	18	,	,	PUNCT
ajst-7806	273	19	john	john	PROPN
ajst-7806	273	20	winn	winn	PROPN
ajst-7806	273	21	,	,	PUNCT
ajst-7806	273	22	and	and	CCONJ
ajst-7806	273	23	andrew	andrew	PROPN
ajst-7806	273	24	zisserman	zisserman	PROPN
ajst-7806	273	25	.	.	PUNCT
ajst-7806	274	1	the	the	DET
ajst-7806	274	2	pascal	pascal	ADJ
ajst-7806	274	3	visual	visual	ADJ
ajst-7806	274	4	object	object	NOUN
ajst-7806	274	5	classes	class	NOUN
ajst-7806	274	6	challenge	challenge	VERB
ajst-7806	274	7	:	:	PUNCT
ajst-7806	274	8	a	a	DET
ajst-7806	274	9	retrospective	retrospective	NOUN
ajst-7806	274	10	.	.	PUNCT
ajst-7806	275	1	international	international	ADJ
ajst-7806	275	2	journal	journal	NOUN
ajst-7806	275	3	of	of	ADP
ajst-7806	275	4	computer	computer	NOUN
ajst-7806	275	5	vision	vision	NOUN
ajst-7806	275	6	,	,	PUNCT
ajst-7806	275	7	111(1):98–136	111(1):98–136	NUM
ajst-7806	275	8	,	,	PUNCT
ajst-7806	275	9	2015	2015	NUM
ajst-7806	275	10	.	.	PUNCT
ajst-7806	276	1	[	[	X
ajst-7806	276	2	7	7	NUM
ajst-7806	276	3	]	]	PUNCT
ajst-7806	276	4	mark	mark	PROPN
ajst-7806	276	5	everingham	everingham	PROPN
ajst-7806	276	6	,	,	PUNCT
ajst-7806	276	7	luc	luc	PROPN
ajst-7806	276	8	van	van	PROPN
ajst-7806	276	9	gool	gool	PROPN
ajst-7806	276	10	,	,	PUNCT
ajst-7806	276	11	christopher	christopher	PROPN
ajst-7806	276	12	ki	ki	PROPN
ajst-7806	276	13	williams	williams	PROPN
ajst-7806	276	14	,	,	PUNCT
ajst-7806	276	15	john	john	PROPN
ajst-7806	276	16	winn	winn	PROPN
ajst-7806	276	17	,	,	PUNCT
ajst-7806	276	18	and	and	CCONJ
ajst-7806	276	19	andrew	andrew	PROPN
ajst-7806	276	20	zisserman	zisserman	PROPN
ajst-7806	276	21	.	.	PUNCT
ajst-7806	277	1	the	the	DET
ajst-7806	277	2	pascal	pascal	ADJ
ajst-7806	277	3	visual	visual	ADJ
ajst-7806	277	4	object	object	NOUN
ajst-7806	277	5	classes	class	NOUN
ajst-7806	277	6	(	(	PUNCT
ajst-7806	277	7	voc	voc	NOUN
ajst-7806	277	8	)	)	PUNCT
ajst-7806	277	9	challenge	challenge	NOUN
ajst-7806	277	10	.	.	PUNCT
ajst-7806	278	1	ijcv	ijcv	NOUN
ajst-7806	278	2	,	,	PUNCT
ajst-7806	278	3	88(2):303–338	88(2):303–338	NUM
ajst-7806	278	4	,	,	PUNCT
ajst-7806	278	5	2010	2010	NUM
ajst-7806	278	6	.	.	PUNCT
ajst-7806	279	1	[	[	X
ajst-7806	279	2	8	8	NUM
ajst-7806	279	3	]	]	PUNCT
ajst-7806	279	4	ross	ross	PROPN
ajst-7806	279	5	girshick	girshick	PROPN
ajst-7806	279	6	.	.	PUNCT
ajst-7806	280	1	fast	fast	ADJ
ajst-7806	280	2	r	r	NOUN
ajst-7806	280	3	-	-	PUNCT
ajst-7806	280	4	cnn	cnn	NOUN
ajst-7806	280	5	.	.	PUNCT
ajst-7806	281	1	in	in	ADP
ajst-7806	281	2	proceedings	proceeding	NOUN
ajst-7806	281	3	of	of	ADP
ajst-7806	281	4	the	the	DET
ajst-7806	281	5	ieee	ieee	NOUN
ajst-7806	281	6	international	international	PROPN
ajst-7806	281	7	conference	conference	NOUN
ajst-7806	281	8	on	on	ADP
ajst-7806	281	9	computer	computer	NOUN
ajst-7806	281	10	vision	vision	NOUN
ajst-7806	281	11	,	,	PUNCT
ajst-7806	281	12	pages	page	NOUN
ajst-7806	281	13	1440–1448	1440–1448	NUM
ajst-7806	281	14	,	,	PUNCT
ajst-7806	281	15	2015	2015	NUM
ajst-7806	281	16	.	.	PUNCT
ajst-7806	282	1	[	[	X
ajst-7806	282	2	9	9	NUM
ajst-7806	282	3	]	]	PUNCT
ajst-7806	282	4	ross	ross	PROPN
ajst-7806	282	5	girshick	girshick	PROPN
ajst-7806	282	6	,	,	PUNCT
ajst-7806	282	7	jeff	jeff	PROPN
ajst-7806	282	8	donahue	donahue	PROPN
ajst-7806	282	9	,	,	PUNCT
ajst-7806	282	10	trevor	trevor	PROPN
ajst-7806	282	11	darrell	darrell	PROPN
ajst-7806	282	12	,	,	PUNCT
ajst-7806	282	13	and	and	CCONJ
ajst-7806	282	14	jitendra	jitendra	PROPN
ajst-7806	282	15	malik	malik	PROPN
ajst-7806	282	16	.	.	PUNCT
ajst-7806	283	1	rich	rich	ADJ
ajst-7806	283	2	feature	feature	NOUN
ajst-7806	283	3	hierarchies	hierarchy	NOUN
ajst-7806	283	4	for	for	ADP
ajst-7806	283	5	accurate	accurate	ADJ
ajst-7806	283	6	object	object	NOUN
ajst-7806	283	7	detection	detection	NOUN
ajst-7806	283	8	and	and	CCONJ
ajst-7806	283	9	semantic	semantic	ADJ
ajst-7806	283	10	segmentation	segmentation	NOUN
ajst-7806	283	11	.	.	PUNCT
ajst-7806	284	1	in	in	ADP
ajst-7806	284	2	proceedings	proceeding	NOUN
ajst-7806	284	3	of	of	ADP
ajst-7806	284	4	the	the	DET
ajst-7806	284	5	ieee	ieee	NOUN
ajst-7806	284	6	conference	conference	NOUN
ajst-7806	284	7	on	on	ADP
ajst-7806	284	8	computer	computer	NOUN
ajst-7806	284	9	vision	vision	NOUN
ajst-7806	284	10	and	and	CCONJ
ajst-7806	284	11	pattern	pattern	NOUN
ajst-7806	284	12	recognition	recognition	NOUN
ajst-7806	284	13	,	,	PUNCT
ajst-7806	284	14	pages	page	NOUN
ajst-7806	284	15	580–587	580–587	NUM
ajst-7806	284	16	,	,	PUNCT
ajst-7806	284	17	2014	2014	NUM
ajst-7806	284	18	.	.	PUNCT
ajst-7806	285	1	[	[	X
ajst-7806	285	2	10	10	NUM
ajst-7806	285	3	]	]	X
ajst-7806	285	4	kaiming	kaime	VERB
ajst-7806	285	5	he	he	PRON
ajst-7806	285	6	,	,	PUNCT
ajst-7806	285	7	xiangyu	xiangyu	PROPN
ajst-7806	285	8	zhang	zhang	PROPN
ajst-7806	285	9	,	,	PUNCT
ajst-7806	285	10	shaoqing	shaoqe	VERB
ajst-7806	285	11	ren	ren	PROPN
ajst-7806	285	12	,	,	PUNCT
ajst-7806	285	13	and	and	CCONJ
ajst-7806	285	14	jian	jian	PROPN
ajst-7806	285	15	sun	sun	PROPN
ajst-7806	285	16	.	.	PUNCT
ajst-7806	286	1	deep	deep	ADJ
ajst-7806	286	2	residual	residual	ADJ
ajst-7806	286	3	learning	learning	NOUN
ajst-7806	286	4	for	for	ADP
ajst-7806	286	5	image	image	NOUN
ajst-7806	286	6	recognition	recognition	NOUN
ajst-7806	286	7	.	.	PUNCT
ajst-7806	287	1	in	in	ADP
ajst-7806	287	2	proceedings	proceeding	NOUN
ajst-7806	287	3	of	of	ADP
ajst-7806	287	4	the	the	DET
ajst-7806	287	5	ieee	ieee	NOUN
ajst-7806	287	6	conference	conference	NOUN
ajst-7806	287	7	on	on	ADP
ajst-7806	287	8	computer	computer	NOUN
ajst-7806	287	9	vision	vision	NOUN
ajst-7806	287	10	and	and	CCONJ
ajst-7806	287	11	pattern	pattern	NOUN
ajst-7806	287	12	recognition	recognition	NOUN
ajst-7806	287	13	,	,	PUNCT
ajst-7806	287	14	pages	page	NOUN
ajst-7806	287	15	770–778	770–778	NUM
ajst-7806	287	16	,	,	PUNCT
ajst-7806	287	17	2016	2016	NUM
ajst-7806	287	18	.	.	PUNCT
ajst-7806	288	1	[	[	X
ajst-7806	288	2	11	11	NUM
ajst-7806	288	3	]	]	X
ajst-7806	288	4	zeng	zeng	PROPN
ajst-7806	288	5	z	z	PROPN
ajst-7806	288	6	,	,	PUNCT
ajst-7806	288	7	liu	liu	PROPN
ajst-7806	288	8	b	b	PROPN
ajst-7806	288	9	,	,	PUNCT
ajst-7806	288	10	fu	fu	PROPN
ajst-7806	288	11	j	j	PROPN
ajst-7806	288	12	,	,	PUNCT
ajst-7806	288	13	et	et	PROPN
ajst-7806	288	14	al	al	PROPN
ajst-7806	288	15	.	.	PUNCT
ajst-7806	288	16	wsod2	wsod2	NOUN
ajst-7806	288	17	:	:	PUNCT
ajst-7806	288	18	learning	learn	VERB
ajst-7806	288	19	bottom	bottom	NOUN
ajst-7806	288	20	-	-	PUNCT
ajst-7806	288	21	up	up	NOUN
ajst-7806	288	22	and	and	CCONJ
ajst-7806	288	23	topdown	topdown	ADJ
ajst-7806	288	24	objectness	objectness	NOUN
ajst-7806	288	25	distillation	distillation	NOUN
ajst-7806	288	26	for	for	ADP
ajst-7806	288	27	weakly	weakly	ADV
ajst-7806	288	28	-	-	PUNCT
ajst-7806	288	29	supervised	supervise	VERB
ajst-7806	288	30	object	object	NOUN
ajst-7806	288	31	detection[c]//proceedings	detection[c]//proceeding	NOUN
ajst-7806	288	32	of	of	ADP
ajst-7806	288	33	the	the	DET
ajst-7806	288	34	ieee	ieee	NOUN
ajst-7806	288	35	/	/	SYM
ajst-7806	288	36	cvf	cvf	NOUN
ajst-7806	288	37	international	international	ADJ
ajst-7806	288	38	conference	conference	NOUN
ajst-7806	288	39	on	on	ADP
ajst-7806	288	40	computer	computer	NOUN
ajst-7806	288	41	vision	vision	NOUN
ajst-7806	288	42	.	.	PUNCT
ajst-7806	289	1	2019	2019	NUM
ajst-7806	289	2	:	:	PUNCT
ajst-7806	289	3	8292	8292	NUM
ajst-7806	289	4	-	-	SYM
ajst-7806	289	5	8300	8300	NUM
ajst-7806	289	6	.	.	PUNCT
ajst-7806	290	1	[	[	X
ajst-7806	290	2	12	12	NUM
ajst-7806	290	3	]	]	X
ajst-7806	290	4	diba	diba	PROPN
ajst-7806	290	5	,	,	PUNCT
ajst-7806	290	6	a.	a.	PROPN
ajst-7806	290	7	,	,	PUNCT
ajst-7806	290	8	sharma	sharma	PROPN
ajst-7806	290	9	,	,	PUNCT
ajst-7806	290	10	v.	v.	PROPN
ajst-7806	290	11	,	,	PUNCT
ajst-7806	290	12	pazandeh	pazandeh	PROPN
ajst-7806	290	13	,	,	PUNCT
ajst-7806	290	14	a.	a.	NOUN
ajst-7806	290	15	,	,	PUNCT
ajst-7806	290	16	pirsiavash	pirsiavash	ADJ
ajst-7806	290	17	,	,	PUNCT
ajst-7806	290	18	h.	h.	PROPN
ajst-7806	290	19	,	,	PUNCT
ajst-7806	290	20	van	van	PROPN
ajst-7806	290	21	gool	gool	PROPN
ajst-7806	290	22	,	,	PUNCT
ajst-7806	290	23	l.	l.	PROPN
ajst-7806	290	24	:	:	PUNCT
ajst-7806	290	25	weakly	weakly	ADV
ajst-7806	290	26	supervised	supervised	ADJ
ajst-7806	290	27	cascaded	cascade	VERB
ajst-7806	290	28	convolutional	convolutional	ADJ
ajst-7806	290	29	networks	network	NOUN
ajst-7806	290	30	.	.	PUNCT
ajst-7806	291	1	in	in	ADP
ajst-7806	291	2	:	:	PUNCT
ajst-7806	291	3	proceedings	proceeding	NOUN
ajst-7806	291	4	of	of	ADP
ajst-7806	291	5	the	the	DET
ajst-7806	291	6	ieee	ieee	NOUN
ajst-7806	291	7	conference	conference	NOUN
ajst-7806	291	8	on	on	ADP
ajst-7806	291	9	computer	computer	NOUN
ajst-7806	291	10	vision	vision	NOUN
ajst-7806	291	11	and	and	CCONJ
ajst-7806	291	12	pattern	pattern	NOUN
ajst-7806	291	13	recognition	recognition	NOUN
ajst-7806	291	14	(	(	PUNCT
ajst-7806	291	15	cvpr	cvpr	NOUN
ajst-7806	291	16	)	)	PUNCT
ajst-7806	291	17	(	(	PUNCT
ajst-7806	291	18	2017	2017	NUM
ajst-7806	291	19	)	)	PUNCT
ajst-7806	291	20	.	.	PUNCT
ajst-7806	292	1	[	[	X
ajst-7806	292	2	13	13	NUM
ajst-7806	292	3	]	]	PUNCT
ajst-7806	292	4	alex	alex	PROPN
ajst-7806	292	5	krizhevsky	krizhevsky	PROPN
ajst-7806	292	6	,	,	PUNCT
ajst-7806	292	7	ilya	ilya	PROPN
ajst-7806	292	8	sutskever	sutskever	VERB
ajst-7806	292	9	,	,	PUNCT
ajst-7806	292	10	and	and	CCONJ
ajst-7806	292	11	geoffrey	geoffrey	PROPN
ajst-7806	292	12	e	e	PROPN
ajst-7806	292	13	hinton	hinton	PROPN
ajst-7806	292	14	.	.	PUNCT
ajst-7806	293	1	imagenet	imagenet	PROPN
ajst-7806	293	2	classification	classification	NOUN
ajst-7806	293	3	with	with	ADP
ajst-7806	293	4	deep	deep	ADJ
ajst-7806	293	5	convolutional	convolutional	ADJ
ajst-7806	293	6	neural	neural	ADJ
ajst-7806	293	7	networks	network	NOUN
ajst-7806	293	8	.	.	PUNCT
ajst-7806	294	1	in	in	ADP
ajst-7806	294	2	advances	advance	NOUN
ajst-7806	294	3	in	in	ADP
ajst-7806	294	4	neural	neural	ADJ
ajst-7806	294	5	information	information	NOUN
ajst-7806	294	6	processing	processing	NOUN
ajst-7806	294	7	systems	system	NOUN
ajst-7806	294	8	,	,	PUNCT
ajst-7806	294	9	pages	page	NOUN
ajst-7806	294	10	1097–1105	1097–1105	NUM
ajst-7806	294	11	,	,	PUNCT
ajst-7806	294	12	2012	2012	NUM
ajst-7806	294	13	.	.	PUNCT
ajst-7806	295	1	[	[	X
ajst-7806	295	2	14	14	NUM
ajst-7806	295	3	]	]	X
ajst-7806	295	4	yann	yann	PROPN
ajst-7806	295	5	lecun	lecun	PROPN
ajst-7806	295	6	,	,	PUNCT
ajst-7806	295	7	l´eon	l´eon	PROPN
ajst-7806	295	8	bottou	bottou	PROPN
ajst-7806	295	9	,	,	PUNCT
ajst-7806	295	10	yoshua	yoshua	PROPN
ajst-7806	295	11	bengio	bengio	PROPN
ajst-7806	295	12	,	,	PUNCT
ajst-7806	295	13	patrick	patrick	PROPN
ajst-7806	295	14	haffner	haffner	PROPN
ajst-7806	295	15	,	,	PUNCT
ajst-7806	295	16	et	et	PROPN
ajst-7806	295	17	al	al	PROPN
ajst-7806	295	18	.	.	PUNCT
ajst-7806	296	1	gradient	gradient	NOUN
ajst-7806	296	2	-	-	PUNCT
ajst-7806	296	3	based	base	VERB
ajst-7806	296	4	learning	learning	NOUN
ajst-7806	296	5	applied	apply	VERB
ajst-7806	296	6	to	to	ADP
ajst-7806	296	7	document	document	NOUN
ajst-7806	296	8	recognition	recognition	NOUN
ajst-7806	296	9	.	.	PUNCT
ajst-7806	297	1	proceedings	proceeding	NOUN
ajst-7806	297	2	of	of	ADP
ajst-7806	297	3	the	the	DET
ajst-7806	297	4	ieee	ieee	NOUN
ajst-7806	297	5	,	,	PUNCT
ajst-7806	297	6	86(11):2278–2324	86(11):2278–2324	NUM
ajst-7806	297	7	,	,	PUNCT
ajst-7806	297	8	1998	1998	NUM
ajst-7806	297	9	.	.	PUNCT
ajst-7806	298	1	[	[	X
ajst-7806	298	2	15	15	NUM
ajst-7806	298	3	]	]	X
ajst-7806	298	4	jie	jie	PROPN
ajst-7806	298	5	,	,	PUNCT
ajst-7806	298	6	z.	z.	PROPN
ajst-7806	298	7	,	,	PUNCT
ajst-7806	298	8	wei	wei	PROPN
ajst-7806	298	9	,	,	PUNCT
ajst-7806	298	10	y.	y.	PROPN
ajst-7806	298	11	,	,	PUNCT
ajst-7806	298	12	jin	jin	NOUN
ajst-7806	298	13	,	,	PUNCT
ajst-7806	298	14	x.	x.	PROPN
ajst-7806	298	15	,	,	PUNCT
ajst-7806	298	16	feng	feng	PROPN
ajst-7806	298	17	,	,	PUNCT
ajst-7806	298	18	j.	j.	PROPN
ajst-7806	298	19	,	,	PUNCT
ajst-7806	298	20	liu	liu	PROPN
ajst-7806	298	21	,	,	PUNCT
ajst-7806	298	22	w.	w.	PROPN
ajst-7806	298	23	:	:	PUNCT
ajst-7806	298	24	deep	deep	ADJ
ajst-7806	298	25	self	self	NOUN
ajst-7806	298	26	-	-	PUNCT
ajst-7806	298	27	taught	teach	VERB
ajst-7806	298	28	learning	learning	NOUN
ajst-7806	298	29	for	for	ADP
ajst-7806	298	30	weakly	weakly	ADJ
ajst-7806	298	31	supervised	supervised	ADJ
ajst-7806	298	32	object	object	NOUN
ajst-7806	298	33	localization	localization	NOUN
ajst-7806	298	34	.	.	PUNCT
ajst-7806	299	1	in	in	ADP
ajst-7806	299	2	:	:	PUNCT
ajst-7806	299	3	proceedings	proceeding	NOUN
ajst-7806	299	4	of	of	ADP
ajst-7806	299	5	the	the	DET
ajst-7806	299	6	conference	conference	NOUN
ajst-7806	299	7	on	on	ADP
ajst-7806	299	8	computer	computer	NOUN
ajst-7806	299	9	vision	vision	NOUN
ajst-7806	299	10	and	and	CCONJ
ajst-7806	299	11	pattern	pattern	NOUN
ajst-7806	299	12	recognition	recognition	NOUN
ajst-7806	299	13	(	(	PUNCT
ajst-7806	299	14	cvpr	cvpr	NOUN
ajst-7806	299	15	)	)	PUNCT
ajst-7806	299	16	(	(	PUNCT
ajst-7806	299	17	2017	2017	NUM
ajst-7806	299	18	)	)	PUNCT
ajst-7806	299	19	.	.	PUNCT
ajst-7806	300	1	[	[	X
ajst-7806	300	2	16	16	NUM
ajst-7806	300	3	]	]	X
ajst-7806	300	4	ren	ren	PROPN
ajst-7806	300	5	,	,	PUNCT
ajst-7806	300	6	z.	z.	PROPN
ajst-7806	300	7	,	,	PUNCT
ajst-7806	300	8	yu	yu	PROPN
ajst-7806	300	9	,	,	PUNCT
ajst-7806	300	10	z.	z.	PROPN
ajst-7806	300	11	,	,	PUNCT
ajst-7806	300	12	yang	yang	PROPN
ajst-7806	300	13	,	,	PUNCT
ajst-7806	300	14	x.	x.	PROPN
ajst-7806	300	15	,	,	PUNCT
ajst-7806	300	16	liu	liu	PROPN
ajst-7806	300	17	,	,	PUNCT
ajst-7806	300	18	m.y	m.y	PROPN
ajst-7806	300	19	.	.	PROPN
ajst-7806	300	20	,	,	PUNCT
ajst-7806	300	21	lee	lee	PROPN
ajst-7806	300	22	,	,	PUNCT
ajst-7806	300	23	y.j	y.j	PROPN
ajst-7806	300	24	.	.	PROPN
ajst-7806	300	25	,	,	PUNCT
ajst-7806	300	26	schwing	schwing	NOUN
ajst-7806	300	27	,	,	PUNCT
ajst-7806	300	28	a.g	a.g	PROPN
ajst-7806	300	29	.	.	PROPN
ajst-7806	300	30	,	,	PUNCT
ajst-7806	300	31	kautz	kautz	PROPN
ajst-7806	300	32	,	,	PUNCT
ajst-7806	300	33	j.	j.	PROPN
ajst-7806	300	34	:	:	PUNCT
ajst-7806	300	35	instanceaware	instanceaware	ADJ
ajst-7806	300	36	,	,	PUNCT
ajst-7806	300	37	context	context	NOUN
ajst-7806	300	38	-	-	PUNCT
ajst-7806	300	39	focused	focused	ADJ
ajst-7806	300	40	,	,	PUNCT
ajst-7806	300	41	and	and	CCONJ
ajst-7806	300	42	memoryefficient	memoryefficient	NOUN
ajst-7806	300	43	weakly	weakly	ADV
ajst-7806	300	44	supervised	supervised	ADJ
ajst-7806	300	45	object	object	NOUN
ajst-7806	300	46	detection	detection	NOUN
ajst-7806	300	47	.	.	PUNCT
ajst-7806	301	1	in	in	ADP
ajst-7806	301	2	:	:	PUNCT
ajst-7806	301	3	proceedings	proceeding	NOUN
ajst-7806	301	4	of	of	ADP
ajst-7806	301	5	the	the	DET
ajst-7806	301	6	ieee	ieee	NOUN
ajst-7806	301	7	/	/	SYM
ajst-7806	301	8	cvf	cvf	NOUN
ajst-7806	301	9	conference	conference	NOUN
ajst-7806	301	10	on	on	ADP
ajst-7806	301	11	computer	computer	NOUN
ajst-7806	301	12	vision	vision	NOUN
ajst-7806	301	13	and	and	CCONJ
ajst-7806	301	14	pattern	pattern	NOUN
ajst-7806	301	15	recognition	recognition	NOUN
ajst-7806	301	16	(	(	PUNCT
ajst-7806	301	17	cvpr	cvpr	NOUN
ajst-7806	301	18	)	)	PUNCT
ajst-7806	301	19	(	(	PUNCT
ajst-7806	301	20	2020	2020	NUM
ajst-7806	301	21	)	)	PUNCT
ajst-7806	301	22	.	.	PUNCT
ajst-7806	302	1	[	[	X
ajst-7806	302	2	17	17	NUM
ajst-7806	302	3	]	]	X
ajst-7806	302	4	tsung	tsung	PROPN
ajst-7806	302	5	-	-	PUNCT
ajst-7806	302	6	yi	yi	PROPN
ajst-7806	302	7	lin	lin	PROPN
ajst-7806	302	8	,	,	PUNCT
ajst-7806	302	9	piotr	piotr	PROPN
ajst-7806	302	10	doll´ar	doll´ar	PROPN
ajst-7806	302	11	,	,	PUNCT
ajst-7806	302	12	ross	ross	PROPN
ajst-7806	302	13	girshick	girshick	PROPN
ajst-7806	302	14	,	,	PUNCT
ajst-7806	302	15	kaiming	kaime	VERB
ajst-7806	302	16	he	he	PRON
ajst-7806	302	17	,	,	PUNCT
ajst-7806	302	18	bharath	bharath	PROPN
ajst-7806	302	19	hariharan	hariharan	PROPN
ajst-7806	302	20	,	,	PUNCT
ajst-7806	302	21	and	and	CCONJ
ajst-7806	302	22	serge	serge	PROPN
ajst-7806	302	23	belongie	belongie	PROPN
ajst-7806	302	24	.	.	PUNCT
ajst-7806	303	1	feature	feature	NOUN
ajst-7806	303	2	pyramid	pyramid	NOUN
ajst-7806	303	3	networks	network	NOUN
ajst-7806	303	4	for	for	ADP
ajst-7806	303	5	object	object	NOUN
ajst-7806	303	6	detection	detection	NOUN
ajst-7806	303	7	.	.	PUNCT
ajst-7806	304	1	in	in	ADP
ajst-7806	304	2	proceedings	proceeding	NOUN
ajst-7806	304	3	of	of	ADP
ajst-7806	304	4	the	the	DET
ajst-7806	304	5	ieee	ieee	NOUN
ajst-7806	304	6	conference	conference	NOUN
ajst-7806	304	7	on	on	ADP
ajst-7806	304	8	computer	computer	NOUN
ajst-7806	304	9	vision	vision	NOUN
ajst-7806	304	10	and	and	CCONJ
ajst-7806	304	11	pattern	pattern	NOUN
ajst-7806	304	12	recognition	recognition	NOUN
ajst-7806	304	13	,	,	PUNCT
ajst-7806	304	14	pages	page	NOUN
ajst-7806	304	15	2117–2125	2117–2125	NUM
ajst-7806	304	16	,	,	PUNCT
ajst-7806	304	17	2017	2017	NUM
ajst-7806	304	18	.	.	PUNCT
ajst-7806	305	1	[	[	X
ajst-7806	305	2	18	18	NUM
ajst-7806	305	3	]	]	PUNCT
ajst-7806	305	4	kaiming	kaime	VERB
ajst-7806	305	5	he	he	PRON
ajst-7806	305	6	,	,	PUNCT
ajst-7806	305	7	georgia	georgia	PROPN
ajst-7806	305	8	gkioxari	gkioxari	NOUN
ajst-7806	305	9	,	,	PUNCT
ajst-7806	305	10	piotr	piotr	PROPN
ajst-7806	305	11	doll´ar	doll´ar	NOUN
ajst-7806	305	12	,	,	PUNCT
ajst-7806	305	13	and	and	CCONJ
ajst-7806	305	14	ross	ross	PROPN
ajst-7806	305	15	girshick	girshick	PROPN
ajst-7806	305	16	.	.	PUNCT
ajst-7806	306	1	mask	mask	VERB
ajst-7806	306	2	r	r	NOUN
ajst-7806	306	3	-	-	PUNCT
ajst-7806	306	4	cnn	cnn	PROPN
ajst-7806	306	5	.	.	PUNCT
ajst-7806	307	1	in	in	ADP
ajst-7806	307	2	proc	proc	PROPN
ajst-7806	307	3	.	.	PUNCT
ajst-7806	308	1	iccv	iccv	PROPN
ajst-7806	308	2	,	,	PUNCT
ajst-7806	308	3	2017	2017	NUM
ajst-7806	308	4	.	.	PUNCT
ajst-7806	309	1	148	148	NUM
ajst-7806	309	2	[	[	SYM
ajst-7806	309	3	19	19	NUM
ajst-7806	309	4	]	]	PUNCT
ajst-7806	309	5	wei	wei	PROPN
ajst-7806	309	6	liu	liu	PROPN
ajst-7806	309	7	,	,	PUNCT
ajst-7806	309	8	dragomir	dragomir	ADJ
ajst-7806	309	9	anguelov	anguelov	NOUN
ajst-7806	309	10	,	,	PUNCT
ajst-7806	309	11	dumitru	dumitru	PROPN
ajst-7806	309	12	erhan	erhan	NOUN
ajst-7806	309	13	,	,	PUNCT
ajst-7806	309	14	christian	christian	PROPN
ajst-7806	309	15	szegedy	szegedy	PROPN
ajst-7806	309	16	,	,	PUNCT
ajst-7806	309	17	scott	scott	PROPN
ajst-7806	309	18	reed	reed	PROPN
ajst-7806	309	19	,	,	PUNCT
ajst-7806	309	20	cheng	cheng	PROPN
ajst-7806	309	21	-	-	PUNCT
ajst-7806	309	22	yang	yang	PROPN
ajst-7806	309	23	fu	fu	PROPN
ajst-7806	309	24	,	,	PUNCT
ajst-7806	309	25	and	and	CCONJ
ajst-7806	309	26	alexander	alexander	PROPN
ajst-7806	309	27	c	c	PROPN
ajst-7806	309	28	berg	berg	PROPN
ajst-7806	309	29	.	.	PUNCT
ajst-7806	310	1	ssd	ssd	PROPN
ajst-7806	310	2	:	:	PUNCT
ajst-7806	310	3	single	single	ADJ
ajst-7806	310	4	shot	shot	NOUN
ajst-7806	310	5	multibox	multibox	NOUN
ajst-7806	310	6	detector	detector	NOUN
ajst-7806	310	7	.	.	PUNCT
ajst-7806	311	1	in	in	ADP
ajst-7806	311	2	european	european	ADJ
ajst-7806	311	3	conference	conference	PROPN
ajst-7806	311	4	on	on	ADP
ajst-7806	311	5	computer	computer	NOUN
ajst-7806	311	6	vision	vision	NOUN
ajst-7806	311	7	,	,	PUNCT
ajst-7806	311	8	pages	page	NOUN
ajst-7806	311	9	21–37	21–37	NUM
ajst-7806	311	10	.	.	PUNCT
ajst-7806	311	11	springer	springer	NOUN
ajst-7806	311	12	,	,	PUNCT
ajst-7806	311	13	2016	2016	NUM
ajst-7806	311	14	.	.	PUNCT
ajst-7806	312	1	[	[	X
ajst-7806	312	2	20	20	NUM
ajst-7806	312	3	]	]	PUNCT
ajst-7806	312	4	alina	alina	PROPN
ajst-7806	312	5	kuznetsova	kuznetsova	PROPN
ajst-7806	312	6	,	,	PUNCT
ajst-7806	312	7	hassan	hassan	PROPN
ajst-7806	312	8	rom	rom	PROPN
ajst-7806	312	9	,	,	PUNCT
ajst-7806	312	10	neil	neil	PROPN
ajst-7806	312	11	alldrin	alldrin	PROPN
ajst-7806	312	12	,	,	PUNCT
ajst-7806	312	13	jasper	jasper	NOUN
ajst-7806	312	14	uijlings	uijling	NOUN
ajst-7806	312	15	,	,	PUNCT
ajst-7806	312	16	ivan	ivan	PROPN
ajst-7806	312	17	krasin	krasin	PROPN
ajst-7806	312	18	,	,	PUNCT
ajst-7806	312	19	jordi	jordi	PROPN
ajst-7806	312	20	pont	pont	PROPN
ajst-7806	312	21	-	-	PUNCT
ajst-7806	312	22	tuset	tuset	NOUN
ajst-7806	312	23	,	,	PUNCT
ajst-7806	312	24	shahab	shahab	PROPN
ajst-7806	312	25	kamali	kamali	PROPN
ajst-7806	312	26	,	,	PUNCT
ajst-7806	312	27	stefan	stefan	PROPN
ajst-7806	312	28	popov	popov	PROPN
ajst-7806	312	29	,	,	PUNCT
ajst-7806	312	30	matteo	matteo	PROPN
ajst-7806	312	31	malloci	malloci	PROPN
ajst-7806	312	32	,	,	PUNCT
ajst-7806	312	33	tom	tom	PROPN
ajst-7806	312	34	duerig	duerig	PROPN
ajst-7806	312	35	,	,	PUNCT
ajst-7806	312	36	and	and	CCONJ
ajst-7806	312	37	vittorio	vittorio	PROPN
ajst-7806	312	38	ferrari	ferrari	PROPN
ajst-7806	312	39	.	.	PUNCT
ajst-7806	313	1	the	the	DET
ajst-7806	313	2	open	open	ADJ
ajst-7806	313	3	images	image	NOUN
ajst-7806	313	4	dataset	dataset	ADJ
ajst-7806	313	5	v4	v4	NOUN
ajst-7806	313	6	:	:	PUNCT
ajst-7806	313	7	unified	unified	ADJ
ajst-7806	313	8	image	image	NOUN
ajst-7806	313	9	classification	classification	NOUN
ajst-7806	313	10	,	,	PUNCT
ajst-7806	313	11	object	object	NOUN
ajst-7806	313	12	detection	detection	NOUN
ajst-7806	313	13	,	,	PUNCT
ajst-7806	313	14	and	and	CCONJ
ajst-7806	313	15	visual	visual	ADJ
ajst-7806	313	16	relationship	relationship	NOUN
ajst-7806	313	17	detection	detection	NOUN
ajst-7806	313	18	at	at	ADP
ajst-7806	313	19	scale	scale	NOUN
ajst-7806	313	20	.	.	PUNCT
ajst-7806	314	1	arxiv:1811.00982	arxiv:1811.00982	PROPN
ajst-7806	314	2	,	,	PUNCT
ajst-7806	314	3	2018	2018	NUM
ajst-7806	314	4	.	.	PUNCT
ajst-7806	315	1	[	[	X
ajst-7806	315	2	21	21	NUM
ajst-7806	315	3	]	]	PUNCT
ajst-7806	315	4	shaoqing	shaoqe	VERB
ajst-7806	315	5	ren	ren	PROPN
ajst-7806	315	6	,	,	PUNCT
ajst-7806	315	7	kaiming	kaime	VERB
ajst-7806	315	8	he	he	PRON
ajst-7806	315	9	,	,	PUNCT
ajst-7806	315	10	ross	ross	PROPN
ajst-7806	315	11	girshick	girshick	PROPN
ajst-7806	315	12	,	,	PUNCT
ajst-7806	315	13	and	and	CCONJ
ajst-7806	315	14	jian	jian	PROPN
ajst-7806	315	15	sun	sun	PROPN
ajst-7806	315	16	.	.	PUNCT
ajst-7806	316	1	faster	fast	ADJ
ajst-7806	316	2	r	r	NOUN
ajst-7806	316	3	-	-	PUNCT
ajst-7806	316	4	cnn	cnn	NOUN
ajst-7806	316	5	:	:	PUNCT
ajst-7806	316	6	towards	towards	ADP
ajst-7806	316	7	real	real	ADJ
ajst-7806	316	8	-	-	PUNCT
ajst-7806	316	9	time	time	NOUN
ajst-7806	316	10	object	object	NOUN
ajst-7806	316	11	detection	detection	NOUN
ajst-7806	316	12	with	with	ADP
ajst-7806	316	13	region	region	NOUN
ajst-7806	316	14	proposal	proposal	NOUN
ajst-7806	316	15	networks	network	NOUN
ajst-7806	316	16	.	.	PUNCT
ajst-7806	317	1	in	in	ADP
ajst-7806	317	2	advances	advance	NOUN
ajst-7806	317	3	in	in	ADP
ajst-7806	317	4	neural	neural	ADJ
ajst-7806	317	5	information	information	NOUN
ajst-7806	317	6	processing	processing	NOUN
ajst-7806	317	7	systems	system	NOUN
ajst-7806	317	8	,	,	PUNCT
ajst-7806	317	9	pages	page	NOUN
ajst-7806	317	10	91–99	91–99	NUM
ajst-7806	317	11	,	,	PUNCT
ajst-7806	317	12	2015	2015	NUM
ajst-7806	317	13	.	.	PUNCT
ajst-7806	318	1	[	[	X
ajst-7806	318	2	22	22	NUM
ajst-7806	318	3	]	]	X
ajst-7806	318	4	yang	yang	PROPN
ajst-7806	318	5	k	k	PROPN
ajst-7806	318	6	,	,	PUNCT
ajst-7806	318	7	li	li	PROPN
ajst-7806	318	8	d	d	PROPN
ajst-7806	318	9	,	,	PUNCT
ajst-7806	318	10	dou	dou	PROPN
ajst-7806	318	11	y.	y.	NOUN
ajst-7806	318	12	towards	towards	ADP
ajst-7806	318	13	precise	precise	ADJ
ajst-7806	318	14	end	end	NOUN
ajst-7806	318	15	-	-	PUNCT
ajst-7806	318	16	to	to	ADP
ajst-7806	318	17	-	-	PUNCT
ajst-7806	318	18	end	end	NOUN
ajst-7806	318	19	weakly	weakly	ADJ
ajst-7806	318	20	supervised	supervised	ADJ
ajst-7806	318	21	object	object	NOUN
ajst-7806	318	22	detection	detection	NOUN
ajst-7806	318	23	network[c]//proceedings	network[c]//proceeding	NOUN
ajst-7806	318	24	of	of	ADP
ajst-7806	318	25	the	the	DET
ajst-7806	318	26	ieee	ieee	NOUN
ajst-7806	318	27	/	/	SYM
ajst-7806	318	28	cvf	cvf	NOUN
ajst-7806	318	29	international	international	ADJ
ajst-7806	318	30	conference	conference	NOUN
ajst-7806	318	31	on	on	ADP
ajst-7806	318	32	computer	computer	NOUN
ajst-7806	318	33	vision	vision	NOUN
ajst-7806	318	34	.	.	PUNCT
ajst-7806	319	1	2019	2019	NUM
ajst-7806	319	2	:	:	PUNCT
ajst-7806	319	3	8372	8372	NUM
ajst-7806	319	4	-	-	SYM
ajst-7806	319	5	8381	8381	NUM
ajst-7806	319	6	.	.	PUNCT
ajst-7806	320	1	[	[	X
ajst-7806	320	2	23	23	NUM
ajst-7806	320	3	]	]	X
ajst-7806	320	4	tsung	tsung	PROPN
ajst-7806	320	5	-	-	PUNCT
ajst-7806	320	6	yi	yi	PROPN
ajst-7806	320	7	lin	lin	PROPN
ajst-7806	320	8	,	,	PUNCT
ajst-7806	320	9	michael	michael	PROPN
ajst-7806	320	10	maire	maire	PROPN
ajst-7806	320	11	,	,	PUNCT
ajst-7806	320	12	serge	serge	NOUN
ajst-7806	320	13	belongie	belongie	PROPN
ajst-7806	320	14	,	,	PUNCT
ajst-7806	320	15	james	james	PROPN
ajst-7806	320	16	hays	hays	PROPN
ajst-7806	320	17	,	,	PUNCT
ajst-7806	320	18	pietro	pietro	PROPN
ajst-7806	320	19	perona	perona	PROPN
ajst-7806	320	20	,	,	PUNCT
ajst-7806	320	21	deva	deva	PROPN
ajst-7806	320	22	ramanan	ramanan	PROPN
ajst-7806	320	23	,	,	PUNCT
ajst-7806	320	24	piotr	piotr	PROPN
ajst-7806	320	25	doll´ar	doll´ar	NOUN
ajst-7806	320	26	,	,	PUNCT
ajst-7806	320	27	and	and	CCONJ
ajst-7806	320	28	c	c	PROPN
ajst-7806	320	29	lawrence	lawrence	PROPN
ajst-7806	320	30	zitnick	zitnick	PROPN
ajst-7806	320	31	.	.	PUNCT
ajst-7806	321	1	microsoft	microsoft	PROPN
ajst-7806	321	2	coco	coco	PROPN
ajst-7806	321	3	:	:	PUNCT
ajst-7806	321	4	common	common	ADJ
ajst-7806	321	5	objects	object	NOUN
ajst-7806	321	6	in	in	ADP
ajst-7806	321	7	context	context	NOUN
ajst-7806	321	8	.	.	PUNCT
ajst-7806	322	1	in	in	ADP
ajst-7806	322	2	eccv	eccv	ADJ
ajst-7806	322	3	,	,	PUNCT
ajst-7806	322	4	pages	page	NOUN
ajst-7806	322	5	740–755	740–755	NUM
ajst-7806	322	6	,	,	PUNCT
ajst-7806	322	7	2014	2014	NUM
ajst-7806	322	8	.	.	PUNCT
ajst-7806	323	1	[	[	X
ajst-7806	323	2	24	24	NUM
ajst-7806	323	3	]	]	X
ajst-7806	323	4	olga	olga	PROPN
ajst-7806	323	5	russakovsky	russakovsky	PROPN
ajst-7806	323	6	,	,	PUNCT
ajst-7806	323	7	jia	jia	PROPN
ajst-7806	323	8	deng	deng	PROPN
ajst-7806	323	9	,	,	PUNCT
ajst-7806	323	10	hao	hao	PROPN
ajst-7806	323	11	su	su	PROPN
ajst-7806	323	12	,	,	PUNCT
ajst-7806	323	13	jonathan	jonathan	PROPN
ajst-7806	323	14	krause	krause	PROPN
ajst-7806	323	15	,	,	PUNCT
ajst-7806	323	16	sanjeev	sanjeev	PROPN
ajst-7806	323	17	satheesh	satheesh	PROPN
ajst-7806	323	18	,	,	PUNCT
ajst-7806	323	19	sean	sean	PROPN
ajst-7806	323	20	ma	ma	PROPN
ajst-7806	323	21	,	,	PUNCT
ajst-7806	323	22	zhiheng	zhiheng	PROPN
ajst-7806	323	23	huang	huang	PROPN
ajst-7806	323	24	,	,	PUNCT
ajst-7806	323	25	andrej	andrej	PROPN
ajst-7806	323	26	karpathy	karpathy	PROPN
ajst-7806	323	27	,	,	PUNCT
ajst-7806	323	28	aditya	aditya	PROPN
ajst-7806	323	29	khosla	khosla	PROPN
ajst-7806	323	30	,	,	PUNCT
ajst-7806	323	31	michael	michael	PROPN
ajst-7806	323	32	bernstein	bernstein	PROPN
ajst-7806	323	33	,	,	PUNCT
ajst-7806	323	34	et	et	PROPN
ajst-7806	323	35	al	al	PROPN
ajst-7806	323	36	.	.	PROPN
ajst-7806	323	37	imagenet	imagenet	PROPN
ajst-7806	323	38	large	large	ADJ
ajst-7806	323	39	scale	scale	NOUN
ajst-7806	323	40	visual	visual	ADJ
ajst-7806	323	41	recognition	recognition	NOUN
ajst-7806	323	42	challenge	challenge	NOUN
ajst-7806	323	43	.	.	PUNCT
ajst-7806	324	1	international	international	ADJ
ajst-7806	324	2	journal	journal	NOUN
ajst-7806	324	3	of	of	ADP
ajst-7806	324	4	computer	computer	NOUN
ajst-7806	324	5	vision	vision	NOUN
ajst-7806	324	6	,	,	PUNCT
ajst-7806	324	7	115(3):211–252	115(3):211–252	NUM
ajst-7806	324	8	,	,	PUNCT
ajst-7806	324	9	2015	2015	NUM
ajst-7806	324	10	.	.	PUNCT
ajst-7806	325	1	[	[	X
ajst-7806	325	2	25	25	NUM
ajst-7806	325	3	]	]	X
ajst-7806	325	4	vadim	vadim	PROPN
ajst-7806	325	5	kantorov	kantorov	PROPN
ajst-7806	325	6	,	,	PUNCT
ajst-7806	325	7	maxime	maxime	PROPN
ajst-7806	325	8	oquab	oquab	PROPN
ajst-7806	325	9	,	,	PUNCT
ajst-7806	325	10	minsu	minsu	PROPN
ajst-7806	325	11	cho	cho	PROPN
ajst-7806	325	12	,	,	PUNCT
ajst-7806	325	13	and	and	CCONJ
ajst-7806	325	14	ivan	ivan	PROPN
ajst-7806	325	15	laptev	laptev	PROPN
ajst-7806	325	16	.	.	PUNCT
ajst-7806	326	1	contextlocnet	contextlocnet	VERB
ajst-7806	326	2	:	:	PUNCT
ajst-7806	326	3	context	context	NOUN
ajst-7806	326	4	-	-	PUNCT
ajst-7806	326	5	aware	aware	ADJ
ajst-7806	326	6	deep	deep	ADJ
ajst-7806	326	7	network	network	NOUN
ajst-7806	326	8	models	model	NOUN
ajst-7806	326	9	for	for	ADP
ajst-7806	326	10	weakly	weakly	ADJ
ajst-7806	326	11	supervised	supervised	ADJ
ajst-7806	326	12	localization	localization	NOUN
ajst-7806	326	13	.	.	PUNCT
ajst-7806	327	1	in	in	ADP
ajst-7806	327	2	proc	proc	PROPN
ajst-7806	327	3	.	.	PUNCT
ajst-7806	328	1	eccv	eccv	PROPN
ajst-7806	328	2	,	,	PUNCT
ajst-7806	328	3	2016	2016	NUM
ajst-7806	328	4	.	.	PUNCT
ajst-7806	329	1	[	[	X
ajst-7806	329	2	26	26	NUM
ajst-7806	329	3	]	]	X
ajst-7806	329	4	wan	wan	PROPN
ajst-7806	329	5	f	f	PROPN
ajst-7806	329	6	,	,	PUNCT
ajst-7806	329	7	wei	wei	PROPN
ajst-7806	329	8	p	p	PROPN
ajst-7806	329	9	,	,	PUNCT
ajst-7806	329	10	jiao	jiao	PROPN
ajst-7806	329	11	j	j	PROPN
ajst-7806	329	12	,	,	PUNCT
ajst-7806	329	13	et	et	PROPN
ajst-7806	329	14	al	al	PROPN
ajst-7806	329	15	.	.	PROPN
ajst-7806	329	16	min	min	PROPN
ajst-7806	329	17	-	-	PUNCT
ajst-7806	329	18	entropy	entropy	PROPN
ajst-7806	329	19	latent	latent	NOUN
ajst-7806	329	20	model	model	NOUN
ajst-7806	329	21	for	for	ADP
ajst-7806	329	22	weakly	weakly	ADJ
ajst-7806	329	23	supervised	supervised	ADJ
ajst-7806	329	24	object	object	NOUN
ajst-7806	329	25	detection[c]//proceedings	detection[c]//proceeding	NOUN
ajst-7806	329	26	of	of	ADP
ajst-7806	329	27	the	the	DET
ajst-7806	329	28	ieee	ieee	NOUN
ajst-7806	329	29	conference	conference	NOUN
ajst-7806	329	30	on	on	ADP
ajst-7806	329	31	computer	computer	NOUN
ajst-7806	329	32	vision	vision	NOUN
ajst-7806	329	33	and	and	CCONJ
ajst-7806	329	34	pattern	pattern	NOUN
ajst-7806	329	35	recognition	recognition	NOUN
ajst-7806	329	36	.	.	PUNCT
ajst-7806	330	1	2018	2018	NUM
ajst-7806	330	2	:	:	PUNCT
ajst-7806	330	3	1297	1297	NUM
ajst-7806	330	4	-	-	SYM
ajst-7806	330	5	1306	1306	NUM
ajst-7806	330	6	.	.	PUNCT
ajst-7806	331	1	[	[	X
ajst-7806	331	2	27	27	NUM
ajst-7806	331	3	]	]	X
ajst-7806	331	4	hei	hei	PROPN
ajst-7806	331	5	law	law	PROPN
ajst-7806	331	6	and	and	CCONJ
ajst-7806	331	7	jia	jia	PROPN
ajst-7806	331	8	deng	deng	PROPN
ajst-7806	331	9	.	.	PUNCT
ajst-7806	332	1	cornernet	cornernet	PROPN
ajst-7806	332	2	:	:	PUNCT
ajst-7806	332	3	detecting	detect	VERB
ajst-7806	332	4	objects	object	NOUN
ajst-7806	332	5	as	as	ADP
ajst-7806	332	6	paired	pair	VERB
ajst-7806	332	7	keypoints	keypoint	NOUN
ajst-7806	332	8	.	.	PUNCT
ajst-7806	333	1	in	in	ADP
ajst-7806	333	2	proc	proc	PROPN
ajst-7806	333	3	.	.	PUNCT
ajst-7806	334	1	eccv	eccv	PROPN
ajst-7806	334	2	,	,	PUNCT
ajst-7806	334	3	2018	2018	NUM
ajst-7806	334	4	.	.	PUNCT
ajst-7806	335	1	[	[	X
ajst-7806	335	2	28	28	NUM
ajst-7806	335	3	]	]	X
ajst-7806	335	4	gao	gao	PROPN
ajst-7806	335	5	m	m	PROPN
ajst-7806	335	6	,	,	PUNCT
ajst-7806	335	7	li	li	PROPN
ajst-7806	335	8	a	a	PROPN
ajst-7806	335	9	,	,	PUNCT
ajst-7806	335	10	yu	yu	PROPN
ajst-7806	335	11	r	r	PROPN
ajst-7806	335	12	,	,	PUNCT
ajst-7806	335	13	et	et	PROPN
ajst-7806	335	14	al	al	PROPN
ajst-7806	335	15	.	.	PROPN
ajst-7806	336	1	c	c	X
ajst-7806	336	2	-	-	PUNCT
ajst-7806	336	3	wsl	wsl	ADJ
ajst-7806	336	4	:	:	PUNCT
ajst-7806	336	5	count	count	NOUN
ajst-7806	336	6	-	-	PUNCT
ajst-7806	336	7	guided	guide	VERB
ajst-7806	336	8	weakly	weakly	ADJ
ajst-7806	336	9	supervised	supervised	ADJ
ajst-7806	336	10	localization[c]//proceedings	localization[c]//proceeding	NOUN
ajst-7806	336	11	of	of	ADP
ajst-7806	336	12	the	the	DET
ajst-7806	336	13	european	european	PROPN
ajst-7806	336	14	conference	conference	PROPN
ajst-7806	336	15	on	on	ADP
ajst-7806	336	16	computer	computer	NOUN
ajst-7806	336	17	vision	vision	NOUN
ajst-7806	336	18	(	(	PUNCT
ajst-7806	336	19	eccv	eccv	ADV
ajst-7806	336	20	)	)	PUNCT
ajst-7806	336	21	.	.	PUNCT
ajst-7806	337	1	2018	2018	NUM
ajst-7806	337	2	:	:	PUNCT
ajst-7806	337	3	152	152	NUM
ajst-7806	337	4	-	-	SYM
ajst-7806	337	5	168	168	NUM
ajst-7806	337	6	.	.	PUNCT
ajst-7806	338	1	[	[	X
ajst-7806	338	2	29	29	NUM
ajst-7806	338	3	]	]	X
ajst-7806	338	4	zhou	zhou	PROPN
ajst-7806	338	5	b	b	PROPN
ajst-7806	338	6	,	,	PUNCT
ajst-7806	338	7	khosla	khosla	PROPN
ajst-7806	338	8	a	a	PRON
ajst-7806	338	9	,	,	PUNCT
ajst-7806	338	10	lapedriza	lapedriza	NOUN
ajst-7806	338	11	a	a	PRON
ajst-7806	338	12	,	,	PUNCT
ajst-7806	338	13	et	et	PROPN
ajst-7806	338	14	al	al	PROPN
ajst-7806	338	15	.	.	PUNCT
ajst-7806	339	1	learning	learn	VERB
ajst-7806	339	2	deep	deep	ADJ
ajst-7806	339	3	features	feature	NOUN
ajst-7806	339	4	for	for	ADP
ajst-7806	339	5	discriminative	discriminative	NOUN
ajst-7806	339	6	localization[c]//proceedings	localization[c]//proceeding	NOUN
ajst-7806	339	7	of	of	ADP
ajst-7806	339	8	the	the	DET
ajst-7806	339	9	ieee	ieee	NOUN
ajst-7806	339	10	conference	conference	NOUN
ajst-7806	339	11	on	on	ADP
ajst-7806	339	12	computer	computer	NOUN
ajst-7806	339	13	vision	vision	NOUN
ajst-7806	339	14	and	and	CCONJ
ajst-7806	339	15	pattern	pattern	NOUN
ajst-7806	339	16	recognition	recognition	NOUN
ajst-7806	339	17	.	.	PUNCT
ajst-7806	340	1	2016	2016	NUM
ajst-7806	340	2	:	:	PUNCT
ajst-7806	340	3	2921	2921	NUM
ajst-7806	340	4	-	-	SYM
ajst-7806	340	5	2929	2929	NUM
ajst-7806	340	6	.	.	PUNCT
ajst-7806	341	1	[	[	X
ajst-7806	341	2	30	30	NUM
ajst-7806	341	3	]	]	X
ajst-7806	341	4	uijlings	uijling	NOUN
ajst-7806	341	5	j	j	NOUN
ajst-7806	341	6	r	r	NOUN
ajst-7806	341	7	r	r	PROPN
ajst-7806	341	8	,	,	PUNCT
ajst-7806	341	9	van	van	PROPN
ajst-7806	341	10	de	de	PROPN
ajst-7806	341	11	sande	sande	PROPN
ajst-7806	341	12	k	k	PROPN
ajst-7806	341	13	e	e	PROPN
ajst-7806	341	14	a	a	PROPN
ajst-7806	341	15	,	,	PUNCT
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ajst-7806	341	17	t	t	PROPN
ajst-7806	341	18	,	,	PUNCT
ajst-7806	341	19	et	et	PROPN
ajst-7806	341	20	al	al	PROPN
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ajst-7806	341	23	search	search	NOUN
ajst-7806	341	24	for	for	ADP
ajst-7806	341	25	object	object	NOUN
ajst-7806	341	26	recognition[j	recognition[j	NOUN
ajst-7806	341	27	]	]	PUNCT
ajst-7806	341	28	.	.	PUNCT
ajst-7806	342	1	international	international	ADJ
ajst-7806	342	2	journal	journal	PROPN
ajst-7806	342	3	of	of	ADP
ajst-7806	342	4	computer	computer	NOUN
ajst-7806	342	5	vision	vision	NOUN
ajst-7806	342	6	,	,	PUNCT
ajst-7806	342	7	2013	2013	NUM
ajst-7806	342	8	,	,	PUNCT
ajst-7806	342	9	104	104	NUM
ajst-7806	342	10	:	:	SYM
ajst-7806	342	11	154	154	NUM
ajst-7806	342	12	-	-	SYM
ajst-7806	342	13	171	171	NUM
ajst-7806	342	14	.	.	PUNCT
ajst-7806	343	1	[	[	X
ajst-7806	343	2	31	31	NUM
ajst-7806	343	3	]	]	PUNCT
ajst-7806	343	4	chattopadhay	chattopadhay	PROPN
ajst-7806	343	5	a	a	PROPN
ajst-7806	343	6	,	,	PUNCT
ajst-7806	343	7	sarkar	sarkar	PROPN
ajst-7806	343	8	a	a	X
ajst-7806	343	9	,	,	PUNCT
ajst-7806	343	10	howlader	howlader	NOUN
ajst-7806	343	11	p	p	X
ajst-7806	343	12	,	,	PUNCT
ajst-7806	343	13	et	et	PROPN
ajst-7806	343	14	al	al	PROPN
ajst-7806	343	15	.	.	PROPN
ajst-7806	343	16	grad	grad	PROPN
ajst-7806	343	17	-	-	PUNCT
ajst-7806	343	18	cam++	cam++	PROPN
ajst-7806	343	19	:	:	PUNCT
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ajst-7806	343	22	-	-	PUNCT
ajst-7806	343	23	based	base	VERB
ajst-7806	343	24	visual	visual	ADJ
ajst-7806	343	25	explanations	explanation	NOUN
ajst-7806	343	26	for	for	ADP
ajst-7806	343	27	deep	deep	ADJ
ajst-7806	343	28	convolutional	convolutional	ADJ
ajst-7806	343	29	networks[c]//2018	networks[c]//2018	NUM
ajst-7806	343	30	ieee	ieee	NOUN
ajst-7806	343	31	winter	winter	NOUN
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ajst-7806	343	33	on	on	ADP
ajst-7806	343	34	applications	application	NOUN
ajst-7806	343	35	of	of	ADP
ajst-7806	343	36	computer	computer	NOUN
ajst-7806	343	37	vision	vision	NOUN
ajst-7806	343	38	(	(	PUNCT
ajst-7806	343	39	wacv	wacv	NOUN
ajst-7806	343	40	)	)	PUNCT
ajst-7806	343	41	.	.	PUNCT
ajst-7806	344	1	ieee	ieee	NOUN
ajst-7806	344	2	,	,	PUNCT
ajst-7806	344	3	2018	2018	NUM
ajst-7806	344	4	:	:	PUNCT
ajst-7806	344	5	839847	839847	NUM
ajst-7806	344	6	.	.	PUNCT
ajst-7806	345	1	[	[	X
ajst-7806	345	2	32	32	NUM
ajst-7806	345	3	]	]	PUNCT
ajst-7806	345	4	arun	arun	NOUN
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ajst-7806	345	6	,	,	PUNCT
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ajst-7806	345	15	coefficient	coefficient	NOUN
ajst-7806	345	16	based	base	VERB
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ajst-7806	345	22	the	the	DET
ajst-7806	345	23	ieee	ieee	NOUN
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ajst-7806	345	25	cvf	cvf	NOUN
ajst-7806	345	26	conference	conference	NOUN
ajst-7806	345	27	on	on	ADP
ajst-7806	345	28	computer	computer	NOUN
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ajst-7806	345	31	pattern	pattern	NOUN
ajst-7806	345	32	recognition	recognition	NOUN
ajst-7806	345	33	.	.	PUNCT
ajst-7806	346	1	2019	2019	NUM
ajst-7806	346	2	:	:	PUNCT
ajst-7806	346	3	9432	9432	NUM
ajst-7806	346	4	-	-	SYM
ajst-7806	346	5	9441	9441	NUM
ajst-7806	346	6	.	.	PUNCT
ajst-7806	347	1	[	[	X
ajst-7806	347	2	33	33	NUM
ajst-7806	347	3	]	]	X
ajst-7806	347	4	shen	shen	PROPN
ajst-7806	347	5	y	y	PROPN
ajst-7806	347	6	,	,	PUNCT
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ajst-7806	347	9	,	,	PUNCT
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ajst-7806	347	11	y	y	PROPN
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ajst-7806	347	13	et	et	PROPN
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ajst-7806	347	19	weakly	weakly	ADJ
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ajst-7806	347	21	joint	joint	ADJ
ajst-7806	347	22	detection	detection	NOUN
ajst-7806	347	23	and	and	CCONJ
ajst-7806	347	24	segmentation[c]//proceedings	segmentation[c]//proceeding	NOUN
ajst-7806	347	25	of	of	ADP
ajst-7806	347	26	the	the	DET
ajst-7806	347	27	ieee	ieee	NOUN
ajst-7806	347	28	/	/	SYM
ajst-7806	347	29	cvf	cvf	NOUN
ajst-7806	347	30	conference	conference	NOUN
ajst-7806	347	31	on	on	ADP
ajst-7806	347	32	computer	computer	NOUN
ajst-7806	347	33	vision	vision	NOUN
ajst-7806	347	34	and	and	CCONJ
ajst-7806	347	35	pattern	pattern	NOUN
ajst-7806	347	36	recognition	recognition	NOUN
ajst-7806	347	37	.	.	PUNCT
ajst-7806	348	1	2019	2019	NUM
ajst-7806	348	2	:	:	PUNCT
ajst-7806	348	3	697	697	NUM
ajst-7806	348	4	-	-	SYM
ajst-7806	348	5	707	707	NUM
ajst-7806	348	6	.	.	PUNCT
ajst-7806	349	1	[	[	X
ajst-7806	349	2	34	34	NUM
ajst-7806	349	3	]	]	X
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ajst-7806	349	5	x	x	PROPN
ajst-7806	349	6	,	,	PUNCT
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ajst-7806	349	8	m	m	PROPN
ajst-7806	349	9	,	,	PUNCT
ajst-7806	349	10	shan	shan	PROPN
ajst-7806	349	11	s	s	PROPN
ajst-7806	349	12	,	,	PUNCT
ajst-7806	349	13	et	et	PROPN
ajst-7806	349	14	al	al	PROPN
ajst-7806	349	15	.	.	PROPN
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ajst-7806	349	17	supervised	supervise	VERB
ajst-7806	349	18	object	object	NOUN
ajst-7806	349	19	detection	detection	NOUN
ajst-7806	349	20	with	with	ADP
ajst-7806	349	21	segmentation	segmentation	NOUN
ajst-7806	349	22	collaboration[c]//proceedings	collaboration[c]//proceeding	NOUN
ajst-7806	349	23	of	of	ADP
ajst-7806	349	24	the	the	DET
ajst-7806	349	25	ieee	ieee	NOUN
ajst-7806	349	26	/	/	SYM
ajst-7806	349	27	cvf	cvf	NOUN
ajst-7806	349	28	international	international	ADJ
ajst-7806	349	29	conference	conference	NOUN
ajst-7806	349	30	on	on	ADP
ajst-7806	349	31	computer	computer	NOUN
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ajst-7806	349	33	.	.	PUNCT
ajst-7806	350	1	2019	2019	NUM
ajst-7806	350	2	:	:	PUNCT
ajst-7806	350	3	9735	9735	NUM
ajst-7806	350	4	-	-	SYM
ajst-7806	350	5	9744	9744	NUM
ajst-7806	350	6	.	.	PUNCT
ajst-7806	351	1	[	[	X
ajst-7806	351	2	35	35	NUM
ajst-7806	351	3	]	]	X
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ajst-7806	351	5	redmon	redmon	PROPN
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ajst-7806	351	8	kumar	kumar	PROPN
ajst-7806	351	9	divvala	divvala	PROPN
ajst-7806	351	10	,	,	PUNCT
ajst-7806	351	11	ross	ross	PROPN
ajst-7806	351	12	b.	b.	PROPN
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ajst-7806	351	14	,	,	PUNCT
ajst-7806	351	15	and	and	CCONJ
ajst-7806	351	16	ali	ali	PROPN
ajst-7806	351	17	farhadi	farhadi	PROPN
ajst-7806	351	18	.	.	PUNCT
ajst-7806	352	1	you	you	PRON
ajst-7806	352	2	only	only	ADV
ajst-7806	352	3	look	look	VERB
ajst-7806	352	4	once	once	ADV
ajst-7806	352	5	:	:	PUNCT
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ajst-7806	352	7	,	,	PUNCT
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ajst-7806	352	9	-	-	PUNCT
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ajst-7806	353	3	.	.	PUNCT
ajst-7806	354	1	cvpr	cvpr	NOUN
ajst-7806	354	2	,	,	PUNCT
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ajst-7806	354	4	.	.	PUNCT
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ajst-7806	355	2	36	36	NUM
ajst-7806	355	3	]	]	X
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ajst-7806	355	8	z	z	PROPN
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ajst-7806	355	20	voting	vote	VERB
ajst-7806	355	21	for	for	ADP
ajst-7806	355	22	weakly	weakly	ADJ
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ajst-7806	355	36	pattern	pattern	NOUN
ajst-7806	355	37	recognition	recognition	NOUN
ajst-7806	355	38	.	.	PUNCT
ajst-7806	356	1	2020	2020	NUM
ajst-7806	356	2	:	:	PUNCT
ajst-7806	356	3	12995	12995	NUM
ajst-7806	356	4	-	-	SYM
ajst-7806	356	5	13004	13004	NUM
ajst-7806	356	6	.	.	PUNCT
ajst-7806	357	1	[	[	X
ajst-7806	357	2	37	37	NUM
ajst-7806	357	3	]	]	X
ajst-7806	357	4	lin	lin	PROPN
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ajst-7806	358	3	et	et	PROPN
ajst-7806	358	4	al	al	PROPN
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ajst-7806	358	9	for	for	ADP
ajst-7806	358	10	weakly	weakly	ADJ
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ajst-7806	358	16	aaai	aaai	PROPN
ajst-7806	358	17	conference	conference	NOUN
ajst-7806	358	18	on	on	ADP
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ajst-7806	358	21	.	.	PUNCT
ajst-7806	359	1	2020	2020	NUM
ajst-7806	359	2	,	,	PUNCT
ajst-7806	359	3	34(07	34(07	NUM
ajst-7806	359	4	):	):	PUNCT
ajst-7806	359	5	1148211489.zhang	1148211489.zhang	PROPN
ajst-7806	359	6	d	d	PROPN
ajst-7806	359	7	,	,	PUNCT
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ajst-7806	359	10	,	,	PUNCT
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ajst-7806	359	18	unsupervised	unsupervised	ADJ
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ajst-7806	359	20	of	of	ADP
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ajst-7806	359	22	salient	salient	NOUN
ajst-7806	359	23	object	object	NOUN
ajst-7806	359	24	detector[a	detector[a	NOUN
ajst-7806	359	25	]	]	PUNCT
ajst-7806	359	26	.	.	PUNCT
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ajst-7806	360	4	on	on	ADP
ajst-7806	360	5	computer	computer	NOUN
ajst-7806	360	6	vision[c	vision[c	NOUN
ajst-7806	360	7	]	]	PUNCT
ajst-7806	360	8	.	.	PUNCT
ajst-7806	361	1	honolulu	honolulu	PROPN
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ajst-7806	361	8	,	,	PUNCT
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ajst-7806	361	10	.	.	PUNCT
ajst-7806	361	11	4048	4048	NUM
ajst-7806	361	12	4056	4056	NUM
ajst-7806	361	13	.	.	PUNCT
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ajst-7806	362	3	]	]	PUNCT
ajst-7806	362	4	wang	wang	PROPN
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ajst-7806	363	11	]	]	PUNCT
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ajst-7806	367	3	]	]	PUNCT
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ajst-7806	367	13	et	et	PROPN
ajst-7806	367	14	al	al	PROPN
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ajst-7806	367	17	:	:	PUNCT
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ajst-7806	367	22	]	]	PUNCT
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ajst-7806	368	1	proceedings	proceeding	NOUN
ajst-7806	368	2	of	of	ADP
ajst-7806	368	3	the	the	DET
ajst-7806	368	4	european	european	PROPN
ajst-7806	368	5	conference	conference	NOUN
ajst-7806	368	6	on	on	ADP
ajst-7806	368	7	computer	computer	NOUN
ajst-7806	368	8	vision(eccv	vision(eccv	PROPN
ajst-7806	368	9	)	)	PUNCT
ajst-7806	368	10	,	,	PUNCT
ajst-7806	368	11	2018	2018	NUM
ajst-7806	368	12	,	,	PUNCT
ajst-7806	368	13	3	3	NUM
ajst-7806	368	14	-	-	SYM
ajst-7806	368	15	19	19	NUM
ajst-7806	368	16	.	.	PUNCT
ajst-7806	369	1	[	[	X
ajst-7806	369	2	40	40	NUM
ajst-7806	369	3	]	]	X
ajst-7806	369	4	zhang	zhang	PROPN
ajst-7806	369	5	y	y	PROPN
ajst-7806	369	6	,	,	PUNCT
ajst-7806	369	7	bai	bai	PROPN
ajst-7806	369	8	y	y	PROPN
ajst-7806	369	9	,	,	PUNCT
ajst-7806	369	10	ding	de	VERB
ajst-7806	369	11	m	m	PRON
ajst-7806	369	12	,	,	PUNCT
ajst-7806	369	13	et	et	PROPN
ajst-7806	369	14	al	al	PROPN
ajst-7806	369	15	.	.	PUNCT
ajst-7806	370	1	w2f	w2f	VERB
ajst-7806	370	2	:	:	PUNCT
ajst-7806	370	3	a	a	DET
ajst-7806	370	4	weakly	weakly	ADV
ajst-7806	370	5	-	-	PUNCT
ajst-7806	370	6	supervised	supervised	ADJ
ajst-7806	370	7	to	to	ADP
ajst-7806	370	8	fully	fully	ADV
ajst-7806	370	9	-	-	PUNCT
ajst-7806	370	10	supervised	supervise	VERB
ajst-7806	370	11	framework	framework	NOUN
ajst-7806	370	12	for	for	ADP
ajst-7806	370	13	object	object	NOUN
ajst-7806	370	14	detection	detection	NOUN
ajst-7806	370	15	[	[	X
ajst-7806	370	16	c	c	X
ajst-7806	370	17	]	]	X
ajst-7806	370	18	//proceedings	//proceeding	NOUN
ajst-7806	370	19	of	of	ADP
ajst-7806	370	20	the	the	DET
ajst-7806	370	21	ieee	ieee	NOUN
ajst-7806	370	22	conference	conference	NOUN
ajst-7806	370	23	on	on	ADP
ajst-7806	370	24	computer	computer	NOUN
ajst-7806	370	25	vision	vision	NOUN
ajst-7806	370	26	and	and	CCONJ
ajst-7806	370	27	pattern	pattern	NOUN
ajst-7806	370	28	recognition	recognition	NOUN
ajst-7806	370	29	.	.	PUNCT
ajst-7806	371	1	2018	2018	NUM
ajst-7806	371	2	:	:	PUNCT
ajst-7806	371	3	928	928	NUM
ajst-7806	371	4	-	-	SYM
ajst-7806	371	5	936	936	NUM
ajst-7806	371	6	.	.	PUNCT
ajst-7806	372	1	[	[	X
ajst-7806	372	2	41	41	NUM
ajst-7806	372	3	]	]	X
ajst-7806	372	4	zitnick	zitnick	NOUN
ajst-7806	372	5	c	c	PROPN
ajst-7806	372	6	l	l	PROPN
ajst-7806	372	7	,	,	PUNCT
ajst-7806	372	8	dollár	dollár	NOUN
ajst-7806	372	9	p.	p.	NOUN
ajst-7806	372	10	edge	edge	NOUN
ajst-7806	372	11	boxes	box	NOUN
ajst-7806	372	12	:	:	PUNCT
ajst-7806	372	13	locating	locate	VERB
ajst-7806	372	14	object	object	NOUN
ajst-7806	372	15	proposals	proposal	NOUN
ajst-7806	372	16	from	from	ADP
ajst-7806	372	17	edges[c	edges[c	PROPN
ajst-7806	372	18	]	]	PUNCT
ajst-7806	372	19	.	.	PUNCT
ajst-7806	373	1	computer	computer	NOUN
ajst-7806	373	2	vision	vision	NOUN
ajst-7806	373	3	–	–	PUNCT
ajst-7806	373	4	eccv	eccv	ADJ
ajst-7806	373	5	2014	2014	NUM
ajst-7806	373	6	:	:	PUNCT
ajst-7806	373	7	13th	13th	ADJ
ajst-7806	373	8	european	european	PROPN
ajst-7806	373	9	conference	conference	PROPN
ajst-7806	373	10	,	,	PUNCT
ajst-7806	373	11	zurich	zurich	PROPN
ajst-7806	373	12	,	,	PUNCT
ajst-7806	373	13	switzerland	switzerland	PROPN
ajst-7806	373	14	,	,	PUNCT
ajst-7806	373	15	september	september	PROPN
ajst-7806	373	16	6	6	NUM
ajst-7806	373	17	-	-	SYM
ajst-7806	373	18	12	12	NUM
ajst-7806	373	19	,	,	PUNCT
ajst-7806	373	20	2014	2014	NUM
ajst-7806	373	21	,	,	PUNCT
ajst-7806	373	22	proceedings	proceeding	NOUN
ajst-7806	373	23	,	,	PUNCT
ajst-7806	373	24	part	part	NOUN
ajst-7806	373	25	v	v	ADP
ajst-7806	373	26	13	13	NUM
ajst-7806	373	27	.	.	PUNCT
ajst-7806	374	1	springer	springer	NOUN
ajst-7806	374	2	international	international	ADJ
ajst-7806	374	3	publishing	publishing	NOUN
ajst-7806	374	4	,	,	PUNCT
ajst-7806	374	5	2014	2014	NUM
ajst-7806	374	6	:	:	PUNCT
ajst-7806	374	7	391	391	NUM
ajst-7806	374	8	-	-	SYM
ajst-7806	374	9	405	405	NUM
ajst-7806	374	10	.	.	PUNCT
ajst-7806	375	1	[	[	X
ajst-7806	375	2	42	42	NUM
ajst-7806	375	3	]	]	PUNCT
ajst-7806	375	4	alexe	alexe	PROPN
ajst-7806	375	5	b	b	PROPN
ajst-7806	375	6	,	,	PUNCT
ajst-7806	375	7	deselaers	deselaer	NOUN
ajst-7806	375	8	t	t	PROPN
ajst-7806	375	9	,	,	PUNCT
ajst-7806	375	10	ferrari	ferrari	PROPN
ajst-7806	375	11	v.	v.	CCONJ
ajst-7806	375	12	what	what	PRON
ajst-7806	375	13	is	be	AUX
ajst-7806	375	14	an	an	DET
ajst-7806	375	15	object?[c]//2010	object?[c]//2010	ADJ
ajst-7806	375	16	ieee	ieee	NOUN
ajst-7806	375	17	computer	computer	NOUN
ajst-7806	375	18	society	society	PROPN
ajst-7806	375	19	conference	conference	NOUN
ajst-7806	375	20	on	on	ADP
ajst-7806	375	21	computer	computer	NOUN
ajst-7806	375	22	vision	vision	NOUN
ajst-7806	375	23	and	and	CCONJ
ajst-7806	375	24	pattern	pattern	NOUN
ajst-7806	375	25	recognition	recognition	NOUN
ajst-7806	375	26	.	.	PUNCT
ajst-7806	376	1	ieee	ieee	PROPN
ajst-7806	376	2	,	,	PUNCT
ajst-7806	376	3	2010	2010	NUM
ajst-7806	376	4	:	:	PUNCT
ajst-7806	376	5	73	73	NUM
ajst-7806	376	6	-	-	SYM
ajst-7806	376	7	80	80	NUM
ajst-7806	376	8	.	.	PUNCT