id	sid	tid	token	lemma	pos
fcis-6024	1	1	frontiers	frontier	NOUN
fcis-6024	1	2	in	in	ADP
fcis-6024	1	3	computing	computing	NOUN
fcis-6024	1	4	and	and	CCONJ
fcis-6024	1	5	intelligent	intelligent	ADJ
fcis-6024	1	6	systems	system	NOUN
fcis-6024	1	7	issn	issn	VERB
fcis-6024	1	8	:	:	PUNCT
fcis-6024	1	9	2832	2832	NUM
fcis-6024	1	10	-	-	SYM
fcis-6024	1	11	6024	6024	NUM
fcis-6024	1	12	|	|	NOUN
fcis-6024	1	13	vol	vol	NOUN
fcis-6024	1	14	.	.	PROPN
fcis-6024	2	1	3	3	NUM
fcis-6024	2	2	,	,	PUNCT
fcis-6024	2	3	no	no	INTJ
fcis-6024	2	4	.	.	NOUN
fcis-6024	2	5	1	1	NUM
fcis-6024	2	6	,	,	PUNCT
fcis-6024	2	7	2023	2023	NUM
fcis-6024	2	8	56	56	NUM
fcis-6024	2	9	vehicle	vehicle	NOUN
fcis-6024	2	10	target	target	NOUN
fcis-6024	2	11	detection	detection	NOUN
fcis-6024	2	12	algorithm	algorithm	NOUN
fcis-6024	2	13	based	base	VERB
fcis-6024	2	14	on	on	ADP
fcis-6024	2	15	yolov5	yolov5	NOUN
fcis-6024	2	16	xing	xing	PROPN
fcis-6024	3	1	he	he	PRON
fcis-6024	3	2	college	college	NOUN
fcis-6024	3	3	of	of	ADP
fcis-6024	3	4	electrical	electrical	ADJ
fcis-6024	3	5	engineering	engineering	NOUN
fcis-6024	3	6	,	,	PUNCT
fcis-6024	3	7	southwest	southwest	PROPN
fcis-6024	3	8	minzu	minzu	PROPN
fcis-6024	3	9	university	university	PROPN
fcis-6024	3	10	.	.	PUNCT
fcis-6024	4	1	chengdu	chengdu	PROPN
fcis-6024	4	2	,	,	PUNCT
fcis-6024	4	3	sichuan	sichuan	PROPN
fcis-6024	4	4	610041	610041	NUM
fcis-6024	4	5	,	,	PUNCT
fcis-6024	4	6	china	china	PROPN
fcis-6024	4	7	abstract	abstract	NOUN
fcis-6024	4	8	:	:	PUNCT
fcis-6024	4	9	with	with	ADP
fcis-6024	4	10	the	the	DET
fcis-6024	4	11	rapid	rapid	ADJ
fcis-6024	4	12	advancement	advancement	NOUN
fcis-6024	4	13	of	of	ADP
fcis-6024	4	14	automobile	automobile	NOUN
fcis-6024	4	15	manufacturing	manufacturing	NOUN
fcis-6024	4	16	technology	technology	NOUN
fcis-6024	4	17	and	and	CCONJ
fcis-6024	4	18	the	the	DET
fcis-6024	4	19	intensification	intensification	NOUN
fcis-6024	4	20	of	of	ADP
fcis-6024	4	21	competition	competition	NOUN
fcis-6024	4	22	among	among	ADP
fcis-6024	4	23	automobile	automobile	NOUN
fcis-6024	4	24	brands	brand	NOUN
fcis-6024	4	25	,	,	PUNCT
fcis-6024	4	26	the	the	DET
fcis-6024	4	27	development	development	NOUN
fcis-6024	4	28	of	of	ADP
fcis-6024	4	29	autonomous	autonomous	ADJ
fcis-6024	4	30	driving	driving	NOUN
fcis-6024	4	31	has	have	AUX
fcis-6024	4	32	been	be	AUX
fcis-6024	4	33	pushed	push	VERB
fcis-6024	4	34	to	to	ADP
fcis-6024	4	35	the	the	DET
fcis-6024	4	36	forefront	forefront	NOUN
fcis-6024	4	37	of	of	ADP
fcis-6024	4	38	automobile	automobile	NOUN
fcis-6024	4	39	development	development	NOUN
fcis-6024	4	40	,	,	PUNCT
fcis-6024	4	41	which	which	PRON
fcis-6024	4	42	causes	cause	VERB
fcis-6024	4	43	approximately	approximately	ADV
fcis-6024	4	44	$	$	SYM
fcis-6024	4	45	650	650	NUM
fcis-6024	4	46	billion	billion	NUM
fcis-6024	4	47	in	in	ADP
fcis-6024	4	48	losses	loss	NOUN
fcis-6024	4	49	due	due	ADP
fcis-6024	4	50	to	to	ADP
fcis-6024	4	51	traffic	traffic	NOUN
fcis-6024	4	52	accidents	accident	NOUN
fcis-6024	4	53	worldwide	worldwide	ADV
fcis-6024	4	54	every	every	DET
fcis-6024	4	55	year	year	NOUN
fcis-6024	4	56	.	.	PUNCT
fcis-6024	5	1	for	for	ADP
fcis-6024	5	2	in	in	ADP
fcis-6024	5	3	the	the	DET
fcis-6024	5	4	complex	complex	ADJ
fcis-6024	5	5	vehicle	vehicle	NOUN
fcis-6024	5	6	target	target	NOUN
fcis-6024	5	7	scene	scene	NOUN
fcis-6024	5	8	,	,	PUNCT
fcis-6024	5	9	because	because	SCONJ
fcis-6024	5	10	of	of	ADP
fcis-6024	5	11	its	its	PRON
fcis-6024	5	12	many	many	ADJ
fcis-6024	5	13	vehicle	vehicle	NOUN
fcis-6024	5	14	targets	target	NOUN
fcis-6024	5	15	,	,	PUNCT
fcis-6024	5	16	dense	dense	ADJ
fcis-6024	5	17	targets	target	NOUN
fcis-6024	5	18	often	often	ADV
fcis-6024	5	19	exist	exist	VERB
fcis-6024	5	20	between	between	ADP
fcis-6024	5	21	the	the	DET
fcis-6024	5	22	occlusion	occlusion	NOUN
fcis-6024	5	23	,	,	PUNCT
fcis-6024	5	24	overlap	overlap	NOUN
fcis-6024	5	25	,	,	PUNCT
fcis-6024	5	26	a	a	DET
fcis-6024	5	27	variety	variety	NOUN
fcis-6024	5	28	of	of	ADP
fcis-6024	5	29	different	different	ADJ
fcis-6024	5	30	weather	weather	NOUN
fcis-6024	5	31	reasons	reason	NOUN
fcis-6024	5	32	,	,	PUNCT
fcis-6024	5	33	rain	rain	NOUN
fcis-6024	5	34	,	,	PUNCT
fcis-6024	5	35	fog	fog	NOUN
fcis-6024	5	36	,	,	PUNCT
fcis-6024	5	37	sunny	sunny	ADJ
fcis-6024	5	38	days	day	NOUN
fcis-6024	5	39	,	,	PUNCT
fcis-6024	5	40	etc	etc	X
fcis-6024	5	41	.	.	X
fcis-6024	5	42	resulting	result	VERB
fcis-6024	5	43	in	in	ADP
fcis-6024	5	44	obstruction	obstruction	NOUN
fcis-6024	5	45	of	of	ADP
fcis-6024	5	46	the	the	DET
fcis-6024	5	47	field	field	NOUN
fcis-6024	5	48	of	of	ADP
fcis-6024	5	49	view	view	NOUN
fcis-6024	5	50	,	,	PUNCT
fcis-6024	5	51	and	and	CCONJ
fcis-6024	5	52	the	the	DET
fcis-6024	5	53	vehicle	vehicle	NOUN
fcis-6024	5	54	camera	camera	NOUN
fcis-6024	5	55	often	often	ADV
fcis-6024	5	56	intercepted	intercept	VERB
fcis-6024	5	57	images	image	NOUN
fcis-6024	5	58	mostly	mostly	ADV
fcis-6024	5	59	exist	exist	VERB
fcis-6024	5	60	blurred	blurred	ADJ
fcis-6024	5	61	,	,	PUNCT
fcis-6024	5	62	ghosting	ghosting	ADJ
fcis-6024	5	63	and	and	CCONJ
fcis-6024	5	64	other	other	ADJ
fcis-6024	5	65	problems	problem	NOUN
fcis-6024	5	66	,	,	PUNCT
fcis-6024	5	67	making	make	VERB
fcis-6024	5	68	the	the	DET
fcis-6024	5	69	vehicle	vehicle	NOUN
fcis-6024	5	70	target	target	NOUN
fcis-6024	5	71	detection	detection	NOUN
fcis-6024	5	72	for	for	ADP
fcis-6024	5	73	detailed	detailed	ADJ
fcis-6024	5	74	feature	feature	NOUN
fcis-6024	5	75	extraction	extraction	NOUN
fcis-6024	5	76	requirements	requirement	NOUN
fcis-6024	5	77	are	be	AUX
fcis-6024	5	78	high	high	ADJ
fcis-6024	5	79	,	,	PUNCT
fcis-6024	5	80	the	the	DET
fcis-6024	5	81	detection	detection	NOUN
fcis-6024	5	82	accuracy	accuracy	NOUN
fcis-6024	5	83	is	be	AUX
fcis-6024	5	84	often	often	ADV
fcis-6024	5	85	difficult	difficult	ADJ
fcis-6024	5	86	to	to	PART
fcis-6024	5	87	meet	meet	VERB
fcis-6024	5	88	the	the	DET
fcis-6024	5	89	requirements	requirement	NOUN
fcis-6024	5	90	,	,	PUNCT
fcis-6024	5	91	proposed	propose	VERB
fcis-6024	5	92	based	base	VERB
fcis-6024	5	93	on	on	ADP
fcis-6024	5	94	yolov5s	yolov5s	PROPN
fcis-6024	5	95	vehicle	vehicle	NOUN
fcis-6024	5	96	target	target	NOUN
fcis-6024	5	97	detection	detection	NOUN
fcis-6024	5	98	algorithm	algorithm	NOUN
fcis-6024	5	99	is	be	AUX
fcis-6024	5	100	proposed	propose	VERB
fcis-6024	5	101	.	.	PUNCT
fcis-6024	6	1	acmix	acmix	NOUN
fcis-6024	6	2	attention	attention	NOUN
fcis-6024	6	3	mechanism	mechanism	NOUN
fcis-6024	6	4	is	be	AUX
fcis-6024	6	5	introduced	introduce	VERB
fcis-6024	6	6	to	to	PART
fcis-6024	6	7	make	make	VERB
fcis-6024	6	8	the	the	DET
fcis-6024	6	9	model	model	NOUN
fcis-6024	6	10	achieve	achieve	VERB
fcis-6024	6	11	the	the	DET
fcis-6024	6	12	purpose	purpose	NOUN
fcis-6024	6	13	of	of	ADP
fcis-6024	6	14	expanding	expand	VERB
fcis-6024	6	15	the	the	DET
fcis-6024	6	16	target	target	NOUN
fcis-6024	6	17	perception	perception	NOUN
fcis-6024	6	18	field	field	NOUN
fcis-6024	6	19	,	,	PUNCT
fcis-6024	6	20	adaptively	adaptively	ADV
fcis-6024	6	21	focusing	focus	VERB
fcis-6024	6	22	on	on	ADP
fcis-6024	6	23	different	different	ADJ
fcis-6024	6	24	target	target	NOUN
fcis-6024	6	25	regions	region	NOUN
fcis-6024	6	26	,	,	PUNCT
fcis-6024	6	27	and	and	CCONJ
fcis-6024	6	28	capturing	capture	VERB
fcis-6024	6	29	more	more	ADJ
fcis-6024	6	30	information	information	NOUN
fcis-6024	6	31	features	feature	NOUN
fcis-6024	6	32	,	,	PUNCT
fcis-6024	6	33	etc	etc	X
fcis-6024	6	34	.	.	X
fcis-6024	7	1	the	the	DET
fcis-6024	7	2	test	test	NOUN
fcis-6024	7	3	shows	show	VERB
fcis-6024	7	4	that	that	SCONJ
fcis-6024	7	5	the	the	DET
fcis-6024	7	6	accuracy	accuracy	NOUN
fcis-6024	7	7	of	of	ADP
fcis-6024	7	8	the	the	DET
fcis-6024	7	9	model	model	NOUN
fcis-6024	7	10	increases	increase	NOUN
fcis-6024	7	11	by	by	ADP
fcis-6024	7	12	1.1	1.1	NUM
fcis-6024	7	13	%	%	NOUN
fcis-6024	7	14	on	on	ADP
fcis-6024	7	15	a	a	DET
fcis-6024	7	16	subset	subset	NOUN
fcis-6024	7	17	of	of	ADP
fcis-6024	7	18	bdd100k	bdd100k	NOUN
fcis-6024	7	19	data	datum	NOUN
fcis-6024	7	20	set	set	VERB
fcis-6024	7	21	after	after	ADP
fcis-6024	7	22	improvement	improvement	NOUN
fcis-6024	7	23	.	.	PUNCT
fcis-6024	8	1	the	the	DET
fcis-6024	8	2	accuracy	accuracy	NOUN
fcis-6024	8	3	of	of	ADP
fcis-6024	8	4	the	the	DET
fcis-6024	8	5	improved	improved	ADJ
fcis-6024	8	6	model	model	NOUN
fcis-6024	8	7	increased	increase	VERB
fcis-6024	8	8	by	by	ADP
fcis-6024	8	9	1.1	1.1	NUM
fcis-6024	8	10	%	%	NOUN
fcis-6024	8	11	on	on	ADP
fcis-6024	8	12	the	the	DET
fcis-6024	8	13	subset	subset	NOUN
fcis-6024	8	14	of	of	ADP
fcis-6024	8	15	bdd100k	bdd100k	NOUN
fcis-6024	8	16	dataset	dataset	NOUN
fcis-6024	8	17	and	and	CCONJ
fcis-6024	8	18	by	by	ADP
fcis-6024	8	19	1.2	1.2	NUM
fcis-6024	8	20	%	%	NOUN
fcis-6024	8	21	on	on	ADP
fcis-6024	8	22	the	the	DET
fcis-6024	8	23	pascal	pascal	ADJ
fcis-6024	8	24	voc2007	voc2007	NOUN
fcis-6024	8	25	general	general	ADJ
fcis-6024	8	26	dataset	dataset	NOUN
fcis-6024	8	27	.	.	PUNCT
fcis-6024	9	1	keywords	keyword	NOUN
fcis-6024	9	2	:	:	PUNCT
fcis-6024	9	3	yolov5s	yolov5s	NOUN
fcis-6024	9	4	;	;	PUNCT
fcis-6024	9	5	acmix	acmix	NOUN
fcis-6024	9	6	;	;	PUNCT
fcis-6024	9	7	deep	deep	ADJ
fcis-6024	9	8	learning	learning	NOUN
fcis-6024	9	9	.	.	PUNCT
fcis-6024	10	1	1	1	X
fcis-6024	10	2	.	.	X
fcis-6024	10	3	introduction	introduction	NOUN
fcis-6024	10	4	machine	machine	NOUN
fcis-6024	10	5	learning	learn	VERB
fcis-6024	10	6	class	class	NOUN
fcis-6024	10	7	algorithms	algorithm	NOUN
fcis-6024	10	8	were	be	AUX
fcis-6024	10	9	the	the	DET
fcis-6024	10	10	first	first	ADJ
fcis-6024	10	11	deep	deep	ADJ
fcis-6024	10	12	learning	learning	NOUN
fcis-6024	10	13	algorithms	algorithm	NOUN
fcis-6024	10	14	,	,	PUNCT
fcis-6024	10	15	and	and	CCONJ
fcis-6024	10	16	in	in	ADP
fcis-6024	10	17	2000	2000	NUM
fcis-6024	10	18	,	,	PUNCT
fcis-6024	10	19	the	the	DET
fcis-6024	10	20	svm	svm	ADJ
fcis-6024	10	21	algorithm	algorithm	NOUN
fcis-6024	10	22	was	be	AUX
fcis-6024	10	23	proposed	propose	VERB
fcis-6024	10	24	by	by	ADP
fcis-6024	10	25	osuna	osuna	PROPN
fcis-6024	10	26	[	[	X
fcis-6024	10	27	1	1	NUM
fcis-6024	10	28	]	]	PUNCT
fcis-6024	10	29	,	,	PUNCT
fcis-6024	10	30	which	which	PRON
fcis-6024	10	31	aims	aim	VERB
fcis-6024	10	32	to	to	PART
fcis-6024	10	33	find	find	VERB
fcis-6024	10	34	a	a	DET
fcis-6024	10	35	reasonable	reasonable	ADJ
fcis-6024	10	36	curve	curve	NOUN
fcis-6024	10	37	or	or	CCONJ
fcis-6024	10	38	a	a	DET
fcis-6024	10	39	plane	plane	NOUN
fcis-6024	10	40	to	to	PART
fcis-6024	10	41	segment	segment	VERB
fcis-6024	10	42	the	the	DET
fcis-6024	10	43	binary	binary	ADJ
fcis-6024	10	44	objects	object	NOUN
fcis-6024	10	45	perfectly	perfectly	ADV
fcis-6024	10	46	;	;	PUNCT
fcis-6024	10	47	in	in	ADP
fcis-6024	10	48	2001	2001	NUM
fcis-6024	10	49	,	,	PUNCT
fcis-6024	10	50	jones[2	jones[2	PROPN
fcis-6024	10	51	]	]	PUNCT
fcis-6024	10	52	proposed	propose	VERB
fcis-6024	10	53	the	the	DET
fcis-6024	10	54	vj	vj	PROPN
fcis-6024	10	55	algorithm	algorithm	NOUN
fcis-6024	10	56	.	.	PUNCT
fcis-6024	11	1	in	in	ADP
fcis-6024	11	2	2004	2004	NUM
fcis-6024	11	3	,	,	PUNCT
fcis-6024	11	4	the	the	DET
fcis-6024	11	5	sift[3	sift[3	NOUN
fcis-6024	11	6	]	]	X
fcis-6024	11	7	algorithm	algorithm	NOUN
fcis-6024	11	8	was	be	AUX
fcis-6024	11	9	refined	refine	VERB
fcis-6024	11	10	to	to	PART
fcis-6024	11	11	preserve	preserve	VERB
fcis-6024	11	12	the	the	DET
fcis-6024	11	13	robustness	robustness	NOUN
fcis-6024	11	14	of	of	ADP
fcis-6024	11	15	the	the	DET
fcis-6024	11	16	size	size	NOUN
fcis-6024	11	17	and	and	CCONJ
fcis-6024	11	18	rotation	rotation	NOUN
fcis-6024	11	19	variations	variation	NOUN
fcis-6024	11	20	of	of	ADP
fcis-6024	11	21	the	the	DET
fcis-6024	11	22	features	feature	NOUN
fcis-6024	11	23	extracted	extract	VERB
fcis-6024	11	24	from	from	ADP
fcis-6024	11	25	the	the	DET
fcis-6024	11	26	image	image	NOUN
fcis-6024	11	27	;	;	PUNCT
fcis-6024	11	28	these	these	PRON
fcis-6024	11	29	were	be	AUX
fcis-6024	11	30	the	the	DET
fcis-6024	11	31	early	early	ADJ
fcis-6024	11	32	target	target	NOUN
fcis-6024	11	33	detection	detection	NOUN
fcis-6024	11	34	algorithms	algorithm	NOUN
fcis-6024	11	35	.	.	PUNCT
fcis-6024	12	1	2013	2013	NUM
fcis-6024	12	2	,	,	PUNCT
fcis-6024	12	3	girshick	girshick	NOUN
fcis-6024	12	4	et	et	PROPN
fcis-6024	12	5	al	al	PROPN
fcis-6024	12	6	.	.	PROPN
fcis-6024	13	1	in	in	ADP
fcis-6024	13	2	the	the	DET
fcis-6024	13	3	network	network	NOUN
fcis-6024	13	4	,	,	PUNCT
fcis-6024	13	5	the	the	DET
fcis-6024	13	6	candidate	candidate	NOUN
fcis-6024	13	7	regions	region	NOUN
fcis-6024	13	8	(	(	PUNCT
fcis-6024	13	9	region	region	NOUN
fcis-6024	13	10	proposals	proposal	NOUN
fcis-6024	13	11	)	)	PUNCT
fcis-6024	13	12	in	in	ADP
fcis-6024	13	13	the	the	DET
fcis-6024	13	14	image	image	NOUN
fcis-6024	13	15	are	be	AUX
fcis-6024	13	16	obtained	obtain	VERB
fcis-6024	13	17	by	by	ADP
fcis-6024	13	18	selective	selective	ADJ
fcis-6024	13	19	search	search	NOUN
fcis-6024	13	20	,	,	PUNCT
fcis-6024	13	21	and	and	CCONJ
fcis-6024	13	22	then	then	ADV
fcis-6024	13	23	the	the	DET
fcis-6024	13	24	candidate	candidate	NOUN
fcis-6024	13	25	regions	region	NOUN
fcis-6024	13	26	are	be	AUX
fcis-6024	13	27	input	input	ADJ
fcis-6024	13	28	to	to	ADP
fcis-6024	13	29	the	the	DET
fcis-6024	13	30	convolutional	convolutional	ADJ
fcis-6024	13	31	neural	neural	ADJ
fcis-6024	13	32	network	network	NOUN
fcis-6024	13	33	(	(	PUNCT
fcis-6024	13	34	cnn	cnn	PROPN
fcis-6024	13	35	)	)	PUNCT
fcis-6024	13	36	to	to	PART
fcis-6024	13	37	extract	extract	VERB
fcis-6024	13	38	features	feature	NOUN
fcis-6024	13	39	,	,	PUNCT
fcis-6024	13	40	and	and	CCONJ
fcis-6024	13	41	finally	finally	ADV
fcis-6024	13	42	the	the	DET
fcis-6024	13	43	svm	svm	PROPN
fcis-6024	13	44	completes	complete	VERB
fcis-6024	13	45	the	the	DET
fcis-6024	13	46	region	region	NOUN
fcis-6024	13	47	classification	classification	NOUN
fcis-6024	13	48	.	.	PUNCT
fcis-6024	14	1	however	however	ADV
fcis-6024	14	2	,	,	PUNCT
fcis-6024	14	3	r	r	X
fcis-6024	14	4	-	-	PUNCT
fcis-6024	14	5	cnn	cnn	PROPN
fcis-6024	14	6	uses	use	VERB
fcis-6024	14	7	selection	selection	NOUN
fcis-6024	14	8	search	search	NOUN
fcis-6024	14	9	also	also	ADV
fcis-6024	14	10	generates	generate	VERB
fcis-6024	14	11	a	a	DET
fcis-6024	14	12	large	large	ADJ
fcis-6024	14	13	number	number	NOUN
fcis-6024	14	14	of	of	ADP
fcis-6024	14	15	redundant	redundant	ADJ
fcis-6024	14	16	candidate	candidate	NOUN
fcis-6024	14	17	regions	region	NOUN
fcis-6024	14	18	,	,	PUNCT
fcis-6024	14	19	which	which	PRON
fcis-6024	14	20	lowers	lower	VERB
fcis-6024	14	21	the	the	DET
fcis-6024	14	22	detection	detection	NOUN
fcis-6024	14	23	speed	speed	NOUN
fcis-6024	14	24	of	of	ADP
fcis-6024	14	25	the	the	DET
fcis-6024	14	26	model[4	model[4	NOUN
fcis-6024	14	27	]	]	PUNCT
fcis-6024	14	28	.	.	PUNCT
fcis-6024	15	1	the	the	DET
fcis-6024	15	2	spp	spp	NOUN
fcis-6024	15	3	-	-	PUNCT
fcis-6024	15	4	net	net	NOUN
fcis-6024	15	5	model	model	NOUN
fcis-6024	15	6	was	be	AUX
fcis-6024	15	7	introduced	introduce	VERB
fcis-6024	15	8	in	in	ADP
fcis-6024	15	9	2015	2015	NUM
fcis-6024	15	10	by	by	ADP
fcis-6024	15	11	k.	k.	PROPN
fcis-6024	15	12	ho	ho	PROPN
fcis-6024	15	13	et	et	PROPN
fcis-6024	15	14	al	al	PROPN
fcis-6024	15	15	.	.	PUNCT
fcis-6024	16	1	this	this	DET
fcis-6024	16	2	model	model	NOUN
fcis-6024	16	3	solves	solve	VERB
fcis-6024	16	4	the	the	DET
fcis-6024	16	5	subsequent	subsequent	ADJ
fcis-6024	16	6	problems	problem	NOUN
fcis-6024	16	7	caused	cause	VERB
fcis-6024	16	8	by	by	ADP
fcis-6024	16	9	images	image	NOUN
fcis-6024	16	10	with	with	ADP
fcis-6024	16	11	inconsistent	inconsistent	ADJ
fcis-6024	16	12	sizes	size	NOUN
fcis-6024	16	13	of	of	ADP
fcis-6024	16	14	candidate	candidate	NOUN
fcis-6024	16	15	regions	region	NOUN
fcis-6024	16	16	,	,	PUNCT
fcis-6024	16	17	and	and	CCONJ
fcis-6024	16	18	it	it	PRON
fcis-6024	16	19	can	can	AUX
fcis-6024	16	20	accept	accept	VERB
fcis-6024	16	21	any	any	DET
fcis-6024	16	22	size	size	NOUN
fcis-6024	16	23	of	of	ADP
fcis-6024	16	24	image	image	NOUN
fcis-6024	16	25	input	input	NOUN
fcis-6024	16	26	into	into	ADP
fcis-6024	16	27	the	the	DET
fcis-6024	16	28	convolutional	convolutional	ADJ
fcis-6024	16	29	neural	neural	ADJ
fcis-6024	16	30	network	network	NOUN
fcis-6024	16	31	to	to	PART
fcis-6024	16	32	perform	perform	VERB
fcis-6024	16	33	convolutional	convolutional	ADJ
fcis-6024	16	34	operation	operation	NOUN
fcis-6024	16	35	on	on	ADP
fcis-6024	16	36	the	the	DET
fcis-6024	16	37	whole	whole	ADJ
fcis-6024	16	38	image	image	NOUN
fcis-6024	16	39	,	,	PUNCT
fcis-6024	16	40	eliminating	eliminate	VERB
fcis-6024	16	41	the	the	DET
fcis-6024	16	42	redundant	redundant	ADJ
fcis-6024	16	43	computation	computation	NOUN
fcis-6024	16	44	of	of	ADP
fcis-6024	16	45	each	each	DET
fcis-6024	16	46	region	region	NOUN
fcis-6024	16	47	.	.	PUNCT
fcis-6024	17	1	in	in	ADP
fcis-6024	17	2	addition	addition	NOUN
fcis-6024	17	3	spp	spp	NOUN
fcis-6024	17	4	-	-	PUNCT
fcis-6024	17	5	net	net	NOUN
fcis-6024	17	6	also	also	ADV
fcis-6024	17	7	adds	add	VERB
fcis-6024	17	8	spp	spp	NOUN
fcis-6024	17	9	pooling	pool	VERB
fcis-6024	17	10	behind	behind	ADP
fcis-6024	17	11	the	the	DET
fcis-6024	17	12	convolutional	convolutional	ADJ
fcis-6024	17	13	network	network	NOUN
fcis-6024	17	14	,	,	PUNCT
fcis-6024	17	15	which	which	PRON
fcis-6024	17	16	makes	make	VERB
fcis-6024	17	17	the	the	DET
fcis-6024	17	18	feature	feature	NOUN
fcis-6024	17	19	maps	map	NOUN
fcis-6024	17	20	of	of	ADP
fcis-6024	17	21	different	different	ADJ
fcis-6024	17	22	sizes	size	NOUN
fcis-6024	17	23	converted	convert	VERB
fcis-6024	17	24	to	to	ADP
fcis-6024	17	25	fixed	fix	VERB
fcis-6024	17	26	size	size	NOUN
fcis-6024	17	27	size	size	NOUN
fcis-6024	17	28	.	.	PUNCT
fcis-6024	18	1	however	however	ADV
fcis-6024	18	2	,	,	PUNCT
fcis-6024	18	3	the	the	DET
fcis-6024	18	4	classifier	classifier	NOUN
fcis-6024	18	5	of	of	ADP
fcis-6024	18	6	spp	spp	NOUN
fcis-6024	18	7	-	-	PUNCT
fcis-6024	18	8	net	net	NOUN
fcis-6024	18	9	is	be	AUX
fcis-6024	18	10	still	still	ADV
fcis-6024	18	11	an	an	DET
fcis-6024	18	12	svm	svm	NOUN
fcis-6024	18	13	,	,	PUNCT
fcis-6024	18	14	which	which	PRON
fcis-6024	18	15	can	can	AUX
fcis-6024	18	16	not	not	PART
fcis-6024	18	17	be	be	AUX
fcis-6024	18	18	trained	train	VERB
fcis-6024	18	19	end	end	NOUN
fcis-6024	18	20	-	-	PUNCT
fcis-6024	18	21	to	to	ADP
fcis-6024	18	22	-	-	PUNCT
fcis-6024	18	23	end	end	NOUN
fcis-6024	18	24	,	,	PUNCT
fcis-6024	18	25	so	so	CCONJ
fcis-6024	18	26	a	a	DET
fcis-6024	18	27	large	large	ADJ
fcis-6024	18	28	number	number	NOUN
fcis-6024	18	29	of	of	ADP
fcis-6024	18	30	pixels	pixel	NOUN
fcis-6024	18	31	need	need	VERB
fcis-6024	18	32	to	to	PART
fcis-6024	18	33	be	be	AUX
fcis-6024	18	34	stored	store	VERB
fcis-6024	18	35	.	.	PUNCT
fcis-6024	19	1	in	in	ADP
fcis-6024	19	2	addition	addition	NOUN
fcis-6024	19	3	,	,	PUNCT
fcis-6024	19	4	it	it	PRON
fcis-6024	19	5	is	be	AUX
fcis-6024	19	6	difficult	difficult	ADJ
fcis-6024	19	7	to	to	PART
fcis-6024	19	8	adjust	adjust	VERB
fcis-6024	19	9	the	the	DET
fcis-6024	19	10	whole	whole	ADJ
fcis-6024	19	11	convolutional	convolutional	ADJ
fcis-6024	19	12	neural	neural	ADJ
fcis-6024	19	13	network	network	NOUN
fcis-6024	19	14	by	by	ADP
fcis-6024	19	15	adding	add	VERB
fcis-6024	19	16	spp	spp	NOUN
fcis-6024	19	17	-	-	PUNCT
fcis-6024	19	18	net	net	NOUN
fcis-6024	19	19	,	,	PUNCT
fcis-6024	19	20	resulting	result	VERB
fcis-6024	19	21	in	in	ADP
fcis-6024	19	22	the	the	DET
fcis-6024	19	23	convolutional	convolutional	ADJ
fcis-6024	19	24	neural	neural	ADJ
fcis-6024	19	25	network	network	NOUN
fcis-6024	19	26	is	be	AUX
fcis-6024	19	27	not	not	PART
fcis-6024	19	28	well	well	ADV
fcis-6024	19	29	used	use	VERB
fcis-6024	19	30	in	in	ADP
fcis-6024	19	31	practical	practical	ADJ
fcis-6024	19	32	applications	application	NOUN
fcis-6024	19	33	2015	2015	NUM
fcis-6024	19	34	girshick	girshick	NOUN
fcis-6024	19	35	et	et	NOUN
fcis-6024	19	36	al.[6]borrowed	al.[6]borrowe	VERB
fcis-6024	19	37	the	the	DET
fcis-6024	19	38	practice	practice	NOUN
fcis-6024	19	39	of	of	ADP
fcis-6024	19	40	global	global	ADJ
fcis-6024	19	41	feature	feature	NOUN
fcis-6024	19	42	extraction	extraction	NOUN
fcis-6024	19	43	of	of	ADP
fcis-6024	19	44	feature	feature	NOUN
fcis-6024	19	45	maps	map	NOUN
fcis-6024	19	46	in	in	ADP
fcis-6024	19	47	sppnet	sppnet	NOUN
fcis-6024	19	48	,	,	PUNCT
fcis-6024	19	49	and	and	CCONJ
fcis-6024	19	50	improved	improve	VERB
fcis-6024	19	51	on	on	ADP
fcis-6024	19	52	the	the	DET
fcis-6024	19	53	basis	basis	NOUN
fcis-6024	19	54	of	of	ADP
fcis-6024	19	55	r	r	NOUN
fcis-6024	19	56	-	-	PUNCT
fcis-6024	19	57	cnn	cnn	NOUN
fcis-6024	19	58	to	to	PART
fcis-6024	19	59	propose	propose	VERB
fcis-6024	19	60	fast	fast	ADJ
fcis-6024	19	61	r	r	NOUN
fcis-6024	19	62	-	-	PUNCT
fcis-6024	19	63	cnn	cnn	PROPN
fcis-6024	19	64	,	,	PUNCT
fcis-6024	19	65	which	which	PRON
fcis-6024	19	66	solved	solve	VERB
fcis-6024	19	67	the	the	DET
fcis-6024	19	68	problem	problem	NOUN
fcis-6024	19	69	of	of	ADP
fcis-6024	19	70	cumbersome	cumbersome	ADJ
fcis-6024	19	71	training	training	NOUN
fcis-6024	19	72	of	of	ADP
fcis-6024	19	73	r	r	NOUN
fcis-6024	19	74	-	-	PUNCT
fcis-6024	19	75	cnn	cnn	PROPN
fcis-6024	19	76	and	and	CCONJ
fcis-6024	19	77	each	each	DET
fcis-6024	19	78	training	training	NOUN
fcis-6024	19	79	stage	stage	NOUN
fcis-6024	19	80	being	be	AUX
fcis-6024	19	81	independent	independent	ADJ
fcis-6024	19	82	of	of	ADP
fcis-6024	19	83	each	each	DET
fcis-6024	19	84	other	other	ADJ
fcis-6024	19	85	,	,	PUNCT
fcis-6024	19	86	and	and	CCONJ
fcis-6024	19	87	secondly	secondly	ADV
fcis-6024	19	88	solved	solve	VERB
fcis-6024	19	89	the	the	DET
fcis-6024	19	90	problem	problem	NOUN
fcis-6024	19	91	of	of	ADP
fcis-6024	19	92	requiring	require	VERB
fcis-6024	19	93	consistent	consistent	ADJ
fcis-6024	19	94	input	input	NOUN
fcis-6024	19	95	image	image	NOUN
fcis-6024	19	96	size	size	NOUN
fcis-6024	19	97	,	,	PUNCT
fcis-6024	19	98	and	and	CCONJ
fcis-6024	19	99	finally	finally	ADV
fcis-6024	19	100	replaced	replace	VERB
fcis-6024	19	101	the	the	DET
fcis-6024	19	102	original	original	ADJ
fcis-6024	19	103	svm	svm	NOUN
fcis-6024	19	104	classifier	classifier	NOUN
fcis-6024	19	105	with	with	ADP
fcis-6024	19	106	softmax	softmax	NOUN
fcis-6024	19	107	.	.	PUNCT
fcis-6024	20	1	faster	fast	ADV
fcis-6024	20	2	rcnn[7	rcnn[7	PROPN
fcis-6024	20	3	]	]	PUNCT
fcis-6024	20	4	compared	compare	VERB
fcis-6024	20	5	with	with	ADP
fcis-6024	20	6	fast	fast	ADJ
fcis-6024	20	7	r	r	NOUN
fcis-6024	20	8	-	-	PUNCT
fcis-6024	20	9	cnn	cnn	PROPN
fcis-6024	20	10	,	,	PUNCT
fcis-6024	20	11	its	its	PRON
fcis-6024	20	12	biggest	big	ADJ
fcis-6024	20	13	innovation	innovation	NOUN
fcis-6024	20	14	is	be	AUX
fcis-6024	20	15	to	to	PART
fcis-6024	20	16	abandon	abandon	VERB
fcis-6024	20	17	the	the	DET
fcis-6024	20	18	use	use	NOUN
fcis-6024	20	19	of	of	ADP
fcis-6024	20	20	selective	selective	ADJ
fcis-6024	20	21	search	search	NOUN
fcis-6024	20	22	network	network	NOUN
fcis-6024	20	23	and	and	CCONJ
fcis-6024	20	24	use	use	VERB
fcis-6024	20	25	region	region	NOUN
fcis-6024	20	26	proposal	proposal	NOUN
fcis-6024	20	27	network	network	NOUN
fcis-6024	20	28	(	(	PUNCT
fcis-6024	20	29	rpn	rpn	PROPN
fcis-6024	20	30	)	)	PUNCT
fcis-6024	20	31	as	as	ADP
fcis-6024	20	32	a	a	DET
fcis-6024	20	33	replacement	replacement	NOUN
fcis-6024	20	34	.	.	PUNCT
fcis-6024	21	1	the	the	DET
fcis-6024	21	2	rpn	rpn	PROPN
fcis-6024	21	3	,	,	PUNCT
fcis-6024	21	4	as	as	SCONJ
fcis-6024	21	5	the	the	DET
fcis-6024	21	6	core	core	NOUN
fcis-6024	21	7	network	network	NOUN
fcis-6024	21	8	of	of	ADP
fcis-6024	21	9	faster	fast	ADJ
fcis-6024	21	10	r	r	NOUN
fcis-6024	21	11	-	-	PUNCT
fcis-6024	21	12	cnn	cnn	PROPN
fcis-6024	21	13	,	,	PUNCT
fcis-6024	21	14	uses	use	VERB
fcis-6024	21	15	a	a	DET
fcis-6024	21	16	fully	fully	ADV
fcis-6024	21	17	convolutional	convolutional	ADJ
fcis-6024	21	18	layer	layer	NOUN
fcis-6024	21	19	instead	instead	ADV
fcis-6024	21	20	of	of	ADP
fcis-6024	21	21	a	a	DET
fcis-6024	21	22	fully	fully	ADV
fcis-6024	21	23	connected	connect	VERB
fcis-6024	21	24	layer	layer	NOUN
fcis-6024	21	25	.	.	PUNCT
fcis-6024	22	1	its	its	PRON
fcis-6024	22	2	biggest	big	ADJ
fcis-6024	22	3	advantage	advantage	NOUN
fcis-6024	22	4	is	be	AUX
fcis-6024	22	5	that	that	SCONJ
fcis-6024	22	6	it	it	PRON
fcis-6024	22	7	can	can	AUX
fcis-6024	22	8	automatically	automatically	ADV
fcis-6024	22	9	learn	learn	VERB
fcis-6024	22	10	to	to	PART
fcis-6024	22	11	distinguish	distinguish	VERB
fcis-6024	22	12	the	the	DET
fcis-6024	22	13	foreground	foreground	NOUN
fcis-6024	22	14	and	and	CCONJ
fcis-6024	22	15	background	background	NOUN
fcis-6024	22	16	of	of	ADP
fcis-6024	22	17	the	the	DET
fcis-6024	22	18	image	image	NOUN
fcis-6024	22	19	during	during	ADP
fcis-6024	22	20	candidate	candidate	NOUN
fcis-6024	22	21	region	region	NOUN
fcis-6024	22	22	extraction	extraction	NOUN
fcis-6024	22	23	by	by	ADP
fcis-6024	22	24	the	the	DET
fcis-6024	22	25	algorithm	algorithm	NOUN
fcis-6024	22	26	to	to	PART
fcis-6024	22	27	extract	extract	VERB
fcis-6024	22	28	valid	valid	ADJ
fcis-6024	22	29	candidate	candidate	NOUN
fcis-6024	22	30	regions	region	NOUN
fcis-6024	22	31	and	and	CCONJ
fcis-6024	22	32	avoid	avoid	VERB
fcis-6024	22	33	generating	generate	VERB
fcis-6024	22	34	candidate	candidate	NOUN
fcis-6024	22	35	regions	region	NOUN
fcis-6024	22	36	with	with	ADP
fcis-6024	22	37	negative	negative	ADJ
fcis-6024	22	38	samples	sample	NOUN
fcis-6024	22	39	,	,	PUNCT
fcis-6024	22	40	thus	thus	ADV
fcis-6024	22	41	reducing	reduce	VERB
fcis-6024	22	42	the	the	DET
fcis-6024	22	43	number	number	NOUN
fcis-6024	22	44	of	of	ADP
fcis-6024	22	45	candidate	candidate	NOUN
fcis-6024	22	46	regions	region	NOUN
fcis-6024	22	47	.	.	PUNCT
fcis-6024	23	1	the	the	DET
fcis-6024	23	2	yolo	yolo	NOUN
fcis-6024	23	3	(	(	PUNCT
fcis-6024	23	4	you	you	PRON
fcis-6024	23	5	only	only	ADV
fcis-6024	23	6	look	look	VERB
fcis-6024	23	7	once	once	ADV
fcis-6024	23	8	)	)	PUNCT
fcis-6024	23	9	series	series	NOUN
fcis-6024	23	10	is	be	AUX
fcis-6024	23	11	one	one	NUM
fcis-6024	23	12	of	of	ADP
fcis-6024	23	13	the	the	DET
fcis-6024	23	14	representative	representative	ADJ
fcis-6024	23	15	works	work	NOUN
fcis-6024	23	16	of	of	ADP
fcis-6024	23	17	single	single	ADJ
fcis-6024	23	18	-	-	PUNCT
fcis-6024	23	19	stage	stage	NOUN
fcis-6024	23	20	target	target	NOUN
fcis-6024	23	21	detection	detection	NOUN
fcis-6024	23	22	methods	method	NOUN
fcis-6024	23	23	.	.	PUNCT
fcis-6024	24	1	single	single	ADJ
fcis-6024	24	2	-	-	PUNCT
fcis-6024	24	3	stage	stage	NOUN
fcis-6024	24	4	target	target	NOUN
fcis-6024	24	5	detection	detection	NOUN
fcis-6024	24	6	does	do	AUX
fcis-6024	24	7	not	not	PART
fcis-6024	24	8	have	have	VERB
fcis-6024	24	9	the	the	DET
fcis-6024	24	10	process	process	NOUN
fcis-6024	24	11	of	of	ADP
fcis-6024	24	12	finding	find	VERB
fcis-6024	24	13	candidate	candidate	NOUN
fcis-6024	24	14	regions	region	NOUN
fcis-6024	24	15	,	,	PUNCT
fcis-6024	24	16	and	and	CCONJ
fcis-6024	24	17	its	its	PRON
fcis-6024	24	18	can	can	AUX
fcis-6024	24	19	be	be	AUX
fcis-6024	24	20	unified	unify	VERB
fcis-6024	24	21	into	into	ADP
fcis-6024	24	22	a	a	DET
fcis-6024	24	23	regression	regression	NOUN
fcis-6024	24	24	problem	problem	NOUN
fcis-6024	24	25	.	.	PUNCT
fcis-6024	25	1	compared	compare	VERB
fcis-6024	25	2	with	with	ADP
fcis-6024	25	3	,	,	PUNCT
fcis-6024	25	4	for	for	ADP
fcis-6024	25	5	example	example	NOUN
fcis-6024	25	6	,	,	PUNCT
fcis-6024	25	7	the	the	DET
fcis-6024	25	8	faster	fast	ADJ
fcis-6024	25	9	r	r	NOUN
fcis-6024	25	10	-	-	PUNCT
fcis-6024	25	11	cnn	cnn	PROPN
fcis-6024	25	12	algorithm	algorithm	NOUN
fcis-6024	25	13	,	,	PUNCT
fcis-6024	25	14	yolo	yolo	PROPN
fcis-6024	25	15	can	can	AUX
fcis-6024	25	16	better	well	ADV
fcis-6024	25	17	distinguish	distinguish	VERB
fcis-6024	25	18	the	the	DET
fcis-6024	25	19	foreground	foreground	NOUN
fcis-6024	25	20	and	and	CCONJ
fcis-6024	25	21	background	background	NOUN
fcis-6024	25	22	of	of	ADP
fcis-6024	25	23	an	an	DET
fcis-6024	25	24	image	image	NOUN
fcis-6024	25	25	by	by	ADP
fcis-6024	25	26	this	this	DET
fcis-6024	25	27	way	way	NOUN
fcis-6024	25	28	,	,	PUNCT
fcis-6024	25	29	and	and	CCONJ
fcis-6024	25	30	the	the	DET
fcis-6024	25	31	detection	detection	NOUN
fcis-6024	25	32	speed	speed	NOUN
fcis-6024	25	33	is	be	AUX
fcis-6024	25	34	faster	fast	ADJ
fcis-6024	25	35	.	.	PUNCT
fcis-6024	26	1	however	however	ADV
fcis-6024	26	2	,	,	PUNCT
fcis-6024	26	3	the	the	DET
fcis-6024	26	4	yolov1[8	yolov1[8	PROPN
fcis-6024	26	5	]	]	PUNCT
fcis-6024	26	6	algorithm	algorithm	NOUN
fcis-6024	26	7	requires	require	VERB
fcis-6024	26	8	a	a	DET
fcis-6024	26	9	fixed	fix	VERB
fcis-6024	26	10	size	size	NOUN
fcis-6024	26	11	of	of	ADP
fcis-6024	26	12	the	the	DET
fcis-6024	26	13	input	input	NOUN
fcis-6024	26	14	image	image	NOUN
fcis-6024	26	15	,	,	PUNCT
fcis-6024	26	16	and	and	CCONJ
fcis-6024	26	17	when	when	SCONJ
fcis-6024	26	18	the	the	DET
fcis-6024	26	19	input	input	NOUN
fcis-6024	26	20	image	image	NOUN
fcis-6024	26	21	is	be	AUX
fcis-6024	26	22	divided	divide	VERB
fcis-6024	26	23	into	into	ADP
fcis-6024	26	24	a	a	DET
fcis-6024	26	25	grid	grid	NOUN
fcis-6024	26	26	,	,	PUNCT
fcis-6024	26	27	each	each	DET
fcis-6024	26	28	grid	grid	NOUN
fcis-6024	26	29	can	can	AUX
fcis-6024	26	30	only	only	ADV
fcis-6024	26	31	predict	predict	VERB
fcis-6024	26	32	a	a	DET
fcis-6024	26	33	single	single	ADJ
fcis-6024	26	34	object	object	NOUN
fcis-6024	26	35	,	,	PUNCT
fcis-6024	26	36	and	and	CCONJ
fcis-6024	26	37	when	when	SCONJ
fcis-6024	26	38	multiple	multiple	ADJ
fcis-6024	26	39	objects	object	NOUN
fcis-6024	26	40	appear	appear	VERB
fcis-6024	26	41	inside	inside	ADP
fcis-6024	26	42	the	the	DET
fcis-6024	26	43	same	same	ADJ
fcis-6024	26	44	grid	grid	NOUN
fcis-6024	26	45	,	,	PUNCT
fcis-6024	26	46	the	the	DET
fcis-6024	26	47	nms	nms	PROPN
fcis-6024	26	48	algorithm	algorithm	NOUN
fcis-6024	26	49	will	will	AUX
fcis-6024	26	50	be	be	AUX
fcis-6024	26	51	used	use	VERB
fcis-6024	26	52	to	to	PART
fcis-6024	26	53	merge	merge	VERB
fcis-6024	26	54	,	,	PUNCT
fcis-6024	26	55	filter	filter	NOUN
fcis-6024	26	56	,	,	PUNCT
fcis-6024	26	57	and	and	CCONJ
fcis-6024	26	58	delete	delete	VERB
fcis-6024	26	59	the	the	DET
fcis-6024	26	60	bounding	bounding	NOUN
fcis-6024	26	61	box	box	NOUN
fcis-6024	26	62	,	,	PUNCT
fcis-6024	26	63	and	and	CCONJ
fcis-6024	26	64	finally	finally	ADV
fcis-6024	26	65	leave	leave	VERB
fcis-6024	26	66	the	the	DET
fcis-6024	26	67	bounding	bounding	NOUN
fcis-6024	26	68	box	box	NOUN
fcis-6024	26	69	with	with	ADP
fcis-6024	26	70	the	the	DET
fcis-6024	26	71	highest	high	ADJ
fcis-6024	26	72	confidence	confidence	NOUN
fcis-6024	26	73	,	,	PUNCT
fcis-6024	26	74	which	which	PRON
fcis-6024	26	75	will	will	AUX
fcis-6024	26	76	lead	lead	VERB
fcis-6024	26	77	to	to	ADP
fcis-6024	26	78	the	the	DET
fcis-6024	26	79	target	target	NOUN
fcis-6024	26	80	increase	increase	NOUN
fcis-6024	26	81	of	of	ADP
fcis-6024	26	82	missed	miss	VERB
fcis-6024	26	83	detection	detection	NOUN
fcis-6024	26	84	rate	rate	NOUN
fcis-6024	26	85	.	.	PUNCT
fcis-6024	27	1	later	later	ADV
fcis-6024	27	2	,	,	PUNCT
fcis-6024	27	3	scholars	scholar	NOUN
fcis-6024	27	4	improved	improve	VERB
fcis-6024	27	5	yolo	yolo	PROPN
fcis-6024	27	6	,	,	PUNCT
fcis-6024	27	7	and	and	CCONJ
fcis-6024	27	8	there	there	PRON
fcis-6024	27	9	are	be	VERB
fcis-6024	27	10	yolov2	yolov2	PROPN
fcis-6024	27	11	version	version	NOUN
fcis-6024	27	12	,	,	PUNCT
fcis-6024	27	13	yolov3	yolov3	PROPN
fcis-6024	27	14	version	version	PROPN
fcis-6024	27	15	,	,	PUNCT
fcis-6024	27	16	yolov4	yolov4	PROPN
fcis-6024	27	17	version	version	PROPN
fcis-6024	27	18	,	,	PUNCT
fcis-6024	27	19	etc	etc	X
fcis-6024	27	20	.	.	X
fcis-6024	28	1	redmon	redmon	PROPN
fcis-6024	29	1	[	[	X
fcis-6024	29	2	9	9	NUM
fcis-6024	29	3	]	]	PUNCT
fcis-6024	29	4	et	et	PROPN
fcis-6024	29	5	al	al	PROPN
fcis-6024	29	6	.	.	PROPN
fcis-6024	29	7	proposed	propose	VERB
fcis-6024	29	8	the	the	DET
fcis-6024	29	9	yolov2	yolov2	PROPN
fcis-6024	29	10	algorithm	algorithm	PROPN
fcis-6024	29	11	to	to	PART
fcis-6024	29	12	address	address	VERB
fcis-6024	29	13	the	the	DET
fcis-6024	29	14	shortcomings	shortcoming	NOUN
fcis-6024	29	15	of	of	ADP
fcis-6024	29	16	yolo	yolo	NOUN
fcis-6024	29	17	,	,	PUNCT
fcis-6024	29	18	which	which	PRON
fcis-6024	29	19	improves	improve	VERB
fcis-6024	29	20	on	on	ADP
fcis-6024	29	21	yolo	yolo	NOUN
fcis-6024	29	22	in	in	ADP
fcis-6024	29	23	three	three	NUM
fcis-6024	29	24	main	main	ADJ
fcis-6024	29	25	aspects	aspect	NOUN
fcis-6024	29	26	.	.	PUNCT
fcis-6024	30	1	the	the	DET
fcis-6024	30	2	first	first	ADJ
fcis-6024	30	3	aspect	aspect	NOUN
fcis-6024	30	4	is	be	AUX
fcis-6024	30	5	that	that	SCONJ
fcis-6024	30	6	yolov2	yolov2	PROPN
fcis-6024	30	7	adopts	adopt	VERB
fcis-6024	30	8	the	the	DET
fcis-6024	30	9	darknet-19	darknet-19	PROPN
fcis-6024	30	10	network	network	NOUN
fcis-6024	30	11	as	as	ADP
fcis-6024	30	12	the	the	DET
fcis-6024	30	13	backbone	backbone	NOUN
fcis-6024	30	14	network	network	NOUN
fcis-6024	30	15	,	,	PUNCT
fcis-6024	30	16	mainly	mainly	ADV
fcis-6024	30	17	using	use	VERB
fcis-6024	30	18	3	3	NUM
fcis-6024	30	19	×	×	NOUN
fcis-6024	30	20	3	3	NUM
fcis-6024	30	21	convolution	convolution	NOUN
fcis-6024	30	22	and	and	CCONJ
fcis-6024	30	23	1	1	NUM
fcis-6024	30	24	×	×	NOUN
fcis-6024	30	25	1	1	NUM
fcis-6024	30	26	convolution	convolution	NOUN
fcis-6024	30	27	,	,	PUNCT
fcis-6024	30	28	and	and	CCONJ
fcis-6024	30	29	discards	discard	VERB
fcis-6024	30	30	the	the	DET
fcis-6024	30	31	dropout	dropout	NOUN
fcis-6024	30	32	network	network	NOUN
fcis-6024	30	33	and	and	CCONJ
fcis-6024	30	34	adds	add	VERB
fcis-6024	30	35	a	a	DET
fcis-6024	30	36	batch	batch	NOUN
fcis-6024	30	37	normalization	normalization	NOUN
fcis-6024	30	38	after	after	ADP
fcis-6024	30	39	each	each	DET
fcis-6024	30	40	convolution	convolution	NOUN
fcis-6024	30	41	,	,	PUNCT
fcis-6024	30	42	which	which	PRON
fcis-6024	30	43	not	not	PART
fcis-6024	30	44	only	only	ADV
fcis-6024	30	45	accelerates	accelerate	VERB
fcis-6024	30	46	the	the	DET
fcis-6024	30	47	convergence	convergence	NOUN
fcis-6024	30	48	speed	speed	NOUN
fcis-6024	30	49	of	of	ADP
fcis-6024	30	50	the	the	DET
fcis-6024	30	51	model	model	NOUN
fcis-6024	30	52	in	in	ADP
fcis-6024	30	53	the	the	DET
fcis-6024	30	54	training	training	NOUN
fcis-6024	30	55	phase	phase	NOUN
fcis-6024	30	56	,	,	PUNCT
fcis-6024	30	57	but	but	CCONJ
fcis-6024	30	58	also	also	ADV
fcis-6024	30	59	effectively	effectively	ADV
fcis-6024	30	60	reduces	reduce	VERB
fcis-6024	30	61	the	the	DET
fcis-6024	30	62	possibility	possibility	NOUN
fcis-6024	30	63	of	of	ADP
fcis-6024	30	64	overfitting	overfitte	VERB
fcis-6024	30	65	the	the	DET
fcis-6024	30	66	model	model	NOUN
fcis-6024	30	67	.	.	PUNCT
fcis-6024	31	1	the	the	DET
fcis-6024	31	2	second	second	ADJ
fcis-6024	31	3	aspect	aspect	NOUN
fcis-6024	31	4	is	be	AUX
fcis-6024	31	5	the	the	DET
fcis-6024	31	6	use	use	NOUN
fcis-6024	31	7	of	of	ADP
fcis-6024	31	8	convolutional	convolutional	ADJ
fcis-6024	31	9	layers	layer	NOUN
fcis-6024	31	10	instead	instead	ADV
fcis-6024	31	11	57	57	NUM
fcis-6024	31	12	of	of	ADP
fcis-6024	31	13	fully	fully	ADV
fcis-6024	31	14	connected	connected	ADJ
fcis-6024	31	15	layers	layer	NOUN
fcis-6024	31	16	in	in	ADP
fcis-6024	31	17	yolo	yolo	PROPN
fcis-6024	31	18	,	,	PUNCT
fcis-6024	31	19	which	which	PRON
fcis-6024	31	20	reduces	reduce	VERB
fcis-6024	31	21	the	the	DET
fcis-6024	31	22	sensitivity	sensitivity	NOUN
fcis-6024	31	23	of	of	ADP
fcis-6024	31	24	the	the	DET
fcis-6024	31	25	network	network	NOUN
fcis-6024	31	26	structure	structure	NOUN
fcis-6024	31	27	to	to	ADP
fcis-6024	31	28	the	the	DET
fcis-6024	31	29	size	size	NOUN
fcis-6024	31	30	of	of	ADP
fcis-6024	31	31	the	the	DET
fcis-6024	31	32	input	input	NOUN
fcis-6024	31	33	image	image	NOUN
fcis-6024	31	34	,	,	PUNCT
fcis-6024	31	35	and	and	CCONJ
fcis-6024	31	36	thus	thus	ADV
fcis-6024	31	37	allows	allow	VERB
fcis-6024	31	38	multi	multi	ADJ
fcis-6024	31	39	-	-	ADJ
fcis-6024	31	40	scale	scale	ADJ
fcis-6024	31	41	training	training	NOUN
fcis-6024	31	42	by	by	ADP
fcis-6024	31	43	changing	change	VERB
fcis-6024	31	44	the	the	DET
fcis-6024	31	45	size	size	NOUN
fcis-6024	31	46	of	of	ADP
fcis-6024	31	47	the	the	DET
fcis-6024	31	48	image	image	NOUN
fcis-6024	31	49	,	,	PUNCT
fcis-6024	31	50	and	and	CCONJ
fcis-6024	31	51	then	then	ADV
fcis-6024	31	52	fine	fine	ADV
fcis-6024	31	53	-	-	PUNCT
fcis-6024	31	54	tuning	tune	VERB
fcis-6024	31	55	the	the	DET
fcis-6024	31	56	initial	initial	ADJ
fcis-6024	31	57	classification	classification	NOUN
fcis-6024	31	58	network	network	NOUN
fcis-6024	31	59	with	with	ADP
fcis-6024	31	60	a	a	DET
fcis-6024	31	61	high	high	ADJ
fcis-6024	31	62	-	-	PUNCT
fcis-6024	31	63	resolution	resolution	NOUN
fcis-6024	31	64	classifier	classifier	NOUN
fcis-6024	31	65	,	,	PUNCT
fcis-6024	31	66	which	which	PRON
fcis-6024	31	67	can	can	AUX
fcis-6024	31	68	reach	reach	VERB
fcis-6024	31	69	448×448	448×448	NUM
fcis-6024	31	70	in	in	ADP
fcis-6024	31	71	yolov2	yolov2	PROPN
fcis-6024	31	72	.	.	PUNCT
fcis-6024	32	1	the	the	DET
fcis-6024	32	2	third	third	ADJ
fcis-6024	32	3	aspect	aspect	NOUN
fcis-6024	32	4	is	be	AUX
fcis-6024	32	5	the	the	DET
fcis-6024	32	6	introduction	introduction	NOUN
fcis-6024	32	7	of	of	ADP
fcis-6024	32	8	the	the	DET
fcis-6024	32	9	anchors	anchor	NOUN
fcis-6024	32	10	box	box	PROPN
fcis-6024	32	11	in	in	ADP
fcis-6024	32	12	the	the	DET
fcis-6024	32	13	idea	idea	NOUN
fcis-6024	32	14	of	of	ADP
fcis-6024	32	15	anchors	anchors	PROPN
fcis-6024	32	16	box	box	PROPN
fcis-6024	32	17	is	be	AUX
fcis-6024	32	18	introduced	introduce	VERB
fcis-6024	32	19	in	in	ADP
fcis-6024	32	20	prediction	prediction	NOUN
fcis-6024	32	21	,	,	PUNCT
fcis-6024	32	22	and	and	CCONJ
fcis-6024	32	23	the	the	DET
fcis-6024	32	24	direct	direct	ADJ
fcis-6024	32	25	regression	regression	NOUN
fcis-6024	32	26	method	method	NOUN
fcis-6024	32	27	is	be	AUX
fcis-6024	32	28	improved	improve	VERB
fcis-6024	32	29	by	by	ADP
fcis-6024	32	30	introducing	introduce	VERB
fcis-6024	32	31	anchors	anchor	NOUN
fcis-6024	32	32	box	box	PROPN
fcis-6024	32	33	.	.	PUNCT
fcis-6024	33	1	compared	compare	VERB
fcis-6024	33	2	with	with	ADP
fcis-6024	33	3	the	the	DET
fcis-6024	33	4	yolo	yolo	ADJ
fcis-6024	33	5	algorithm	algorithm	NOUN
fcis-6024	33	6	,	,	PUNCT
fcis-6024	33	7	the	the	DET
fcis-6024	33	8	yolov2	yolov2	PROPN
fcis-6024	33	9	algorithm	algorithm	PROPN
fcis-6024	33	10	has	have	AUX
fcis-6024	33	11	all	all	DET
fcis-6024	33	12	improved	improve	VERB
fcis-6024	33	13	performance	performance	NOUN
fcis-6024	33	14	in	in	ADP
fcis-6024	33	15	detection	detection	NOUN
fcis-6024	33	16	speed	speed	NOUN
fcis-6024	33	17	and	and	CCONJ
fcis-6024	33	18	detection	detection	NOUN
fcis-6024	33	19	accuracy	accuracy	NOUN
fcis-6024	33	20	,	,	PUNCT
fcis-6024	33	21	but	but	CCONJ
fcis-6024	33	22	the	the	DET
fcis-6024	33	23	yolov2	yolov2	PROPN
fcis-6024	33	24	algorithm	algorithm	PROPN
fcis-6024	33	25	is	be	AUX
fcis-6024	33	26	still	still	ADV
fcis-6024	33	27	poor	poor	ADJ
fcis-6024	33	28	in	in	ADP
fcis-6024	33	29	detecting	detect	VERB
fcis-6024	33	30	small	small	ADJ
fcis-6024	33	31	targets	target	NOUN
fcis-6024	33	32	in	in	ADP
fcis-6024	33	33	images	image	NOUN
fcis-6024	33	34	.	.	PUNCT
fcis-6024	34	1	yolov3	yolov3	PROPN
fcis-6024	35	1	[	[	X
fcis-6024	35	2	10	10	NUM
fcis-6024	35	3	]	]	PUNCT
fcis-6024	35	4	makes	make	VERB
fcis-6024	35	5	improvements	improvement	NOUN
fcis-6024	35	6	on	on	ADP
fcis-6024	35	7	the	the	DET
fcis-6024	35	8	basis	basis	NOUN
fcis-6024	35	9	of	of	ADP
fcis-6024	35	10	yolov2	yolov2	PROPN
fcis-6024	35	11	and	and	CCONJ
fcis-6024	35	12	has	have	VERB
fcis-6024	35	13	better	well	ADJ
fcis-6024	35	14	performance	performance	NOUN
fcis-6024	35	15	.	.	PUNCT
fcis-6024	36	1	the	the	DET
fcis-6024	36	2	yolov3	yolov3	PROPN
fcis-6024	36	3	algorithm	algorithm	PROPN
fcis-6024	36	4	uses	use	VERB
fcis-6024	36	5	darknet53	darknet53	NOUN
fcis-6024	36	6	network	network	NOUN
fcis-6024	36	7	in	in	ADP
fcis-6024	36	8	the	the	DET
fcis-6024	36	9	feature	feature	NOUN
fcis-6024	36	10	extraction	extraction	NOUN
fcis-6024	36	11	part	part	NOUN
fcis-6024	36	12	,	,	PUNCT
fcis-6024	36	13	and	and	CCONJ
fcis-6024	36	14	its	its	PRON
fcis-6024	36	15	structure	structure	NOUN
fcis-6024	36	16	is	be	AUX
fcis-6024	36	17	referred	refer	VERB
fcis-6024	36	18	to	to	ADP
fcis-6024	36	19	the	the	DET
fcis-6024	36	20	residual	residual	ADJ
fcis-6024	36	21	network	network	NOUN
fcis-6024	36	22	,	,	PUNCT
fcis-6024	36	23	and	and	CCONJ
fcis-6024	36	24	layerhopping	layerhoppe	VERB
fcis-6024	36	25	connections	connection	NOUN
fcis-6024	36	26	are	be	AUX
fcis-6024	36	27	made	make	VERB
fcis-6024	36	28	in	in	ADP
fcis-6024	36	29	different	different	ADJ
fcis-6024	36	30	network	network	NOUN
fcis-6024	36	31	layers	layer	NOUN
fcis-6024	36	32	to	to	PART
fcis-6024	36	33	fuse	fuse	VERB
fcis-6024	36	34	deep	deep	ADJ
fcis-6024	36	35	and	and	CCONJ
fcis-6024	36	36	shallow	shallow	ADJ
fcis-6024	36	37	features	feature	NOUN
fcis-6024	36	38	,	,	PUNCT
fcis-6024	36	39	which	which	PRON
fcis-6024	36	40	increases	increase	VERB
fcis-6024	36	41	the	the	DET
fcis-6024	36	42	depth	depth	NOUN
fcis-6024	36	43	of	of	ADP
fcis-6024	36	44	the	the	DET
fcis-6024	36	45	network	network	NOUN
fcis-6024	36	46	layers	layer	NOUN
fcis-6024	36	47	and	and	CCONJ
fcis-6024	36	48	not	not	PART
fcis-6024	36	49	only	only	ADV
fcis-6024	36	50	reduces	reduce	VERB
fcis-6024	36	51	the	the	DET
fcis-6024	36	52	loss	loss	NOUN
fcis-6024	36	53	of	of	ADP
fcis-6024	36	54	feature	feature	NOUN
fcis-6024	36	55	information	information	NOUN
fcis-6024	36	56	between	between	ADP
fcis-6024	36	57	feature	feature	NOUN
fcis-6024	36	58	layers	layer	NOUN
fcis-6024	36	59	in	in	ADP
fcis-6024	36	60	the	the	DET
fcis-6024	36	61	convolution	convolution	NOUN
fcis-6024	36	62	process	process	NOUN
fcis-6024	36	63	this	this	PRON
fcis-6024	36	64	not	not	PART
fcis-6024	36	65	only	only	ADV
fcis-6024	36	66	reduces	reduce	VERB
fcis-6024	36	67	the	the	DET
fcis-6024	36	68	loss	loss	NOUN
fcis-6024	36	69	of	of	ADP
fcis-6024	36	70	feature	feature	NOUN
fcis-6024	36	71	information	information	NOUN
fcis-6024	36	72	during	during	ADP
fcis-6024	36	73	the	the	DET
fcis-6024	36	74	convolution	convolution	NOUN
fcis-6024	36	75	process	process	NOUN
fcis-6024	36	76	,	,	PUNCT
fcis-6024	36	77	but	but	CCONJ
fcis-6024	36	78	also	also	ADV
fcis-6024	36	79	reduces	reduce	VERB
fcis-6024	36	80	the	the	DET
fcis-6024	36	81	number	number	NOUN
fcis-6024	36	82	of	of	ADP
fcis-6024	36	83	parameters	parameter	NOUN
fcis-6024	36	84	and	and	CCONJ
fcis-6024	36	85	the	the	DET
fcis-6024	36	86	amount	amount	NOUN
fcis-6024	36	87	of	of	ADP
fcis-6024	36	88	operations	operation	NOUN
fcis-6024	36	89	,	,	PUNCT
fcis-6024	36	90	and	and	CCONJ
fcis-6024	36	91	improves	improve	VERB
fcis-6024	36	92	the	the	DET
fcis-6024	36	93	detection	detection	NOUN
fcis-6024	36	94	speed	speed	NOUN
fcis-6024	36	95	.	.	PUNCT
fcis-6024	37	1	in	in	ADP
fcis-6024	37	2	addition	addition	NOUN
fcis-6024	37	3	,	,	PUNCT
fcis-6024	37	4	yolov3	yolov3	PROPN
fcis-6024	37	5	uses	use	VERB
fcis-6024	37	6	a	a	DET
fcis-6024	37	7	similar	similar	ADJ
fcis-6024	37	8	operation	operation	NOUN
fcis-6024	37	9	to	to	ADP
fcis-6024	37	10	fpn	fpn	VERB
fcis-6024	37	11	network	network	NOUN
fcis-6024	37	12	in	in	ADP
fcis-6024	37	13	the	the	DET
fcis-6024	37	14	detection	detection	NOUN
fcis-6024	37	15	process	process	NOUN
fcis-6024	37	16	,	,	PUNCT
fcis-6024	37	17	after	after	ADP
fcis-6024	37	18	upsampling	upsample	VERB
fcis-6024	37	19	the	the	DET
fcis-6024	37	20	shallow	shallow	ADJ
fcis-6024	37	21	features	feature	NOUN
fcis-6024	37	22	,	,	PUNCT
fcis-6024	37	23	1×1	1×1	NUM
fcis-6024	37	24	convolution	convolution	NOUN
fcis-6024	37	25	is	be	AUX
fcis-6024	37	26	performed	perform	VERB
fcis-6024	37	27	,	,	PUNCT
fcis-6024	37	28	and	and	CCONJ
fcis-6024	37	29	then	then	ADV
fcis-6024	37	30	the	the	DET
fcis-6024	37	31	two	two	NUM
fcis-6024	37	32	feature	feature	NOUN
fcis-6024	37	33	maps	map	NOUN
fcis-6024	37	34	are	be	AUX
fcis-6024	37	35	summed	sum	VERB
fcis-6024	37	36	to	to	PART
fcis-6024	37	37	complete	complete	VERB
fcis-6024	37	38	a	a	DET
fcis-6024	37	39	feature	feature	NOUN
fcis-6024	37	40	fusion	fusion	NOUN
fcis-6024	37	41	.	.	PUNCT
fcis-6024	38	1	after	after	SCONJ
fcis-6024	38	2	repeated	repeat	VERB
fcis-6024	38	3	such	such	ADJ
fcis-6024	38	4	top	top	ADJ
fcis-6024	38	5	-	-	PUNCT
fcis-6024	38	6	down	down	ADP
fcis-6024	38	7	feature	feature	NOUN
fcis-6024	38	8	fusion	fusion	NOUN
fcis-6024	38	9	three	three	NUM
fcis-6024	38	10	feature	feature	NOUN
fcis-6024	38	11	maps	map	NOUN
fcis-6024	38	12	of	of	ADP
fcis-6024	38	13	different	different	ADJ
fcis-6024	38	14	sizes	size	NOUN
fcis-6024	38	15	are	be	AUX
fcis-6024	38	16	obtained	obtain	VERB
fcis-6024	38	17	.	.	PUNCT
fcis-6024	39	1	using	use	VERB
fcis-6024	39	2	multi	multi	ADJ
fcis-6024	39	3	-	-	ADJ
fcis-6024	39	4	scale	scale	ADJ
fcis-6024	39	5	prediction	prediction	NOUN
fcis-6024	39	6	,	,	PUNCT
fcis-6024	39	7	the	the	DET
fcis-6024	39	8	target	target	NOUN
fcis-6024	39	9	is	be	AUX
fcis-6024	39	10	predicted	predict	VERB
fcis-6024	39	11	on	on	ADP
fcis-6024	39	12	three	three	NUM
fcis-6024	39	13	different	different	ADJ
fcis-6024	39	14	sizes	size	NOUN
fcis-6024	39	15	of	of	ADP
fcis-6024	39	16	feature	feature	NOUN
fcis-6024	39	17	maps	map	NOUN
fcis-6024	39	18	.	.	PUNCT
fcis-6024	40	1	in	in	ADP
fcis-6024	40	2	addition	addition	NOUN
fcis-6024	40	3	,	,	PUNCT
fcis-6024	40	4	for	for	ADP
fcis-6024	40	5	label	label	NOUN
fcis-6024	40	6	classification	classification	NOUN
fcis-6024	40	7	,	,	PUNCT
fcis-6024	40	8	yolov2	yolov2	PROPN
fcis-6024	40	9	uses	use	VERB
fcis-6024	40	10	the	the	DET
fcis-6024	40	11	softmax	softmax	NOUN
fcis-6024	40	12	function	function	NOUN
fcis-6024	40	13	,	,	PUNCT
fcis-6024	40	14	but	but	CCONJ
fcis-6024	40	15	when	when	SCONJ
fcis-6024	40	16	the	the	DET
fcis-6024	40	17	target	target	NOUN
fcis-6024	40	18	of	of	ADP
fcis-6024	40	19	the	the	DET
fcis-6024	40	20	detected	detect	VERB
fcis-6024	40	21	image	image	NOUN
fcis-6024	40	22	has	have	VERB
fcis-6024	40	23	more	more	ADJ
fcis-6024	40	24	than	than	ADP
fcis-6024	40	25	one	one	NUM
fcis-6024	40	26	category	category	NOUN
fcis-6024	40	27	,	,	PUNCT
fcis-6024	40	28	the	the	DET
fcis-6024	40	29	softmax	softmax	NOUN
fcis-6024	40	30	function	function	NOUN
fcis-6024	40	31	will	will	AUX
fcis-6024	40	32	only	only	ADV
fcis-6024	40	33	output	output	VERB
fcis-6024	40	34	the	the	DET
fcis-6024	40	35	prediction	prediction	NOUN
fcis-6024	40	36	type	type	NOUN
fcis-6024	40	37	of	of	ADP
fcis-6024	40	38	a	a	DET
fcis-6024	40	39	certain	certain	ADJ
fcis-6024	40	40	category	category	NOUN
fcis-6024	40	41	,	,	PUNCT
fcis-6024	40	42	so	so	CCONJ
fcis-6024	40	43	softmax	softmax	NOUN
fcis-6024	40	44	is	be	AUX
fcis-6024	40	45	not	not	PART
fcis-6024	40	46	suitable	suitable	ADJ
fcis-6024	40	47	for	for	ADP
fcis-6024	40	48	multi	multi	ADJ
fcis-6024	40	49	-	-	ADJ
fcis-6024	40	50	category	category	ADJ
fcis-6024	40	51	label	label	NOUN
fcis-6024	40	52	prediction	prediction	NOUN
fcis-6024	40	53	.	.	PUNCT
fcis-6024	41	1	yolov3	yolov3	PROPN
fcis-6024	41	2	uses	use	VERB
fcis-6024	41	3	an	an	DET
fcis-6024	41	4	independent	independent	ADJ
fcis-6024	41	5	logistic	logistic	ADJ
fcis-6024	41	6	classifier	classifier	NOUN
fcis-6024	41	7	instead	instead	ADV
fcis-6024	41	8	of	of	ADP
fcis-6024	41	9	softmax	softmax	NOUN
fcis-6024	41	10	to	to	PART
fcis-6024	41	11	compensate	compensate	VERB
fcis-6024	41	12	for	for	ADP
fcis-6024	41	13	this	this	DET
fcis-6024	41	14	drawback	drawback	NOUN
fcis-6024	41	15	.	.	PUNCT
fcis-6024	42	1	the	the	DET
fcis-6024	42	2	classifier	classifier	NOUN
fcis-6024	42	3	will	will	AUX
fcis-6024	42	4	assign	assign	VERB
fcis-6024	42	5	a	a	DET
fcis-6024	42	6	certain	certain	ADJ
fcis-6024	42	7	threshold	threshold	NOUN
fcis-6024	42	8	range	range	NOUN
fcis-6024	42	9	to	to	ADP
fcis-6024	42	10	each	each	DET
fcis-6024	42	11	category	category	NOUN
fcis-6024	42	12	,	,	PUNCT
fcis-6024	42	13	and	and	CCONJ
fcis-6024	42	14	when	when	SCONJ
fcis-6024	42	15	the	the	DET
fcis-6024	42	16	resulting	result	VERB
fcis-6024	42	17	data	datum	NOUN
fcis-6024	42	18	is	be	AUX
fcis-6024	42	19	within	within	ADP
fcis-6024	42	20	a	a	DET
fcis-6024	42	21	certain	certain	ADJ
fcis-6024	42	22	threshold	threshold	NOUN
fcis-6024	42	23	range	range	NOUN
fcis-6024	42	24	,	,	PUNCT
fcis-6024	42	25	it	it	PRON
fcis-6024	42	26	corresponds	correspond	VERB
fcis-6024	42	27	to	to	ADP
fcis-6024	42	28	a	a	DET
fcis-6024	42	29	certain	certain	ADJ
fcis-6024	42	30	category	category	NOUN
fcis-6024	42	31	prediction	prediction	NOUN
fcis-6024	42	32	.	.	PUNCT
fcis-6024	43	1	compared	compare	VERB
fcis-6024	43	2	with	with	ADP
fcis-6024	43	3	the	the	DET
fcis-6024	43	4	yolov2	yolov2	PROPN
fcis-6024	43	5	algorithm	algorithm	NOUN
fcis-6024	43	6	,	,	PUNCT
fcis-6024	43	7	yolov3	yolov3	PROPN
fcis-6024	43	8	greatly	greatly	ADV
fcis-6024	43	9	improves	improve	VERB
fcis-6024	43	10	the	the	DET
fcis-6024	43	11	detection	detection	NOUN
fcis-6024	43	12	accuracy	accuracy	NOUN
fcis-6024	43	13	with	with	ADP
fcis-6024	43	14	guaranteed	guarantee	VERB
fcis-6024	43	15	speed	speed	NOUN
fcis-6024	43	16	.	.	PUNCT
fcis-6024	44	1	yolov4[11	yolov4[11	NOUN
fcis-6024	44	2	]	]	X
fcis-6024	44	3	,	,	PUNCT
fcis-6024	44	4	as	as	ADP
fcis-6024	44	5	an	an	DET
fcis-6024	44	6	enhanced	enhanced	ADJ
fcis-6024	44	7	version	version	NOUN
fcis-6024	44	8	of	of	ADP
fcis-6024	44	9	yolov3	yolov3	PROPN
fcis-6024	44	10	,	,	PUNCT
fcis-6024	44	11	has	have	AUX
fcis-6024	44	12	made	make	VERB
fcis-6024	44	13	some	some	DET
fcis-6024	44	14	changes	change	NOUN
fcis-6024	44	15	in	in	ADP
fcis-6024	44	16	the	the	DET
fcis-6024	44	17	network	network	NOUN
fcis-6024	44	18	structure	structure	NOUN
fcis-6024	44	19	to	to	PART
fcis-6024	44	20	address	address	VERB
fcis-6024	44	21	the	the	DET
fcis-6024	44	22	shortcomings	shortcoming	NOUN
fcis-6024	44	23	of	of	ADP
fcis-6024	44	24	yolov3	yolov3	PROPN
fcis-6024	44	25	.	.	PUNCT
fcis-6024	45	1	yolov4	yolov4	PROPN
fcis-6024	45	2	divides	divide	VERB
fcis-6024	45	3	the	the	DET
fcis-6024	45	4	whole	whole	ADJ
fcis-6024	45	5	network	network	NOUN
fcis-6024	45	6	into	into	ADP
fcis-6024	45	7	three	three	NUM
fcis-6024	45	8	parts	part	NOUN
fcis-6024	45	9	,	,	PUNCT
fcis-6024	45	10	including	include	VERB
fcis-6024	45	11	backbone	backbone	NOUN
fcis-6024	45	12	,	,	PUNCT
fcis-6024	45	13	neck	neck	NOUN
fcis-6024	45	14	and	and	CCONJ
fcis-6024	45	15	head	head	NOUN
fcis-6024	45	16	,	,	PUNCT
fcis-6024	45	17	to	to	PART
fcis-6024	45	18	make	make	VERB
fcis-6024	45	19	the	the	DET
fcis-6024	45	20	overall	overall	ADJ
fcis-6024	45	21	structure	structure	NOUN
fcis-6024	45	22	of	of	ADP
fcis-6024	45	23	the	the	DET
fcis-6024	45	24	network	network	NOUN
fcis-6024	45	25	clearer	clear	ADJ
fcis-6024	45	26	,	,	PUNCT
fcis-6024	45	27	and	and	CCONJ
fcis-6024	45	28	yolov4	yolov4	NOUN
fcis-6024	45	29	adopts	adopt	VERB
fcis-6024	45	30	the	the	DET
fcis-6024	45	31	idea	idea	NOUN
fcis-6024	45	32	of	of	ADP
fcis-6024	45	33	sppnet	sppnet	NOUN
fcis-6024	45	34	to	to	PART
fcis-6024	45	35	increase	increase	VERB
fcis-6024	45	36	the	the	DET
fcis-6024	45	37	perceptual	perceptual	ADJ
fcis-6024	45	38	field	field	NOUN
fcis-6024	45	39	by	by	ADP
fcis-6024	45	40	adding	add	VERB
fcis-6024	45	41	a	a	DET
fcis-6024	45	42	spp	spp	NOUN
fcis-6024	45	43	layer	layer	NOUN
fcis-6024	45	44	after	after	SCONJ
fcis-6024	45	45	yolov4	yolov4	PROPN
fcis-6024	45	46	adopts	adopt	VERB
fcis-6024	45	47	the	the	DET
fcis-6024	45	48	idea	idea	NOUN
fcis-6024	45	49	of	of	ADP
fcis-6024	45	50	sppnet	sppnet	NOUN
fcis-6024	45	51	to	to	PART
fcis-6024	45	52	increase	increase	VERB
fcis-6024	45	53	the	the	DET
fcis-6024	45	54	perceptual	perceptual	ADJ
fcis-6024	45	55	field	field	NOUN
fcis-6024	45	56	,	,	PUNCT
fcis-6024	45	57	adding	add	VERB
fcis-6024	45	58	an	an	DET
fcis-6024	45	59	spp	spp	NOUN
fcis-6024	45	60	layer	layer	NOUN
fcis-6024	45	61	after	after	ADP
fcis-6024	45	62	backbone	backbone	NOUN
fcis-6024	45	63	,	,	PUNCT
fcis-6024	45	64	which	which	PRON
fcis-6024	45	65	actually	actually	ADV
fcis-6024	45	66	adopts	adopt	VERB
fcis-6024	45	67	different	different	ADJ
fcis-6024	45	68	size	size	NOUN
fcis-6024	45	69	of	of	ADP
fcis-6024	45	70	pool_size	pool_size	NOUN
fcis-6024	45	71	and	and	CCONJ
fcis-6024	45	72	strides	stride	NOUN
fcis-6024	45	73	to	to	PART
fcis-6024	45	74	realize	realize	VERB
fcis-6024	45	75	the	the	DET
fcis-6024	45	76	feature	feature	NOUN
fcis-6024	45	77	output	output	NOUN
fcis-6024	45	78	of	of	ADP
fcis-6024	45	79	different	different	ADJ
fcis-6024	45	80	perceptual	perceptual	ADJ
fcis-6024	45	81	fields	field	NOUN
fcis-6024	45	82	.	.	PUNCT
fcis-6024	46	1	2	2	X
fcis-6024	46	2	.	.	X
fcis-6024	46	3	yolov5	yolov5	NOUN
fcis-6024	46	4	framework	framework	NOUN
fcis-6024	46	5	yolov5	yolov5	NOUN
fcis-6024	46	6	contains	contain	VERB
fcis-6024	46	7	four	four	NUM
fcis-6024	46	8	versions	version	NOUN
fcis-6024	46	9	:	:	PUNCT
fcis-6024	46	10	yolov5s	yolov5s	PROPN
fcis-6024	46	11	,	,	PUNCT
fcis-6024	46	12	yolov5	yolov5	PROPN
fcis-6024	46	13	m	m	PROPN
fcis-6024	46	14	,	,	PUNCT
fcis-6024	46	15	yolov5x	yolov5x	PROPN
fcis-6024	46	16	,	,	PUNCT
fcis-6024	46	17	and	and	CCONJ
fcis-6024	46	18	yolov5l	yolov5l	PROPN
fcis-6024	46	19	.	.	PUNCT
fcis-6024	47	1	because	because	SCONJ
fcis-6024	47	2	yolov5s	yolov5s	PROPN
fcis-6024	47	3	has	have	VERB
fcis-6024	47	4	the	the	DET
fcis-6024	47	5	smallest	small	ADJ
fcis-6024	47	6	model	model	NOUN
fcis-6024	47	7	structure	structure	NOUN
fcis-6024	47	8	,	,	PUNCT
fcis-6024	47	9	the	the	DET
fcis-6024	47	10	smallest	small	ADJ
fcis-6024	47	11	number	number	NOUN
fcis-6024	47	12	of	of	ADP
fcis-6024	47	13	parameters	parameter	NOUN
fcis-6024	47	14	,	,	PUNCT
fcis-6024	47	15	and	and	CCONJ
fcis-6024	47	16	the	the	DET
fcis-6024	47	17	fastest	fast	ADJ
fcis-6024	47	18	running	running	NOUN
fcis-6024	47	19	speed	speed	NOUN
fcis-6024	47	20	among	among	ADP
fcis-6024	47	21	the	the	DET
fcis-6024	47	22	four	four	NUM
fcis-6024	47	23	versions	version	NOUN
fcis-6024	47	24	,	,	PUNCT
fcis-6024	47	25	this	this	DET
fcis-6024	47	26	paper	paper	NOUN
fcis-6024	47	27	selects	select	VERB
fcis-6024	47	28	yolov5s	yolov5s	PROPN
fcis-6024	47	29	as	as	ADP
fcis-6024	47	30	its	its	PRON
fcis-6024	47	31	backbone	backbone	NOUN
fcis-6024	47	32	network	network	NOUN
fcis-6024	47	33	,	,	PUNCT
fcis-6024	47	34	and	and	CCONJ
fcis-6024	47	35	the	the	DET
fcis-6024	47	36	overall	overall	ADJ
fcis-6024	47	37	framework	framework	NOUN
fcis-6024	47	38	of	of	ADP
fcis-6024	47	39	yolov5s	yolov5s	PROPN
fcis-6024	47	40	is	be	AUX
fcis-6024	47	41	shown	show	VERB
fcis-6024	47	42	in	in	ADP
fcis-6024	47	43	the	the	DET
fcis-6024	47	44	following	follow	VERB
fcis-6024	47	45	figure	figure	NOUN
fcis-6024	47	46	,	,	PUNCT
fcis-6024	47	47	which	which	PRON
fcis-6024	47	48	can	can	AUX
fcis-6024	47	49	be	be	AUX
fcis-6024	47	50	distinguished	distinguish	VERB
fcis-6024	47	51	into	into	ADP
fcis-6024	47	52	three	three	NUM
fcis-6024	47	53	parts	part	NOUN
fcis-6024	47	54	backbone	backbone	NOUN
fcis-6024	47	55	,	,	PUNCT
fcis-6024	47	56	neck	neck	NOUN
fcis-6024	47	57	,	,	PUNCT
fcis-6024	47	58	and	and	CCONJ
fcis-6024	47	59	head	head	NOUN
fcis-6024	47	60	.	.	PUNCT
fcis-6024	48	1	figure	figure	NOUN
fcis-6024	48	2	1	1	NUM
fcis-6024	48	3	.	.	PUNCT
fcis-6024	49	1	yolov5s	yolov5s	NOUN
fcis-6024	49	2	structure	structure	NOUN
fcis-6024	49	3	diagram	diagram	NOUN
fcis-6024	49	4	backbone	backbone	NOUN
fcis-6024	49	5	is	be	AUX
fcis-6024	49	6	the	the	DET
fcis-6024	49	7	backbone	backbone	NOUN
fcis-6024	49	8	network	network	NOUN
fcis-6024	49	9	of	of	ADP
fcis-6024	49	10	the	the	DET
fcis-6024	49	11	model	model	NOUN
fcis-6024	49	12	,	,	PUNCT
fcis-6024	49	13	and	and	CCONJ
fcis-6024	49	14	the	the	DET
fcis-6024	49	15	focus	focus	NOUN
fcis-6024	49	16	module	module	NOUN
fcis-6024	49	17	is	be	AUX
fcis-6024	49	18	removed	remove	VERB
fcis-6024	49	19	after	after	ADP
fcis-6024	49	20	v6.0	v6.0	NOUN
fcis-6024	49	21	.	.	PUNCT
fcis-6024	50	1	yolov5	yolov5	NOUN
fcis-6024	50	2	adopts	adopt	VERB
fcis-6024	50	3	the	the	DET
fcis-6024	50	4	same	same	ADJ
fcis-6024	50	5	mosaic	mosaic	ADJ
fcis-6024	50	6	data	datum	NOUN
fcis-6024	50	7	enhancement	enhancement	NOUN
fcis-6024	50	8	as	as	ADP
fcis-6024	50	9	yolov4	yolov4	PROPN
fcis-6024	50	10	,	,	PUNCT
fcis-6024	50	11	i.e.	i.e.	X
fcis-6024	50	12	,	,	PUNCT
fcis-6024	50	13	four	four	NUM
fcis-6024	50	14	images	image	NOUN
fcis-6024	50	15	are	be	AUX
fcis-6024	50	16	randomly	randomly	ADV
fcis-6024	50	17	selected	select	VERB
fcis-6024	50	18	from	from	ADP
fcis-6024	50	19	the	the	DET
fcis-6024	50	20	original	original	ADJ
fcis-6024	50	21	image	image	NOUN
fcis-6024	50	22	for	for	ADP
fcis-6024	50	23	stitching	stitching	NOUN
fcis-6024	50	24	,	,	PUNCT
fcis-6024	50	25	which	which	PRON
fcis-6024	50	26	can	can	AUX
fcis-6024	50	27	prevent	prevent	VERB
fcis-6024	50	28	data	datum	NOUN
fcis-6024	50	29	overfitting	overfitte	VERB
fcis-6024	50	30	and	and	CCONJ
fcis-6024	50	31	reduce	reduce	VERB
fcis-6024	50	32	the	the	DET
fcis-6024	50	33	local	local	ADJ
fcis-6024	50	34	loss	loss	NOUN
fcis-6024	50	35	caused	cause	VERB
fcis-6024	50	36	by	by	ADP
fcis-6024	50	37	data	datum	NOUN
fcis-6024	50	38	sampling	sampling	NOUN
fcis-6024	50	39	.	.	PUNCT
fcis-6024	51	1	the	the	DET
fcis-6024	51	2	cross	cross	ADJ
fcis-6024	51	3	-	-	ADJ
fcis-6024	51	4	stage	stage	ADJ
fcis-6024	51	5	localization	localization	NOUN
fcis-6024	51	6	(	(	PUNCT
fcis-6024	51	7	csp	csp	PROPN
fcis-6024	51	8	)	)	PUNCT
fcis-6024	51	9	module	module	NOUN
fcis-6024	51	10	is	be	AUX
fcis-6024	51	11	used	use	VERB
fcis-6024	51	12	in	in	ADP
fcis-6024	51	13	yolov5	yolov5	NOUN
fcis-6024	51	14	.	.	PUNCT
fcis-6024	52	1	there	there	PRON
fcis-6024	52	2	are	be	VERB
fcis-6024	52	3	two	two	NUM
fcis-6024	52	4	designs	design	NOUN
fcis-6024	52	5	in	in	ADP
fcis-6024	52	6	yolov5	yolov5	NOUN
fcis-6024	52	7	,	,	PUNCT
fcis-6024	52	8	csp1_x	csp1_x	ADJ
fcis-6024	52	9	structure	structure	NOUN
fcis-6024	52	10	and	and	CCONJ
fcis-6024	52	11	csp2_x	csp2_x	PROPN
fcis-6024	52	12	structure	structure	NOUN
fcis-6024	52	13	,	,	PUNCT
fcis-6024	52	14	the	the	DET
fcis-6024	52	15	former	former	NOUN
fcis-6024	52	16	contains	contain	VERB
fcis-6024	52	17	n	n	DET
fcis-6024	52	18	residual	residual	ADJ
fcis-6024	52	19	structure	structure	NOUN
fcis-6024	52	20	to	to	PART
fcis-6024	52	21	solve	solve	VERB
fcis-6024	52	22	the	the	DET
fcis-6024	52	23	degradation	degradation	NOUN
fcis-6024	52	24	problem	problem	NOUN
fcis-6024	52	25	of	of	ADP
fcis-6024	52	26	deep	deep	ADJ
fcis-6024	52	27	network	network	NOUN
fcis-6024	52	28	,	,	PUNCT
fcis-6024	52	29	the	the	DET
fcis-6024	52	30	latter	latter	ADJ
fcis-6024	52	31	mainly	mainly	ADV
fcis-6024	52	32	connects	connect	VERB
fcis-6024	52	33	neck	neck	NOUN
fcis-6024	52	34	part	part	NOUN
fcis-6024	52	35	to	to	PART
fcis-6024	52	36	fuse	fuse	VERB
fcis-6024	52	37	the	the	DET
fcis-6024	52	38	semantic	semantic	ADJ
fcis-6024	52	39	information	information	NOUN
fcis-6024	52	40	of	of	ADP
fcis-6024	52	41	high	high	ADJ
fcis-6024	52	42	level	level	NOUN
fcis-6024	52	43	with	with	ADP
fcis-6024	52	44	the	the	DET
fcis-6024	52	45	location	location	NOUN
fcis-6024	52	46	information	information	NOUN
fcis-6024	52	47	of	of	ADP
fcis-6024	52	48	bottom	bottom	ADJ
fcis-6024	52	49	level	level	NOUN
fcis-6024	52	50	,	,	PUNCT
fcis-6024	52	51	the	the	DET
fcis-6024	52	52	output	output	NOUN
fcis-6024	52	53	has	have	VERB
fcis-6024	52	54	3	3	NUM
fcis-6024	52	55	different	different	ADJ
fcis-6024	52	56	sizes	size	NOUN
fcis-6024	52	57	of	of	ADP
fcis-6024	52	58	probe	probe	NOUN
fcis-6024	52	59	head	head	NOUN
fcis-6024	52	60	,	,	PUNCT
fcis-6024	52	61	the	the	DET
fcis-6024	52	62	generated	generate	VERB
fcis-6024	52	63	prediction	prediction	NOUN
fcis-6024	52	64	frame	frame	NOUN
fcis-6024	52	65	is	be	AUX
fcis-6024	52	66	nms	nms	PROPN
fcis-6024	52	67	suppressed	suppress	VERB
fcis-6024	52	68	to	to	PART
fcis-6024	52	69	save	save	VERB
fcis-6024	52	70	the	the	DET
fcis-6024	52	71	higher	high	ADJ
fcis-6024	52	72	confidence	confidence	NOUN
fcis-6024	52	73	prediction	prediction	NOUN
fcis-6024	52	74	frames	frame	NOUN
fcis-6024	52	75	.	.	PUNCT
fcis-6024	53	1	3	3	X
fcis-6024	53	2	.	.	X
fcis-6024	53	3	hybrid	hybrid	ADJ
fcis-6024	53	4	self	self	NOUN
fcis-6024	53	5	-	-	PUNCT
fcis-6024	53	6	attention	attention	NOUN
fcis-6024	53	7	mechanism	mechanism	NOUN
fcis-6024	53	8	(	(	PUNCT
fcis-6024	53	9	acmix	acmix	PROPN
fcis-6024	53	10	)	)	PUNCT
fcis-6024	53	11	the	the	DET
fcis-6024	53	12	acmix	acmix	NOUN
fcis-6024	53	13	hybrid	hybrid	ADJ
fcis-6024	53	14	attention	attention	NOUN
fcis-6024	53	15	mechanism	mechanism	NOUN
fcis-6024	53	16	[	[	X
fcis-6024	53	17	12	12	NUM
fcis-6024	53	18	]	]	PUNCT
fcis-6024	53	19	combines	combine	VERB
fcis-6024	53	20	the	the	DET
fcis-6024	53	21	advantages	advantage	NOUN
fcis-6024	53	22	of	of	ADP
fcis-6024	53	23	both	both	CCONJ
fcis-6024	53	24	traditional	traditional	ADJ
fcis-6024	53	25	convolutional	convolutional	ADJ
fcis-6024	53	26	and	and	CCONJ
fcis-6024	53	27	selfattentive	selfattentive	ADJ
fcis-6024	53	28	mechanisms	mechanism	NOUN
fcis-6024	53	29	.	.	PUNCT
fcis-6024	54	1	the	the	DET
fcis-6024	54	2	former	former	ADJ
fcis-6024	54	3	uses	use	VERB
fcis-6024	54	4	an	an	DET
fcis-6024	54	5	aggregation	aggregation	NOUN
fcis-6024	54	6	function	function	NOUN
fcis-6024	54	7	over	over	ADP
fcis-6024	54	8	the	the	DET
fcis-6024	54	9	local	local	ADJ
fcis-6024	54	10	receptive	receptive	ADJ
fcis-6024	54	11	field	field	NOUN
fcis-6024	54	12	based	base	VERB
fcis-6024	54	13	on	on	ADP
fcis-6024	54	14	convolutional	convolutional	ADJ
fcis-6024	54	15	filter	filter	NOUN
fcis-6024	54	16	weights	weight	NOUN
fcis-6024	54	17	,	,	PUNCT
fcis-6024	54	18	which	which	PRON
fcis-6024	54	19	are	be	AUX
fcis-6024	54	20	shared	share	VERB
fcis-6024	54	21	throughout	throughout	ADP
fcis-6024	54	22	the	the	DET
fcis-6024	54	23	network	network	NOUN
fcis-6024	54	24	structure	structure	NOUN
fcis-6024	54	25	to	to	PART
fcis-6024	54	26	obtain	obtain	VERB
fcis-6024	54	27	indispensable	indispensable	ADJ
fcis-6024	54	28	inductive	inductive	ADJ
fcis-6024	54	29	bias	bias	NOUN
fcis-6024	54	30	with	with	ADP
fcis-6024	54	31	its	its	PRON
fcis-6024	54	32	inherent	inherent	ADJ
fcis-6024	54	33	properties	property	NOUN
fcis-6024	54	34	.	.	PUNCT
fcis-6024	55	1	the	the	DET
fcis-6024	55	2	latter	latter	ADJ
fcis-6024	55	3	uses	use	VERB
fcis-6024	55	4	a	a	DET
fcis-6024	55	5	weighted	weighted	ADJ
fcis-6024	55	6	averaging	averaging	NOUN
fcis-6024	55	7	operation	operation	NOUN
fcis-6024	55	8	based	base	VERB
fcis-6024	55	9	on	on	ADP
fcis-6024	55	10	the	the	DET
fcis-6024	55	11	input	input	NOUN
fcis-6024	55	12	feature	feature	NOUN
fcis-6024	55	13	context	context	NOUN
fcis-6024	55	14	,	,	PUNCT
fcis-6024	55	15	and	and	CCONJ
fcis-6024	55	16	the	the	DET
fcis-6024	55	17	attention	attention	NOUN
fcis-6024	55	18	weights	weight	NOUN
fcis-6024	55	19	are	be	AUX
fcis-6024	55	20	dynamically	dynamically	ADV
fcis-6024	55	21	calculated	calculate	VERB
fcis-6024	55	22	by	by	ADP
fcis-6024	55	23	the	the	DET
fcis-6024	55	24	similarity	similarity	NOUN
fcis-6024	55	25	function	function	NOUN
fcis-6024	55	26	between	between	ADP
fcis-6024	55	27	neighboring	neighbor	VERB
fcis-6024	55	28	pixel	pixel	NOUN
fcis-6024	55	29	pairs	pair	NOUN
fcis-6024	55	30	,	,	PUNCT
fcis-6024	55	31	so	so	SCONJ
fcis-6024	55	32	as	as	SCONJ
fcis-6024	55	33	to	to	PART
fcis-6024	55	34	expand	expand	VERB
fcis-6024	55	35	the	the	DET
fcis-6024	55	36	perceptual	perceptual	ADJ
fcis-6024	55	37	field	field	NOUN
fcis-6024	55	38	of	of	ADP
fcis-6024	55	39	the	the	DET
fcis-6024	55	40	network	network	NOUN
fcis-6024	55	41	model	model	NOUN
fcis-6024	55	42	,	,	PUNCT
fcis-6024	55	43	focus	focus	VERB
fcis-6024	55	44	on	on	ADP
fcis-6024	55	45	different	different	ADJ
fcis-6024	55	46	target	target	NOUN
fcis-6024	55	47	regions	region	NOUN
fcis-6024	55	48	adaptively	adaptively	ADV
fcis-6024	55	49	,	,	PUNCT
fcis-6024	55	50	capture	capture	VERB
fcis-6024	55	51	more	more	ADJ
fcis-6024	55	52	information	information	NOUN
fcis-6024	55	53	features	feature	NOUN
fcis-6024	55	54	,	,	PUNCT
fcis-6024	55	55	etc	etc	X
fcis-6024	55	56	.	.	X
fcis-6024	55	57	figure	figure	VERB
fcis-6024	55	58	2	2	NUM
fcis-6024	55	59	.	.	X
fcis-6024	55	60	acmix	acmix	PROPN
fcis-6024	55	61	hybrid	hybrid	ADJ
fcis-6024	55	62	self	self	NOUN
fcis-6024	55	63	-	-	PUNCT
fcis-6024	55	64	attention	attention	NOUN
fcis-6024	55	65	structure	structure	NOUN
fcis-6024	55	66	58	58	NUM
fcis-6024	55	67	traditional	traditional	ADJ
fcis-6024	55	68	convolution	convolution	NOUN
fcis-6024	55	69	and	and	CCONJ
fcis-6024	55	70	self	self	NOUN
fcis-6024	55	71	-	-	PUNCT
fcis-6024	55	72	attention	attention	NOUN
fcis-6024	55	73	modules	module	NOUN
fcis-6024	55	74	actually	actually	ADV
fcis-6024	55	75	share	share	VERB
fcis-6024	55	76	the	the	DET
fcis-6024	55	77	same	same	ADJ
fcis-6024	55	78	operation	operation	NOUN
fcis-6024	55	79	when	when	SCONJ
fcis-6024	55	80	projecting	project	VERB
fcis-6024	55	81	input	input	NOUN
fcis-6024	55	82	feature	feature	NOUN
fcis-6024	55	83	maps	map	NOUN
fcis-6024	55	84	by	by	ADP
fcis-6024	55	85	1×1	1×1	NUM
fcis-6024	55	86	convolution	convolution	NOUN
fcis-6024	55	87	,	,	PUNCT
fcis-6024	55	88	and	and	CCONJ
fcis-6024	55	89	these	these	DET
fcis-6024	55	90	two	two	NUM
fcis-6024	55	91	operations	operation	NOUN
fcis-6024	55	92	take	take	VERB
fcis-6024	55	93	up	up	ADP
fcis-6024	55	94	the	the	DET
fcis-6024	55	95	main	main	ADJ
fcis-6024	55	96	computational	computational	ADJ
fcis-6024	55	97	overhead	overhead	NOUN
fcis-6024	55	98	.	.	PUNCT
fcis-6024	56	1	based	base	VERB
fcis-6024	56	2	on	on	ADP
fcis-6024	56	3	this	this	DET
fcis-6024	56	4	idea	idea	NOUN
fcis-6024	56	5	,	,	PUNCT
fcis-6024	56	6	the	the	DET
fcis-6024	56	7	acmix	acmix	NOUN
fcis-6024	56	8	hybrid	hybrid	ADJ
fcis-6024	56	9	self	self	NOUN
fcis-6024	56	10	-	-	PUNCT
fcis-6024	56	11	attention	attention	NOUN
fcis-6024	56	12	mechanism	mechanism	NOUN
fcis-6024	56	13	is	be	AUX
fcis-6024	56	14	mainly	mainly	ADV
fcis-6024	56	15	divided	divide	VERB
fcis-6024	56	16	into	into	ADP
fcis-6024	56	17	two	two	NUM
fcis-6024	56	18	stages	stage	NOUN
fcis-6024	56	19	,	,	PUNCT
fcis-6024	56	20	as	as	SCONJ
fcis-6024	56	21	shown	show	VERB
fcis-6024	56	22	in	in	ADP
fcis-6024	56	23	the	the	DET
fcis-6024	56	24	figure	figure	NOUN
fcis-6024	56	25	,	,	PUNCT
fcis-6024	56	26	in	in	ADP
fcis-6024	56	27	the	the	DET
fcis-6024	56	28	first	first	ADJ
fcis-6024	56	29	stage	stage	NOUN
fcis-6024	56	30	,	,	PUNCT
fcis-6024	56	31	the	the	DET
fcis-6024	56	32	input	input	NOUN
fcis-6024	56	33	tensor	tensor	NOUN
fcis-6024	56	34	of	of	ADP
fcis-6024	56	35	h×w×c	h×w×c	ADJ
fcis-6024	56	36	is	be	AUX
fcis-6024	56	37	projected	project	VERB
fcis-6024	56	38	in	in	ADP
fcis-6024	56	39	three	three	NUM
fcis-6024	56	40	1×1	1×1	ADJ
fcis-6024	56	41	convolutions	convolution	NOUN
fcis-6024	56	42	and	and	CCONJ
fcis-6024	56	43	reshaped	reshape	VERB
fcis-6024	56	44	into	into	ADP
fcis-6024	56	45	n	n	PRON
fcis-6024	56	46	to	to	PART
fcis-6024	56	47	obtain	obtain	VERB
fcis-6024	56	48	an	an	DET
fcis-6024	56	49	intermediate	intermediate	ADJ
fcis-6024	56	50	feature	feature	NOUN
fcis-6024	56	51	set	set	VERB
fcis-6024	56	52	containing	contain	VERB
fcis-6024	56	53	3n	3n	NUM
fcis-6024	56	54	feature	feature	NOUN
fcis-6024	56	55	maps	map	NOUN
fcis-6024	56	56	,	,	PUNCT
fcis-6024	56	57	and	and	CCONJ
fcis-6024	56	58	in	in	ADP
fcis-6024	56	59	the	the	DET
fcis-6024	56	60	second	second	ADJ
fcis-6024	56	61	stage	stage	NOUN
fcis-6024	56	62	,	,	PUNCT
fcis-6024	56	63	for	for	ADP
fcis-6024	56	64	the	the	DET
fcis-6024	56	65	self	self	NOUN
fcis-6024	56	66	-	-	PUNCT
fcis-6024	56	67	attention	attention	NOUN
fcis-6024	56	68	module	module	NOUN
fcis-6024	56	69	,	,	PUNCT
fcis-6024	56	70	acmix	acmix	NOUN
fcis-6024	56	71	aggregates	aggregate	VERB
fcis-6024	56	72	the	the	DET
fcis-6024	56	73	intermediate	intermediate	ADJ
fcis-6024	56	74	features	feature	NOUN
fcis-6024	56	75	into	into	ADP
fcis-6024	56	76	n	n	DET
fcis-6024	56	77	groups	group	NOUN
fcis-6024	56	78	,	,	PUNCT
fcis-6024	56	79	each	each	PRON
fcis-6024	56	80	containing	contain	VERB
fcis-6024	56	81	three	three	NUM
fcis-6024	56	82	feature	feature	NOUN
fcis-6024	56	83	maps	map	NOUN
fcis-6024	56	84	,	,	PUNCT
fcis-6024	56	85	each	each	PRON
fcis-6024	56	86	from	from	ADP
fcis-6024	56	87	1	1	NUM
fcis-6024	56	88	×	×	NOUN
fcis-6024	56	89	1	1	NUM
fcis-6024	56	90	convolution	convolution	NOUN
fcis-6024	56	91	,	,	PUNCT
fcis-6024	56	92	and	and	CCONJ
fcis-6024	56	93	the	the	DET
fcis-6024	56	94	corresponding	corresponding	ADJ
fcis-6024	56	95	feature	feature	NOUN
fcis-6024	56	96	maps	map	NOUN
fcis-6024	56	97	denote	denote	VERB
fcis-6024	56	98	query	query	NOUN
fcis-6024	56	99	,	,	PUNCT
fcis-6024	56	100	key	key	ADJ
fcis-6024	56	101	and	and	CCONJ
fcis-6024	56	102	value	value	NOUN
fcis-6024	56	103	,	,	PUNCT
fcis-6024	56	104	respectively	respectively	ADV
fcis-6024	56	105	,	,	PUNCT
fcis-6024	56	106	following	follow	VERB
fcis-6024	56	107	the	the	DET
fcis-6024	56	108	traditional	traditional	ADJ
fcis-6024	56	109	multi	multi	ADJ
fcis-6024	56	110	-	-	ADJ
fcis-6024	56	111	headed	headed	ADJ
fcis-6024	56	112	self	self	NOUN
fcis-6024	56	113	-	-	PUNCT
fcis-6024	56	114	attention	attention	NOUN
fcis-6024	56	115	model	model	NOUN
fcis-6024	56	116	,	,	PUNCT
fcis-6024	56	117	as	as	SCONJ
fcis-6024	56	118	shown	show	VERB
fcis-6024	56	119	in	in	ADP
fcis-6024	56	120	equation	equation	NOUN
fcis-6024	56	121	(	(	PUNCT
fcis-6024	56	122	1	1	NUM
fcis-6024	56	123	)	)	PUNCT
fcis-6024	56	124	𝑔𝑖𝑗	𝑔𝑖𝑗	NOUN
fcis-6024	56	125	=	=	SYM
fcis-6024	57	1	𝑁	𝑁	PROPN
fcis-6024	57	2	||	||	NOUN
fcis-6024	57	3	𝑙	𝑙	NOUN
fcis-6024	57	4	=	=	SYM
fcis-6024	57	5	1	1	NUM
fcis-6024	57	6	(	(	PUNCT
fcis-6024	57	7	∑	∑	ADV
fcis-6024	57	8	𝐴(𝑞𝑖𝑗	𝐴(𝑞𝑖𝑗	PROPN
fcis-6024	57	9	(	(	PUNCT
fcis-6024	57	10	𝑙	𝑙	NOUN
fcis-6024	57	11	)	)	PUNCT
fcis-6024	57	12	,	,	PUNCT
fcis-6024	57	13	𝑘𝑎𝑏	𝑘𝑎𝑏	X
fcis-6024	57	14	(	(	PUNCT
fcis-6024	57	15	𝑙	𝑙	NOUN
fcis-6024	57	16	)	)	PUNCT
fcis-6024	57	17	)	)	PUNCT
fcis-6024	58	1	𝑉𝑎𝑏	𝑉𝑎𝑏	PROPN
fcis-6024	58	2	(	(	PUNCT
fcis-6024	58	3	𝑙	𝑙	NOUN
fcis-6024	58	4	)	)	PUNCT
fcis-6024	58	5	𝑎,𝑏∈𝑁𝑘(𝑖,𝑗	𝑎,𝑏∈𝑁𝑘(𝑖,𝑗	NOUN
fcis-6024	58	6	)	)	PUNCT
fcis-6024	58	7	)	)	PUNCT
fcis-6024	58	8	)	)	PUNCT
fcis-6024	59	1	(	(	PUNCT
fcis-6024	59	2	1	1	X
fcis-6024	59	3	)	)	PUNCT
fcis-6024	59	4	where𝑔𝑖𝑗	where𝑔𝑖𝑗	NOUN
fcis-6024	59	5	denotes	denote	VERB
fcis-6024	59	6	the	the	DET
fcis-6024	59	7	projection	projection	NOUN
fcis-6024	59	8	tensor	tensor	NOUN
fcis-6024	59	9	corresponding	correspond	VERB
fcis-6024	59	10	to	to	ADP
fcis-6024	59	11	pixel	pixel	PROPN
fcis-6024	59	12	(	(	PUNCT
fcis-6024	59	13	i	i	PROPN
fcis-6024	59	14	,	,	PUNCT
fcis-6024	59	15	j	j	PROPN
fcis-6024	59	16	)	)	PUNCT
fcis-6024	59	17	,	,	PUNCT
fcis-6024	60	1	||	||	PROPN
fcis-6024	61	1	denotes	denote	VERB
fcis-6024	61	2	the	the	DET
fcis-6024	61	3	concatenation	concatenation	NOUN
fcis-6024	61	4	of	of	ADP
fcis-6024	61	5	n	n	PRON
fcis-6024	61	6	attention	attention	NOUN
fcis-6024	61	7	head	head	NOUN
fcis-6024	61	8	outputs	output	NOUN
fcis-6024	61	9	,	,	PUNCT
fcis-6024	61	10	and𝑁𝑘(𝑖,𝑗	and𝑁𝑘(𝑖,𝑗	NOUN
fcis-6024	61	11	)	)	PUNCT
fcis-6024	61	12	denotes	denote	VERB
fcis-6024	61	13	the	the	DET
fcis-6024	61	14	local	local	ADJ
fcis-6024	61	15	region	region	NOUN
fcis-6024	61	16	of	of	ADP
fcis-6024	61	17	pixel	pixel	PROPN
fcis-6024	61	18	space	space	NOUN
fcis-6024	61	19	centered	center	VERB
fcis-6024	61	20	at	at	ADP
fcis-6024	61	21	(	(	PUNCT
fcis-6024	61	22	i	i	PROPN
fcis-6024	61	23	,	,	PUNCT
fcis-6024	61	24	j	j	PROPN
fcis-6024	61	25	)	)	PUNCT
fcis-6024	61	26	ranging	range	VERB
fcis-6024	61	27	over	over	ADP
fcis-6024	61	28	k𝐴(𝑞𝑖𝑗	k𝐴(𝑞𝑖𝑗	PROPN
fcis-6024	61	29	(	(	PUNCT
fcis-6024	61	30	𝑙	𝑙	PROPN
fcis-6024	61	31	)	)	PUNCT
fcis-6024	61	32	,	,	PUNCT
fcis-6024	61	33	𝑘𝑎𝑏	𝑘𝑎𝑏	X
fcis-6024	61	34	(	(	PUNCT
fcis-6024	61	35	𝑙	𝑙	NOUN
fcis-6024	61	36	)	)	PUNCT
fcis-6024	61	37	)	)	PUNCT
fcis-6024	61	38	corresponding	correspond	VERB
fcis-6024	61	39	to𝑁𝑘(𝑖,𝑗	to𝑁𝑘(𝑖,𝑗	NOUN
fcis-6024	61	40	)	)	PUNCT
fcis-6024	61	41	the	the	DET
fcis-6024	61	42	attentional	attentional	ADJ
fcis-6024	61	43	power	power	NOUN
fcis-6024	61	44	of	of	ADP
fcis-6024	61	45	the	the	DET
fcis-6024	61	46	features	feature	NOUN
fcis-6024	61	47	within	within	ADP
fcis-6024	61	48	weight	weight	NOUN
fcis-6024	61	49	,	,	PUNCT
fcis-6024	61	50	the	the	DET
fcis-6024	61	51	size	size	NOUN
fcis-6024	61	52	of	of	ADP
fcis-6024	61	53	the	the	DET
fcis-6024	61	54	attention	attention	NOUN
fcis-6024	61	55	weight	weight	NOUN
fcis-6024	61	56	assigned	assign	VERB
fcis-6024	61	57	to	to	ADP
fcis-6024	61	58	value	value	NOUN
fcis-6024	61	59	depends	depend	VERB
fcis-6024	61	60	on	on	ADP
fcis-6024	61	61	the	the	DET
fcis-6024	61	62	matching	matching	NOUN
fcis-6024	61	63	similarity	similarity	NOUN
fcis-6024	61	64	between	between	ADP
fcis-6024	61	65	query	query	NOUN
fcis-6024	61	66	and	and	CCONJ
fcis-6024	61	67	key	key	NOUN
fcis-6024	61	68	,	,	PUNCT
fcis-6024	61	69	if	if	SCONJ
fcis-6024	61	70	the	the	DET
fcis-6024	61	71	similarity	similarity	NOUN
fcis-6024	61	72	is	be	AUX
fcis-6024	61	73	higher	high	ADJ
fcis-6024	61	74	,	,	PUNCT
fcis-6024	61	75	the	the	DET
fcis-6024	61	76	size	size	NOUN
fcis-6024	61	77	of	of	ADP
fcis-6024	61	78	the	the	DET
fcis-6024	61	79	weight	weight	NOUN
fcis-6024	61	80	assigned	assign	VERB
fcis-6024	61	81	is	be	AUX
fcis-6024	61	82	consequently	consequently	ADV
fcis-6024	61	83	larger	large	ADJ
fcis-6024	61	84	and	and	CCONJ
fcis-6024	61	85	vice	vice	ADV
fcis-6024	61	86	versa	versa	ADV
fcis-6024	61	87	;	;	PUNCT
fcis-6024	61	88	for	for	ADP
fcis-6024	61	89	the	the	DET
fcis-6024	61	90	traditional	traditional	ADJ
fcis-6024	61	91	convolutional	convolutional	ADJ
fcis-6024	61	92	path	path	NOUN
fcis-6024	61	93	,	,	PUNCT
fcis-6024	61	94	the	the	DET
fcis-6024	61	95	convolution	convolution	NOUN
fcis-6024	61	96	with	with	ADP
fcis-6024	61	97	a	a	DET
fcis-6024	61	98	convolutional	convolutional	ADJ
fcis-6024	61	99	kernel	kernel	NOUN
fcis-6024	61	100	of	of	ADP
fcis-6024	61	101	k	k	PROPN
fcis-6024	61	102	as	as	SCONJ
fcis-6024	61	103	shown	show	VERB
fcis-6024	61	104	in	in	ADP
fcis-6024	61	105	equation	equation	NOUN
fcis-6024	61	106	(	(	PUNCT
fcis-6024	61	107	2	2	NUM
fcis-6024	61	108	)	)	PUNCT
fcis-6024	61	109	(	(	PUNCT
fcis-6024	61	110	3	3	X
fcis-6024	61	111	)	)	PUNCT
fcis-6024	61	112	is	be	AUX
fcis-6024	61	113	connected	connect	VERB
fcis-6024	61	114	by	by	ADP
fcis-6024	61	115	a	a	DET
fcis-6024	61	116	lightweight	lightweight	NOUN
fcis-6024	61	117	fully	fully	ADV
fcis-6024	61	118	connected	connect	VERB
fcis-6024	61	119	layer	layer	NOUN
fcis-6024	61	120	to	to	PART
fcis-6024	61	121	obtain	obtain	VERB
fcis-6024	61	122	k^2	k^2	PROPN
fcis-6024	61	123	feature	feature	NOUN
fcis-6024	61	124	maps	map	NOUN
fcis-6024	61	125	,	,	PUNCT
fcis-6024	61	126	generated	generate	VERB
fcis-6024	61	127	by	by	ADP
fcis-6024	61	128	moving	move	VERB
fcis-6024	61	129	and	and	CCONJ
fcis-6024	61	130	aggregating	aggregate	VERB
fcis-6024	61	131	the	the	DET
fcis-6024	61	132	features	feature	NOUN
fcis-6024	61	133	,	,	PUNCT
fcis-6024	61	134	shown	show	VERB
fcis-6024	61	135	in	in	ADP
fcis-6024	61	136	equation	equation	NOUN
fcis-6024	61	137	xx	xx	NUM
fcis-6024	61	138	,	,	PUNCT
fcis-6024	61	139	to	to	PART
fcis-6024	61	140	process	process	VERB
fcis-6024	61	141	the	the	DET
fcis-6024	61	142	input	input	NOUN
fcis-6024	61	143	features	feature	VERB
fcis-6024	61	144	in	in	ADP
fcis-6024	61	145	a	a	DET
fcis-6024	61	146	convolutional	convolutional	ADJ
fcis-6024	61	147	manner	manner	NOUN
fcis-6024	61	148	and	and	CCONJ
fcis-6024	61	149	collect	collect	VERB
fcis-6024	61	150	information	information	NOUN
fcis-6024	61	151	from	from	ADP
fcis-6024	61	152	the	the	DET
fcis-6024	61	153	local	local	ADJ
fcis-6024	61	154	receiver	receiver	NOUN
fcis-6024	61	155	domain	domain	NOUN
fcis-6024	61	156	as	as	ADP
fcis-6024	61	157	in	in	ADP
fcis-6024	61	158	the	the	DET
fcis-6024	61	159	traditional	traditional	ADJ
fcis-6024	61	160	receiver	receiver	ADJ
fcis-6024	61	161	domain	domain	NOUN
fcis-6024	61	162	.	.	PUNCT
fcis-6024	62	1	𝑔𝑖𝑗	𝑔𝑖𝑗	NOUN
fcis-6024	62	2	(	(	PUNCT
fcis-6024	62	3	𝑝,𝑞	𝑝,𝑞	NOUN
fcis-6024	62	4	)	)	PUNCT
fcis-6024	62	5	=	=	PUNCT
fcis-6024	62	6	𝑆ℎ𝑖𝑓𝑡(	𝑆ℎ𝑖𝑓𝑡(	PROPN
fcis-6024	62	7	�	�	PROPN
fcis-6024	62	8	̃	̃	PROPN
fcis-6024	62	9	�	�	PROPN
fcis-6024	62	10	𝑖𝑗	𝑖𝑗	NOUN
fcis-6024	62	11	(	(	PUNCT
fcis-6024	62	12	𝑝,𝑞	𝑝,𝑞	NOUN
fcis-6024	62	13	)	)	PUNCT
fcis-6024	62	14	,	,	PUNCT
fcis-6024	62	15	𝑝	𝑝	ADP
fcis-6024	62	16	−	−	PROPN
fcis-6024	63	1	[	[	PUNCT
fcis-6024	63	2	𝑘	𝑘	PROPN
fcis-6024	63	3	2	2	NUM
fcis-6024	63	4	,	,	PUNCT
fcis-6024	63	5	𝑞	𝑞	X
fcis-6024	63	6	−	−	PROPN
fcis-6024	63	7	[	[	PUNCT
fcis-6024	63	8	𝑘	𝑘	PROPN
fcis-6024	63	9	2	2	NUM
fcis-6024	63	10	]	]	PUNCT
fcis-6024	63	11	]	]	PUNCT
fcis-6024	63	12	)	)	PUNCT
fcis-6024	63	13	(	(	PUNCT
fcis-6024	63	14	2	2	X
fcis-6024	63	15	)	)	PUNCT
fcis-6024	63	16	𝑔	𝑔	PROPN
fcis-6024	63	17	𝑖𝑗=∑	𝑖𝑗=∑	PROPN
fcis-6024	63	18	𝑔𝑖𝑗	𝑔𝑖𝑗	PROPN
fcis-6024	63	19	(	(	PUNCT
fcis-6024	63	20	𝑝,𝑞	𝑝,𝑞	NOUN
fcis-6024	63	21	)	)	PUNCT
fcis-6024	63	22	𝑝,𝑞	𝑝,𝑞	NOUN
fcis-6024	63	23	(	(	PUNCT
fcis-6024	63	24	3	3	NUM
fcis-6024	63	25	)	)	PUNCT
fcis-6024	63	26	finally	finally	ADV
fcis-6024	63	27	,	,	PUNCT
fcis-6024	63	28	the	the	DET
fcis-6024	63	29	two	two	NUM
fcis-6024	63	30	paths	path	NOUN
fcis-6024	63	31	of	of	ADP
fcis-6024	63	32	the	the	DET
fcis-6024	63	33	conventional	conventional	ADJ
fcis-6024	63	34	convolution	convolution	NOUN
fcis-6024	63	35	and	and	CCONJ
fcis-6024	63	36	the	the	DET
fcis-6024	63	37	self	self	NOUN
fcis-6024	63	38	-	-	PUNCT
fcis-6024	63	39	attentive	attentive	NOUN
fcis-6024	63	40	module	module	NOUN
fcis-6024	63	41	are	be	AUX
fcis-6024	63	42	summed	sum	VERB
fcis-6024	63	43	,	,	PUNCT
fcis-6024	63	44	and	and	CCONJ
fcis-6024	63	45	the	the	DET
fcis-6024	63	46	intensity	intensity	NOUN
fcis-6024	63	47	is	be	AUX
fcis-6024	63	48	controlled	control	VERB
fcis-6024	63	49	by	by	ADP
fcis-6024	63	50	the	the	DET
fcis-6024	63	51	two	two	NUM
fcis-6024	63	52	learnable	learnable	ADJ
fcis-6024	63	53	scalars	scalar	NOUN
fcis-6024	63	54	𝛼	𝛼	NOUN
fcis-6024	63	55	,	,	PUNCT
fcis-6024	64	1	𝛽	𝛽	NOUN
fcis-6024	64	2	controlled	control	VERB
fcis-6024	64	3	by	by	ADP
fcis-6024	64	4	equation	equation	NOUN
fcis-6024	64	5	4	4	NUM
fcis-6024	64	6	𝐹𝑜𝑢𝑡=𝛼𝐹𝑎𝑡𝑡	𝐹𝑜𝑢𝑡=𝛼𝐹𝑎𝑡𝑡	ADJ
fcis-6024	64	7	+	+	CCONJ
fcis-6024	64	8	𝛽𝐹𝑐𝑜𝑛𝑣	𝛽𝐹𝑐𝑜𝑛𝑣	PROPN
fcis-6024	64	9	(	(	PUNCT
fcis-6024	64	10	4	4	NUM
fcis-6024	64	11	)	)	PUNCT
fcis-6024	64	12	where	where	SCONJ
fcis-6024	64	13	𝐹𝑜𝑢𝑡	𝐹𝑜𝑢𝑡	PROPN
fcis-6024	64	14	denotes	denote	VERB
fcis-6024	64	15	the	the	DET
fcis-6024	64	16	final	final	ADJ
fcis-6024	64	17	path	path	NOUN
fcis-6024	64	18	output	output	NOUN
fcis-6024	64	19	of	of	ADP
fcis-6024	64	20	the	the	DET
fcis-6024	64	21	acmix	acmix	NOUN
fcis-6024	64	22	module	module	NOUN
fcis-6024	64	23	,	,	PUNCT
fcis-6024	64	24	and	and	CCONJ
fcis-6024	64	25	𝐹𝑎𝑡𝑡	𝐹𝑎𝑡𝑡	PROPN
fcis-6024	64	26	denotes	denote	VERB
fcis-6024	64	27	the	the	DET
fcis-6024	64	28	path	path	NOUN
fcis-6024	64	29	output	output	NOUN
fcis-6024	64	30	of	of	ADP
fcis-6024	64	31	the	the	DET
fcis-6024	64	32	second	second	ADJ
fcis-6024	64	33	-	-	PUNCT
fcis-6024	64	34	stage	stage	NOUN
fcis-6024	64	35	self	self	NOUN
fcis-6024	64	36	-	-	PUNCT
fcis-6024	64	37	attentive	attentive	ADJ
fcis-6024	64	38	module	module	NOUN
fcis-6024	64	39	branch	branch	NOUN
fcis-6024	64	40	,	,	PUNCT
fcis-6024	64	41	and	and	CCONJ
fcis-6024	64	42	𝐹𝑐𝑜𝑛𝑣	𝐹𝑐𝑜𝑛𝑣	PROPN
fcis-6024	64	43	denotes	denote	VERB
fcis-6024	64	44	the	the	DET
fcis-6024	64	45	path	path	NOUN
fcis-6024	64	46	output	output	NOUN
fcis-6024	64	47	of	of	ADP
fcis-6024	64	48	the	the	DET
fcis-6024	64	49	branch	branch	NOUN
fcis-6024	64	50	of	of	ADP
fcis-6024	64	51	the	the	DET
fcis-6024	64	52	second	second	ADJ
fcis-6024	64	53	-	-	PUNCT
fcis-6024	64	54	stage	stage	NOUN
fcis-6024	64	55	convolution	convolution	NOUN
fcis-6024	64	56	module	module	NOUN
fcis-6024	64	57	,	,	PUNCT
fcis-6024	64	58	and	and	CCONJ
fcis-6024	64	59	the	the	DET
fcis-6024	64	60	intensity	intensity	NOUN
fcis-6024	64	61	controllable	controllable	ADJ
fcis-6024	64	62	scalar𝛼	scalar𝛼	NOUN
fcis-6024	64	63	,	,	PUNCT
fcis-6024	64	64	𝛽	𝛽	NOUN
fcis-6024	64	65	range	range	NOUN
fcis-6024	64	66	from	from	ADP
fcis-6024	64	67	0	0	NUM
fcis-6024	64	68	to	to	ADP
fcis-6024	64	69	1	1	NUM
fcis-6024	64	70	.	.	PUNCT
fcis-6024	65	1	in	in	ADP
fcis-6024	65	2	this	this	DET
fcis-6024	65	3	paper	paper	NOUN
fcis-6024	65	4	,	,	PUNCT
fcis-6024	65	5	the	the	DET
fcis-6024	65	6	learnable	learnable	ADJ
fcis-6024	65	7	scalar	scalar	ADJ
fcis-6024	65	8	𝛼	𝛼	NOUN
fcis-6024	65	9	,	,	PUNCT
fcis-6024	65	10	𝛽	𝛽	NOUN
fcis-6024	65	11	values	value	NOUN
fcis-6024	65	12	are	be	AUX
fcis-6024	65	13	1	1	NUM
fcis-6024	65	14	.	.	ADP
fcis-6024	65	15	4	4	NUM
fcis-6024	65	16	.	.	X
fcis-6024	65	17	experimental	experimental	ADJ
fcis-6024	65	18	results	result	NOUN
fcis-6024	65	19	and	and	CCONJ
fcis-6024	65	20	analysis	analysis	NOUN
fcis-6024	65	21	4.1	4.1	NUM
fcis-6024	65	22	.	.	PUNCT
fcis-6024	66	1	data	datum	NOUN
fcis-6024	66	2	set	set	VERB
fcis-6024	66	3	the	the	DET
fcis-6024	66	4	dataset	dataset	NOUN
fcis-6024	66	5	used	use	VERB
fcis-6024	66	6	for	for	ADP
fcis-6024	66	7	the	the	DET
fcis-6024	66	8	experiment	experiment	NOUN
fcis-6024	66	9	is	be	AUX
fcis-6024	66	10	bdd10k	bdd10k	NOUN
fcis-6024	66	11	,	,	PUNCT
fcis-6024	66	12	a	a	DET
fcis-6024	66	13	subset	subset	NOUN
fcis-6024	66	14	of	of	ADP
fcis-6024	66	15	bdd100k	bdd100k	NOUN
fcis-6024	66	16	released	release	VERB
fcis-6024	66	17	by	by	ADP
fcis-6024	66	18	the	the	DET
fcis-6024	66	19	university	university	PROPN
fcis-6024	66	20	of	of	ADP
fcis-6024	66	21	california	california	PROPN
fcis-6024	66	22	,	,	PUNCT
fcis-6024	66	23	berkeley	berkeley	PROPN
fcis-6024	66	24	,	,	PUNCT
fcis-6024	66	25	which	which	PRON
fcis-6024	66	26	is	be	AUX
fcis-6024	66	27	the	the	DET
fcis-6024	66	28	largest	large	ADJ
fcis-6024	66	29	and	and	CCONJ
fcis-6024	66	30	most	most	ADV
fcis-6024	66	31	diverse	diverse	ADJ
fcis-6024	66	32	autonomous	autonomous	ADJ
fcis-6024	66	33	driving	driving	NOUN
fcis-6024	66	34	dataset	dataset	NOUN
fcis-6024	66	35	to	to	ADP
fcis-6024	66	36	date	date	NOUN
fcis-6024	66	37	.	.	PUNCT
fcis-6024	67	1	this	this	DET
fcis-6024	67	2	experimental	experimental	ADJ
fcis-6024	67	3	dataset	dataset	NOUN
fcis-6024	67	4	contains	contain	VERB
fcis-6024	67	5	a	a	DET
fcis-6024	67	6	total	total	NOUN
fcis-6024	67	7	of	of	ADP
fcis-6024	67	8	10,000	10,000	NUM
fcis-6024	67	9	images	image	NOUN
fcis-6024	67	10	,	,	PUNCT
fcis-6024	67	11	with	with	ADP
fcis-6024	67	12	a	a	DET
fcis-6024	67	13	total	total	NOUN
fcis-6024	67	14	of	of	ADP
fcis-6024	67	15	10	10	NUM
fcis-6024	67	16	categories	category	NOUN
fcis-6024	67	17	of	of	ADP
fcis-6024	67	18	vehicles	vehicle	NOUN
fcis-6024	67	19	,	,	PUNCT
fcis-6024	67	20	buses	bus	NOUN
fcis-6024	67	21	,	,	PUNCT
fcis-6024	67	22	pedestrians	pedestrian	NOUN
fcis-6024	67	23	,	,	PUNCT
fcis-6024	67	24	bicycles	bicycle	NOUN
fcis-6024	67	25	,	,	PUNCT
fcis-6024	67	26	trucks	truck	NOUN
fcis-6024	67	27	,	,	PUNCT
fcis-6024	67	28	motorcycles	motorcycle	NOUN
fcis-6024	67	29	,	,	PUNCT
fcis-6024	67	30	etc	etc	X
fcis-6024	67	31	.	.	X
fcis-6024	68	1	it	it	PRON
fcis-6024	68	2	is	be	AUX
fcis-6024	68	3	divided	divide	VERB
fcis-6024	68	4	into	into	ADP
fcis-6024	68	5	a	a	DET
fcis-6024	68	6	training	training	NOUN
fcis-6024	68	7	set	set	NOUN
fcis-6024	68	8	,	,	PUNCT
fcis-6024	68	9	a	a	DET
fcis-6024	68	10	test	test	NOUN
fcis-6024	68	11	set	set	NOUN
fcis-6024	68	12	,	,	PUNCT
fcis-6024	68	13	and	and	CCONJ
fcis-6024	68	14	a	a	DET
fcis-6024	68	15	validation	validation	NOUN
fcis-6024	68	16	set	set	NOUN
fcis-6024	68	17	according	accord	VERB
fcis-6024	68	18	to	to	ADP
fcis-6024	68	19	7:2:1	7:2:1	NUM
fcis-6024	68	20	.	.	PUNCT
fcis-6024	69	1	an	an	DET
fcis-6024	69	2	example	example	NOUN
fcis-6024	69	3	of	of	ADP
fcis-6024	69	4	the	the	DET
fcis-6024	69	5	dataset	dataset	NOUN
fcis-6024	69	6	is	be	AUX
fcis-6024	69	7	shown	show	VERB
fcis-6024	69	8	in	in	ADP
fcis-6024	69	9	fig	fig	NOUN
fcis-6024	69	10	3	3	NUM
fcis-6024	69	11	.	.	PUNCT
fcis-6024	69	12	figure	figure	NOUN
fcis-6024	69	13	3	3	NUM
fcis-6024	69	14	.	.	NOUN
fcis-6024	69	15	example	example	NOUN
fcis-6024	69	16	data	datum	NOUN
fcis-6024	69	17	set	set	VERB
fcis-6024	69	18	diagram	diagram	PROPN
fcis-6024	69	19	4.2	4.2	NUM
fcis-6024	69	20	.	.	PUNCT
fcis-6024	70	1	training	training	NOUN
fcis-6024	70	2	environment	environment	NOUN
fcis-6024	70	3	the	the	DET
fcis-6024	70	4	experimental	experimental	ADJ
fcis-6024	70	5	platform	platform	NOUN
fcis-6024	70	6	on	on	ADP
fcis-6024	70	7	which	which	PRON
fcis-6024	70	8	this	this	DET
fcis-6024	70	9	experiment	experiment	NOUN
fcis-6024	70	10	is	be	AUX
fcis-6024	70	11	based	base	VERB
fcis-6024	70	12	is	be	AUX
fcis-6024	70	13	shown	show	VERB
fcis-6024	70	14	in	in	ADP
fcis-6024	70	15	table	table	NOUN
fcis-6024	70	16	1	1	NUM
fcis-6024	70	17	table	table	NOUN
fcis-6024	70	18	.1	.1	NUM
fcis-6024	70	19	experimental	experimental	ADJ
fcis-6024	70	20	platform	platform	NOUN
fcis-6024	70	21	environment	environment	NOUN
fcis-6024	70	22	parameters	parameter	NOUN
fcis-6024	70	23	configuration	configuration	NOUN
fcis-6024	70	24	cpu	cpu	VERB
fcis-6024	70	25	gpu	gpu	PROPN
fcis-6024	70	26	r7	r7	PROPN
fcis-6024	70	27	-	-	PUNCT
fcis-6024	70	28	5800h	5800h	NUM
fcis-6024	70	29	3060	3060	NUM
fcis-6024	70	30	framework	framework	NOUN
fcis-6024	70	31	cuda	cuda	NOUN
fcis-6024	70	32	experimentalplatform	experimentalplatform	PROPN
fcis-6024	70	33	language	language	PROPN
fcis-6024	70	34	batch	batch	NOUN
fcis-6024	70	35	-	-	PUNCT
fcis-6024	70	36	size	size	NOUN
fcis-6024	70	37	workers	worker	NOUN
fcis-6024	70	38	pytorch	pytorch	VERB
fcis-6024	70	39	1.8.1	1.8.1	NUM
fcis-6024	70	40	11.1	11.1	NUM
fcis-6024	70	41	vscode	vscode	NOUN
fcis-6024	70	42	python	python	NOUN
fcis-6024	70	43	3.8.1	3.8.1	NUM
fcis-6024	70	44	4	4	NUM
fcis-6024	70	45	8	8	NUM
fcis-6024	70	46	the	the	DET
fcis-6024	70	47	experimental	experimental	ADJ
fcis-6024	70	48	algorithm	algorithm	NOUN
fcis-6024	70	49	is	be	AUX
fcis-6024	70	50	based	base	VERB
fcis-6024	70	51	on	on	ADP
fcis-6024	70	52	cuda	cuda	NOUN
fcis-6024	70	53	11.1	11.1	NUM
fcis-6024	70	54	and	and	CCONJ
fcis-6024	70	55	pytorch	pytorch	VERB
fcis-6024	70	56	version	version	NOUN
fcis-6024	70	57	1.8.1	1.8.1	NUM
fcis-6024	70	58	.	.	PUNCT
fcis-6024	71	1	the	the	DET
fcis-6024	71	2	hyperparameters	hyperparameter	NOUN
fcis-6024	71	3	are	be	AUX
fcis-6024	71	4	batch	batch	NOUN
fcis-6024	71	5	-	-	PUNCT
fcis-6024	71	6	size=4	size=4	NOUN
fcis-6024	71	7	,	,	PUNCT
fcis-6024	71	8	initial	initial	ADJ
fcis-6024	71	9	learning	learning	NOUN
fcis-6024	71	10	rate10−3	rate10−3	NUM
fcis-6024	71	11	,	,	PUNCT
fcis-6024	71	12	epoch=100	epoch=100	PROPN
fcis-6024	71	13	,	,	PUNCT
fcis-6024	71	14	and	and	CCONJ
fcis-6024	71	15	the	the	DET
fcis-6024	71	16	input	input	NOUN
fcis-6024	71	17	image	image	NOUN
fcis-6024	71	18	size	size	NOUN
fcis-6024	71	19	is	be	AUX
fcis-6024	71	20	640×640	640×640	NUM
fcis-6024	71	21	.	.	PUNCT
fcis-6024	72	1	4.3	4.3	NUM
fcis-6024	72	2	.	.	PUNCT
fcis-6024	73	1	experimental	experimental	ADJ
fcis-6024	73	2	results	result	NOUN
fcis-6024	73	3	and	and	CCONJ
fcis-6024	73	4	analysis	analysis	NOUN
fcis-6024	73	5	in	in	ADP
fcis-6024	73	6	this	this	DET
fcis-6024	73	7	paper	paper	NOUN
fcis-6024	73	8	,	,	PUNCT
fcis-6024	73	9	we	we	PRON
fcis-6024	73	10	compare	compare	VERB
fcis-6024	73	11	the	the	DET
fcis-6024	73	12	detection	detection	NOUN
fcis-6024	73	13	results	result	NOUN
fcis-6024	73	14	of	of	ADP
fcis-6024	73	15	the	the	DET
fcis-6024	73	16	original	original	ADJ
fcis-6024	73	17	yolov5	yolov5	NOUN
fcis-6024	73	18	baseline	baseline	PROPN
fcis-6024	73	19	model	model	NOUN
fcis-6024	73	20	and	and	CCONJ
fcis-6024	73	21	the	the	DET
fcis-6024	73	22	improved	improved	ADJ
fcis-6024	73	23	yolov5	yolov5	NOUN
fcis-6024	73	24	network	network	NOUN
fcis-6024	73	25	model	model	NOUN
fcis-6024	73	26	on	on	ADP
fcis-6024	73	27	the	the	DET
fcis-6024	73	28	bdd10k	bdd10k	NOUN
fcis-6024	73	29	and	and	CCONJ
fcis-6024	73	30	pascal	pascal	ADJ
fcis-6024	73	31	voc2007	voc2007	VERB
fcis-6024	73	32	59	59	NUM
fcis-6024	73	33	datasets	dataset	NOUN
fcis-6024	73	34	as	as	SCONJ
fcis-6024	73	35	shown	show	VERB
fcis-6024	73	36	in	in	ADP
fcis-6024	73	37	table	table	NOUN
fcis-6024	73	38	2	2	NUM
fcis-6024	73	39	table	table	NOUN
fcis-6024	73	40	.2	.2	NUM
fcis-6024	73	41	experimental	experimental	ADJ
fcis-6024	73	42	results	result	NOUN
fcis-6024	73	43	model	model	NOUN
fcis-6024	73	44	test	test	NOUN
fcis-6024	73	45	set	set	VERB
fcis-6024	73	46	map@.5(%	map@.5(%	NOUN
fcis-6024	73	47	)	)	PUNCT
fcis-6024	73	48	map@.5:.0.95(%	map@.5:.0.95(%	NOUN
fcis-6024	73	49	)	)	PUNCT
fcis-6024	74	1	yolov5	yolov5	NOUN
fcis-6024	74	2	yolov5acmix	yolov5acmix	NOUN
fcis-6024	74	3	yolov5	yolov5	NOUN
fcis-6024	74	4	bdd10k	bdd10k	NOUN
fcis-6024	74	5	voc2007	voc2007	VERB
fcis-6024	74	6	50.7	50.7	NUM
fcis-6024	74	7	51.8	51.8	NUM
fcis-6024	74	8	84.7	84.7	NUM
fcis-6024	74	9	26.8	26.8	NUM
fcis-6024	74	10	27.3	27.3	NUM
fcis-6024	74	11	65.1	65.1	NUM
fcis-6024	74	12	yolov5acmix	yolov5acmix	NOUN
fcis-6024	74	13	85.9	85.9	NUM
fcis-6024	74	14	65.3	65.3	NUM
fcis-6024	74	15	important	important	ADJ
fcis-6024	74	16	evaluation	evaluation	NOUN
fcis-6024	74	17	indicators	indicator	NOUN
fcis-6024	74	18	in	in	ADP
fcis-6024	74	19	target	target	NOUN
fcis-6024	74	20	detection	detection	NOUN
fcis-6024	74	21	are	be	AUX
fcis-6024	74	22	average	average	ADJ
fcis-6024	74	23	precision	precision	NOUN
fcis-6024	74	24	(	(	PUNCT
fcis-6024	74	25	ap	ap	PROPN
fcis-6024	74	26	)	)	PUNCT
fcis-6024	74	27	and	and	CCONJ
fcis-6024	74	28	mean	mean	VERB
fcis-6024	74	29	average	average	ADJ
fcis-6024	74	30	precision	precision	NOUN
fcis-6024	74	31	(	(	PUNCT
fcis-6024	74	32	map	map	NOUN
fcis-6024	74	33	)	)	PUNCT
fcis-6024	74	34	,	,	PUNCT
fcis-6024	74	35	which	which	PRON
fcis-6024	74	36	are	be	AUX
fcis-6024	74	37	calculated	calculate	VERB
fcis-6024	74	38	as	as	SCONJ
fcis-6024	74	39	follows	follow	VERB
fcis-6024	74	40	.	.	PUNCT
fcis-6024	75	1	𝐴𝑃	𝐴𝑃	PROPN
fcis-6024	75	2	=	=	SYM
fcis-6024	75	3	∫	∫	NOUN
fcis-6024	75	4	𝑃(𝑅)𝑑𝑅	𝑃(𝑅)𝑑𝑅	NOUN
fcis-6024	75	5	1	1	NUM
fcis-6024	75	6	0	0	NUM
fcis-6024	75	7	(	(	PUNCT
fcis-6024	75	8	5	5	NUM
fcis-6024	75	9	)	)	PUNCT
fcis-6024	75	10	𝑚𝐴𝑃	𝑚𝐴𝑃	NOUN
fcis-6024	75	11	=	=	SYM
fcis-6024	75	12	(	(	PUNCT
fcis-6024	75	13	∑	∑	INTJ
fcis-6024	75	14	𝐴𝑃𝑐	𝐴𝑃𝑐	NOUN
fcis-6024	75	15	𝑛	𝑛	PROPN
fcis-6024	75	16	𝑐=1	𝑐=1	PROPN
fcis-6024	75	17	)	)	PUNCT
fcis-6024	75	18	/𝑛	/𝑛	PUNCT
fcis-6024	76	1	(	(	PUNCT
fcis-6024	76	2	6	6	NUM
fcis-6024	76	3	)	)	PUNCT
fcis-6024	76	4	where	where	SCONJ
fcis-6024	76	5	p	p	NOUN
fcis-6024	76	6	is	be	AUX
fcis-6024	76	7	the	the	DET
fcis-6024	76	8	precision	precision	NOUN
fcis-6024	76	9	rate	rate	NOUN
fcis-6024	76	10	,	,	PUNCT
fcis-6024	76	11	which	which	PRON
fcis-6024	76	12	indicates	indicate	VERB
fcis-6024	76	13	the	the	DET
fcis-6024	76	14	proportion	proportion	NOUN
fcis-6024	76	15	of	of	ADP
fcis-6024	76	16	true	true	ADJ
fcis-6024	76	17	positive	positive	ADJ
fcis-6024	76	18	samples	sample	NOUN
fcis-6024	76	19	to	to	PART
fcis-6024	76	20	predicted	predict	VERB
fcis-6024	76	21	positive	positive	ADJ
fcis-6024	76	22	samples	sample	NOUN
fcis-6024	76	23	;	;	PUNCT
fcis-6024	76	24	r	r	NOUN
fcis-6024	76	25	is	be	AUX
fcis-6024	76	26	the	the	DET
fcis-6024	76	27	recall	recall	NOUN
fcis-6024	76	28	rate	rate	NOUN
fcis-6024	76	29	,	,	PUNCT
fcis-6024	76	30	which	which	PRON
fcis-6024	76	31	indicates	indicate	VERB
fcis-6024	76	32	the	the	DET
fcis-6024	76	33	proportion	proportion	NOUN
fcis-6024	76	34	of	of	ADP
fcis-6024	76	35	true	true	ADJ
fcis-6024	76	36	positive	positive	ADJ
fcis-6024	76	37	samples	sample	NOUN
fcis-6024	76	38	to	to	ADP
fcis-6024	76	39	actual	actual	ADJ
fcis-6024	76	40	positive	positive	ADJ
fcis-6024	76	41	samples	sample	NOUN
fcis-6024	76	42	;	;	PUNCT
fcis-6024	76	43	c	c	NOUN
fcis-6024	76	44	is	be	AUX
fcis-6024	76	45	the	the	DET
fcis-6024	76	46	predicted	predict	VERB
fcis-6024	76	47	category	category	NOUN
fcis-6024	76	48	;	;	PUNCT
fcis-6024	76	49	and	and	CCONJ
fcis-6024	76	50	n	n	PRON
fcis-6024	76	51	is	be	AUX
fcis-6024	76	52	the	the	DET
fcis-6024	76	53	number	number	NOUN
fcis-6024	76	54	of	of	ADP
fcis-6024	76	55	categories	category	NOUN
fcis-6024	76	56	.	.	PUNCT
fcis-6024	77	1	map@.5	map@.5	PROPN
fcis-6024	77	2	denotes	denote	VERB
fcis-6024	77	3	the	the	DET
fcis-6024	77	4	average	average	ADJ
fcis-6024	77	5	precision	precision	NOUN
fcis-6024	77	6	of	of	ADP
fcis-6024	77	7	a	a	DET
fcis-6024	77	8	class	class	NOUN
fcis-6024	77	9	when	when	SCONJ
fcis-6024	77	10	the	the	DET
fcis-6024	77	11	iou	iou	ADJ
fcis-6024	77	12	confidence	confidence	NOUN
fcis-6024	77	13	of	of	ADP
fcis-6024	77	14	the	the	DET
fcis-6024	77	15	confusion	confusion	NOUN
fcis-6024	77	16	matrix	matrix	NOUN
fcis-6024	77	17	takes	take	VERB
fcis-6024	77	18	the	the	DET
fcis-6024	77	19	value	value	NOUN
fcis-6024	77	20	of	of	ADP
fcis-6024	77	21	0.5	0.5	NUM
fcis-6024	77	22	.	.	PUNCT
fcis-6024	78	1	map@.5:.0.95	map@.5:.0.95	NOUN
fcis-6024	78	2	indicates	indicate	VERB
fcis-6024	78	3	map	map	NOUN
fcis-6024	78	4	map	map	VERB
fcis-6024	78	5	the	the	DET
fcis-6024	78	6	thresholds	threshold	NOUN
fcis-6024	78	7	range	range	VERB
fcis-6024	78	8	from	from	ADP
fcis-6024	78	9	0.5	0.5	NUM
fcis-6024	78	10	to	to	ADP
fcis-6024	78	11	0.95	0.95	NUM
fcis-6024	78	12	,	,	PUNCT
fcis-6024	78	13	and	and	CCONJ
fcis-6024	78	14	the	the	DET
fcis-6024	78	15	mean	mean	ADJ
fcis-6024	78	16	value	value	NOUN
fcis-6024	78	17	of	of	ADP
fcis-6024	78	18	map	map	NOUN
fcis-6024	78	19	at	at	ADP
fcis-6024	78	20	a	a	DET
fcis-6024	78	21	step	step	NOUN
fcis-6024	78	22	size	size	NOUN
fcis-6024	78	23	of	of	ADP
fcis-6024	78	24	0.05	0.05	NUM
fcis-6024	78	25	.	.	PUNCT
fcis-6024	79	1	the	the	DET
fcis-6024	79	2	improved	improve	VERB
fcis-6024	79	3	algorithm	algorithm	NOUN
fcis-6024	79	4	and	and	CCONJ
fcis-6024	79	5	yolov5	yolov5	NOUN
fcis-6024	79	6	various	various	ADJ
fcis-6024	79	7	algorithm	algorithm	NOUN
fcis-6024	79	8	metrics	metric	NOUN
fcis-6024	79	9	can	can	AUX
fcis-6024	79	10	be	be	AUX
fcis-6024	79	11	seen	see	VERB
fcis-6024	79	12	from	from	ADP
fcis-6024	79	13	table	table	NOUN
fcis-6024	79	14	4	4	NUM
fcis-6024	79	15	-	-	SYM
fcis-6024	79	16	2	2	NUM
fcis-6024	79	17	that	that	PRON
fcis-6024	79	18	the	the	DET
fcis-6024	79	19	improved	improved	ADJ
fcis-6024	79	20	algorithm	algorithm	NOUN
fcis-6024	79	21	increased	increase	VERB
fcis-6024	79	22	map@.5	map@.5	NOUN
fcis-6024	79	23	by	by	ADP
fcis-6024	79	24	1.1	1.1	NUM
fcis-6024	79	25	%	%	NOUN
fcis-6024	79	26	and	and	CCONJ
fcis-6024	79	27	map@.5:.0.95	map@.5:.0.95	NOUN
fcis-6024	79	28	by	by	ADP
fcis-6024	79	29	0.5	0.5	NUM
fcis-6024	79	30	%	%	NOUN
fcis-6024	79	31	on	on	ADP
fcis-6024	79	32	bdd10k	bdd10k	NOUN
fcis-6024	79	33	data	datum	NOUN
fcis-6024	79	34	,	,	PUNCT
fcis-6024	79	35	and	and	CCONJ
fcis-6024	79	36	its	its	PRON
fcis-6024	79	37	map@.0.5	map@.0.5	NOUN
fcis-6024	79	38	increased	increase	VERB
fcis-6024	79	39	by	by	ADP
fcis-6024	79	40	1.2	1.2	NUM
fcis-6024	79	41	%	%	NOUN
fcis-6024	79	42	on	on	ADP
fcis-6024	79	43	pascal	pascal	ADJ
fcis-6024	79	44	voc2007	voc2007	NOUN
fcis-6024	79	45	dataset	dataset	NOUN
fcis-6024	79	46	,	,	PUNCT
fcis-6024	79	47	the	the	DET
fcis-6024	79	48	effect	effect	NOUN
fcis-6024	79	49	comparison	comparison	NOUN
fcis-6024	79	50	graph	graph	NOUN
fcis-6024	79	51	is	be	AUX
fcis-6024	79	52	shown	show	VERB
fcis-6024	79	53	in	in	ADP
fcis-6024	79	54	fig	fig	NOUN
fcis-6024	79	55	figure	figure	NOUN
fcis-6024	79	56	4	4	NUM
fcis-6024	79	57	.	.	PUNCT
fcis-6024	80	1	(	(	PUNCT
fcis-6024	80	2	a	a	X
fcis-6024	80	3	)	)	PUNCT
fcis-6024	80	4	improved	improved	ADJ
fcis-6024	80	5	mode	mode	NOUN
fcis-6024	80	6	(	(	PUNCT
fcis-6024	80	7	b	b	NOUN
fcis-6024	80	8	)	)	PUNCT
fcis-6024	80	9	yolov5	yolov5	NOUN
fcis-6024	80	10	model	model	NOUN
fcis-6024	80	11	5	5	NUM
fcis-6024	80	12	.	.	PUNCT
fcis-6024	80	13	conclusion	conclusion	NOUN
fcis-6024	80	14	in	in	ADP
fcis-6024	80	15	this	this	DET
fcis-6024	80	16	paper	paper	NOUN
fcis-6024	80	17	,	,	PUNCT
fcis-6024	80	18	we	we	PRON
fcis-6024	80	19	introduce	introduce	VERB
fcis-6024	80	20	the	the	DET
fcis-6024	80	21	network	network	NOUN
fcis-6024	80	22	structure	structure	NOUN
fcis-6024	80	23	of	of	ADP
fcis-6024	80	24	yolov5s	yolov5s	PROPN
fcis-6024	80	25	and	and	CCONJ
fcis-6024	80	26	the	the	DET
fcis-6024	80	27	acmix	acmix	NOUN
fcis-6024	80	28	hybrid	hybrid	ADJ
fcis-6024	80	29	attention	attention	NOUN
fcis-6024	80	30	mechanism	mechanism	NOUN
fcis-6024	80	31	,	,	PUNCT
fcis-6024	80	32	based	base	VERB
fcis-6024	80	33	on	on	ADP
fcis-6024	80	34	the	the	DET
fcis-6024	80	35	yolov5	yolov5	NOUN
fcis-6024	80	36	backbone	backbone	NOUN
fcis-6024	80	37	network	network	NOUN
fcis-6024	80	38	,	,	PUNCT
fcis-6024	80	39	add	add	VERB
fcis-6024	80	40	the	the	DET
fcis-6024	80	41	hybrid	hybrid	ADJ
fcis-6024	80	42	attention	attention	NOUN
fcis-6024	80	43	mechanism	mechanism	NOUN
fcis-6024	80	44	acmix	acmix	NOUN
fcis-6024	80	45	into	into	ADP
fcis-6024	80	46	the	the	DET
fcis-6024	80	47	network	network	NOUN
fcis-6024	80	48	,	,	PUNCT
fcis-6024	80	49	and	and	CCONJ
fcis-6024	80	50	simulate	simulate	VERB
fcis-6024	80	51	the	the	DET
fcis-6024	80	52	model	model	NOUN
fcis-6024	80	53	in	in	ADP
fcis-6024	80	54	the	the	DET
fcis-6024	80	55	bdd10k	bdd10k	NOUN
fcis-6024	80	56	and	and	CCONJ
fcis-6024	80	57	voc2007	voc2007	ADJ
fcis-6024	80	58	datasets	dataset	NOUN
fcis-6024	80	59	.	.	PUNCT
fcis-6024	81	1	the	the	DET
fcis-6024	81	2	experiments	experiment	NOUN
fcis-6024	81	3	show	show	VERB
fcis-6024	81	4	that	that	SCONJ
fcis-6024	81	5	the	the	DET
fcis-6024	81	6	improved	improved	ADJ
fcis-6024	81	7	yolov5	yolov5	NOUN
fcis-6024	81	8	model	model	NOUN
fcis-6024	81	9	has	have	AUX
fcis-6024	81	10	improved	improve	VERB
fcis-6024	81	11	the	the	DET
fcis-6024	81	12	accuracy	accuracy	NOUN
fcis-6024	81	13	of	of	ADP
fcis-6024	81	14	the	the	DET
fcis-6024	81	15	original	original	ADJ
fcis-6024	81	16	model	model	NOUN
fcis-6024	81	17	and	and	CCONJ
fcis-6024	81	18	the	the	DET
fcis-6024	81	19	detection	detection	NOUN
fcis-6024	81	20	capability	capability	NOUN
fcis-6024	81	21	of	of	ADP
fcis-6024	81	22	small	small	ADJ
fcis-6024	81	23	targets	target	NOUN
fcis-6024	81	24	is	be	AUX
fcis-6024	81	25	greatly	greatly	ADV
fcis-6024	81	26	improved	improve	VERB
fcis-6024	81	27	.	.	PUNCT
fcis-6024	82	1	acknowledgments	acknowledgment	NOUN
fcis-6024	82	2	funding	funding	PROPN
fcis-6024	82	3	:	:	PUNCT
fcis-6024	82	4	this	this	DET
fcis-6024	82	5	research	research	NOUN
fcis-6024	82	6	was	be	AUX
fcis-6024	82	7	funded	fund	VERB
fcis-6024	82	8	by	by	ADP
fcis-6024	82	9	the	the	DET
fcis-6024	82	10	southwest	southwest	PROPN
fcis-6024	82	11	minzu	minzu	PROPN
fcis-6024	82	12	university	university	PROPN
fcis-6024	82	13	graduate	graduate	NOUN
fcis-6024	82	14	innovative	innovative	ADJ
fcis-6024	82	15	re	re	ADJ
fcis-6024	82	16	-	-	ADJ
fcis-6024	82	17	search	search	ADJ
fcis-6024	82	18	project	project	NOUN
fcis-6024	82	19	grant	grant	NOUN
fcis-6024	82	20	no.(yb2022812	no.(yb2022812	NOUN
fcis-6024	82	21	)	)	PUNCT
fcis-6024	82	22	references	reference	NOUN
fcis-6024	82	23	[	[	X
fcis-6024	82	24	1	1	NUM
fcis-6024	82	25	]	]	PUNCT
fcis-6024	82	26	osuna	osuna	PROPN
fcis-6024	82	27	e	e	PROPN
fcis-6024	82	28	,	,	PUNCT
fcis-6024	82	29	freund	freund	PROPN
fcis-6024	82	30	r	r	NOUN
fcis-6024	82	31	,	,	PUNCT
fcis-6024	82	32	girosit	girosit	PROPN
fcis-6024	82	33	f.	f.	PROPN
fcis-6024	82	34	training	training	NOUN
fcis-6024	82	35	support	support	NOUN
fcis-6024	82	36	vector	vector	NOUN
fcis-6024	82	37	machines	machine	NOUN
fcis-6024	82	38	:	:	PUNCT
fcis-6024	82	39	an	an	DET
fcis-6024	82	40	application	application	NOUN
fcis-6024	82	41	to	to	PART
fcis-6024	82	42	face	face	VERB
fcis-6024	82	43	detection[c	detection[c	VERB
fcis-6024	82	44	]	]	PUNCT
fcis-6024	82	45	.	.	PUNCT
fcis-6024	83	1	ieee	ieee	PROPN
fcis-6024	83	2	computer	computer	PROPN
fcis-6024	83	3	society	society	PROPN
fcis-6024	83	4	conference	conference	NOUN
fcis-6024	83	5	on	on	ADP
fcis-6024	83	6	computer	computer	NOUN
fcis-6024	83	7	vision	vision	NOUN
fcis-6024	83	8	&	&	CCONJ
fcis-6024	83	9	pattern	pattern	NOUN
fcis-6024	83	10	recognition	recognition	NOUN
fcis-6024	83	11	,	,	PUNCT
fcis-6024	83	12	san	san	PROPN
fcis-6024	83	13	juan	juan	PROPN
fcis-6024	83	14	,	,	PUNCT
fcis-6024	83	15	pr	pr	PROPN
fcis-6024	83	16	,	,	PUNCT
fcis-6024	83	17	usa	usa	PROPN
fcis-6024	83	18	,	,	PUNCT
fcis-6024	83	19	1997	1997	NUM
fcis-6024	83	20	:	:	PUNCT
fcis-6024	83	21	130	130	NUM
fcis-6024	83	22	-	-	SYM
fcis-6024	83	23	136	136	NUM
fcis-6024	83	24	.	.	PUNCT
fcis-6024	84	1	[	[	X
fcis-6024	84	2	2	2	X
fcis-6024	84	3	]	]	X
fcis-6024	84	4	viola	viola	NOUN
fcis-6024	84	5	p	p	X
fcis-6024	84	6	,	,	PUNCT
fcis-6024	84	7	jones	jones	PROPN
fcis-6024	84	8	m.	m.	PROPN
fcis-6024	84	9	rapid	rapid	ADJ
fcis-6024	84	10	object	object	NOUN
fcis-6024	84	11	detection	detection	NOUN
fcis-6024	84	12	using	use	VERB
fcis-6024	84	13	a	a	DET
fcis-6024	84	14	boosted	boosted	ADJ
fcis-6024	84	15	cascade	cascade	NOUN
fcis-6024	84	16	of	of	ADP
fcis-6024	84	17	simple	simple	ADJ
fcis-6024	84	18	features[c	features[c	PROPN
fcis-6024	84	19	]	]	PUNCT
fcis-6024	84	20	.	.	PUNCT
fcis-6024	85	1	ieee	ieee	PROPN
fcis-6024	85	2	computer	computer	PROPN
fcis-6024	85	3	society	society	PROPN
fcis-6024	85	4	conference	conference	NOUN
fcis-6024	85	5	on	on	ADP
fcis-6024	85	6	computer	computer	NOUN
fcis-6024	85	7	vision	vision	NOUN
fcis-6024	85	8	and	and	CCONJ
fcis-6024	85	9	pattern	pattern	NOUN
fcis-6024	85	10	recognition	recognition	NOUN
fcis-6024	85	11	,	,	PUNCT
fcis-6024	85	12	kauai	kauai	PROPN
fcis-6024	85	13	,	,	PUNCT
fcis-6024	85	14	hi	hi	PROPN
fcis-6024	85	15	,	,	PUNCT
fcis-6024	85	16	usa	usa	PROPN
fcis-6024	85	17	,	,	PUNCT
fcis-6024	85	18	2001	2001	NUM
fcis-6024	85	19	:	:	PUNCT
fcis-6024	85	20	511	511	NUM
fcis-6024	85	21	-	-	SYM
fcis-6024	85	22	518	518	NUM
fcis-6024	85	23	.	.	PUNCT
fcis-6024	86	1	[	[	X
fcis-6024	86	2	3	3	X
fcis-6024	86	3	]	]	X
fcis-6024	86	4	lowe	lowe	PROPN
fcis-6024	86	5	d.	d.	PROPN
fcis-6024	86	6	distinctive	distinctive	ADJ
fcis-6024	86	7	image	image	NOUN
fcis-6024	86	8	features	feature	NOUN
fcis-6024	86	9	from	from	ADP
fcis-6024	86	10	scale	scale	NOUN
fcis-6024	86	11	-	-	PUNCT
fcis-6024	86	12	invariant	invariant	ADJ
fcis-6024	86	13	key	key	ADJ
fcis-6024	86	14	points[j	points[j	NOUN
fcis-6024	86	15	]	]	PUNCT
fcis-6024	86	16	.	.	PUNCT
fcis-6024	87	1	international	international	ADJ
fcis-6024	87	2	journal	journal	PROPN
fcis-6024	87	3	of	of	ADP
fcis-6024	87	4	computer	computer	NOUN
fcis-6024	87	5	vision	vision	NOUN
fcis-6024	87	6	,	,	PUNCT
fcis-6024	87	7	2004	2004	NUM
fcis-6024	87	8	,	,	PUNCT
fcis-6024	87	9	60	60	NUM
fcis-6024	87	10	(	(	PUNCT
fcis-6024	87	11	2	2	NUM
fcis-6024	87	12	):	):	PUNCT
fcis-6024	87	13	91	91	NUM
fcis-6024	87	14	-	-	SYM
fcis-6024	87	15	110	110	NUM
fcis-6024	87	16	.	.	PUNCT
fcis-6024	88	1	[	[	X
fcis-6024	88	2	4	4	X
fcis-6024	88	3	]	]	X
fcis-6024	88	4	girshick	girshick	ADJ
fcis-6024	88	5	r	r	PROPN
fcis-6024	88	6	,	,	PUNCT
fcis-6024	88	7	donahue	donahue	PROPN
fcis-6024	88	8	j	j	PROPN
fcis-6024	88	9	,	,	PUNCT
fcis-6024	88	10	darrell	darrell	PROPN
fcis-6024	88	11	t	t	PROPN
fcis-6024	88	12	,	,	PUNCT
fcis-6024	88	13	et	et	PROPN
fcis-6024	88	14	a1	a1	PROPN
fcis-6024	88	15	.	.	PUNCT
fcis-6024	88	16	rich	rich	ADJ
fcis-6024	88	17	feature	feature	NOUN
fcis-6024	88	18	hierarchies	hierarchy	NOUN
fcis-6024	88	19	for	for	ADP
fcis-6024	88	20	accurate	accurate	ADJ
fcis-6024	88	21	object	object	NOUN
fcis-6024	88	22	detection	detection	NOUN
fcis-6024	88	23	andsemantic	andsemantic	ADJ
fcis-6024	88	24	segmentation[c	segmentation[c	PROPN
fcis-6024	88	25	]	]	PUNCT
fcis-6024	88	26	.	.	PUNCT
fcis-6024	89	1	ieee	ieee	PROPN
fcis-6024	89	2	conference	conference	PROPN
fcis-6024	89	3	on	on	ADP
fcis-6024	89	4	computer	computer	NOUN
fcis-6024	89	5	vision	vision	NOUN
fcis-6024	89	6	and	and	CCONJ
fcis-6024	89	7	pattem	pattem	NOUN
fcis-6024	89	8	recognition	recognition	NOUN
fcis-6024	89	9	,	,	PUNCT
fcis-6024	89	10	2014	2014	NUM
fcis-6024	89	11	:	:	PUNCT
fcis-6024	89	12	580	580	NUM
fcis-6024	89	13	-	-	SYM
fcis-6024	89	14	587	587	NUM
fcis-6024	89	15	[	[	X
fcis-6024	89	16	5	5	NUM
fcis-6024	89	17	]	]	PUNCT
fcis-6024	89	18	he	he	PRON
fcis-6024	89	19	k	k	PROPN
fcis-6024	89	20	,	,	PUNCT
fcis-6024	89	21	zhang	zhang	PROPN
fcis-6024	89	22	x	x	PROPN
fcis-6024	89	23	,	,	PUNCT
fcis-6024	89	24	ren	ren	PROPN
fcis-6024	89	25	s	s	PROPN
fcis-6024	89	26	,	,	PUNCT
fcis-6024	89	27	et	et	PROPN
fcis-6024	89	28	al	al	PROPN
fcis-6024	89	29	.	.	PUNCT
fcis-6024	90	1	spatial	spatial	ADJ
fcis-6024	90	2	pyramid	pyramid	NOUN
fcis-6024	90	3	pooling	pool	VERB
fcis-6024	90	4	in	in	ADP
fcis-6024	90	5	deep	deep	ADJ
fcis-6024	90	6	convolutional	convolutional	ADJ
fcis-6024	90	7	networks	network	NOUN
fcis-6024	90	8	for	for	ADP
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fcis-6024	90	10	recognition[j	recognition[j	NOUN
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fcis-6024	90	12	.	.	PUNCT
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fcis-6024	91	2	transactions	transaction	NOUN
fcis-6024	91	3	on	on	ADP
fcis-6024	91	4	pattern	pattern	NOUN
fcis-6024	91	5	analysis	analysis	NOUN
fcis-6024	91	6	and	and	CCONJ
fcis-6024	91	7	machine	machine	NOUN
fcis-6024	91	8	intelligence	intelligence	NOUN
fcis-6024	91	9	,	,	PUNCT
fcis-6024	91	10	2015	2015	NUM
fcis-6024	91	11	,	,	PUNCT
fcis-6024	91	12	37(9):1904	37(9):1904	NUM
fcis-6024	91	13	-	-	SYM
fcis-6024	91	14	1916	1916	NUM
fcis-6024	91	15	.	.	PUNCT
fcis-6024	92	1	[	[	X
fcis-6024	92	2	6	6	NUM
fcis-6024	92	3	]	]	X
fcis-6024	92	4	girshick	girshick	PROPN
fcis-6024	92	5	r.	r.	PROPN
fcis-6024	92	6	fast	fast	ADV
fcis-6024	92	7	r	r	PROPN
fcis-6024	92	8	-	-	PUNCT
fcis-6024	92	9	cnn	cnn	NOUN
fcis-6024	93	1	[	[	X
fcis-6024	93	2	c	c	X
fcis-6024	93	3	]	]	PUNCT
fcis-6024	93	4	.	.	PUNCT
fcis-6024	94	1	ieee	ieee	PROPN
fcis-6024	94	2	international	international	PROPN
fcis-6024	94	3	conference	conference	NOUN
fcis-6024	94	4	on	on	ADP
fcis-6024	94	5	computer	computer	NOUN
fcis-6024	94	6	vision	vision	NOUN
fcis-6024	94	7	,	,	PUNCT
fcis-6024	94	8	2015	2015	NUM
fcis-6024	94	9	:	:	PUNCT
fcis-6024	94	10	1440	1440	NUM
fcis-6024	94	11	-	-	SYM
fcis-6024	94	12	1448	1448	NUM
fcis-6024	94	13	[	[	X
fcis-6024	94	14	7	7	X
fcis-6024	94	15	]	]	X
fcis-6024	94	16	ren	ren	PROPN
fcis-6024	94	17	s	s	PROPN
fcis-6024	94	18	,	,	PUNCT
fcis-6024	94	19	he	he	PRON
fcis-6024	94	20	k	k	NOUN
fcis-6024	94	21	,	,	PUNCT
fcis-6024	94	22	girshick	girshick	ADJ
fcis-6024	94	23	r	r	NOUN
fcis-6024	94	24	,	,	PUNCT
fcis-6024	94	25	et	et	PROPN
fcis-6024	94	26	al	al	PROPN
fcis-6024	94	27	.	.	PUNCT
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fcis-6024	95	2	r	r	NOUN
fcis-6024	95	3	-	-	PUNCT
fcis-6024	95	4	cnn	cnn	NOUN
fcis-6024	95	5	:	:	PUNCT
fcis-6024	95	6	towards	towards	ADP
fcis-6024	95	7	real	real	ADJ
fcis-6024	95	8	time	time	NOUN
fcis-6024	95	9	object	object	NOUN
fcis-6024	95	10	detection	detection	NOUN
fcis-6024	95	11	with	with	ADP
fcis-6024	95	12	region	region	NOUN
fcis-6024	95	13	proposal	proposal	NOUN
fcis-6024	95	14	networks[j	networks[j	PROPN
fcis-6024	95	15	]	]	X
fcis-6024	95	16	.	.	PUNCT
fcis-6024	96	1	ieee	ieee	NOUN
fcis-6024	96	2	transactions	transaction	NOUN
fcis-6024	96	3	on	on	ADP
fcis-6024	96	4	pattern	pattern	NOUN
fcis-6024	96	5	analysis	analysis	NOUN
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fcis-6024	96	7	machine	machine	NOUN
fcis-6024	96	8	intelligence	intelligence	NOUN
fcis-6024	96	9	,	,	PUNCT
fcis-6024	96	10	2017	2017	NUM
fcis-6024	96	11	,	,	PUNCT
fcis-6024	96	12	39(6	39(6	NUM
fcis-6024	96	13	):	):	PUNCT
fcis-6024	96	14	1137	1137	NUM
fcis-6024	96	15	-	-	SYM
fcis-6024	96	16	1149	1149	NUM
fcis-6024	96	17	.	.	PUNCT
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fcis-6024	97	2	8	8	NUM
fcis-6024	97	3	]	]	X
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fcis-6024	97	5	j	j	PROPN
fcis-6024	97	6	,	,	PUNCT
fcis-6024	97	7	divvala	divvala	PROPN
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fcis-6024	97	9	,	,	PUNCT
fcis-6024	97	10	girshick	girshick	ADJ
fcis-6024	97	11	r	r	NOUN
fcis-6024	97	12	and	and	CCONJ
fcis-6024	97	13	farhadi	farhadi	NOUN
fcis-6024	97	14	a.	a.	PROPN
fcis-6024	97	15	2016	2016	NUM
fcis-6024	97	16	.	.	PUNCT
fcis-6024	98	1	you	you	PRON
fcis-6024	98	2	only	only	ADV
fcis-6024	98	3	look	look	VERB
fcis-6024	98	4	once	once	ADV
fcis-6024	98	5	:	:	PUNCT
fcis-6024	98	6	unified	unified	ADJ
fcis-6024	98	7	,	,	PUNCT
fcis-6024	98	8	real	real	ADJ
fcis-6024	98	9	-	-	PUNCT
fcis-6024	98	10	time	time	NOUN
fcis-6024	98	11	object	object	NOUN
fcis-6024	98	12	detection	detection	NOUN
fcis-6024	98	13	/	/	SYM
fcis-6024	98	14	/	/	SYM
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fcis-6024	98	17	2016	2016	NUM
fcis-6024	98	18	ieee	ieee	NOUN
fcis-6024	98	19	conference	conference	NOUN
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fcis-6024	98	21	computer	computer	NOUN
fcis-6024	98	22	vision	vision	NOUN
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fcis-6024	98	24	pattern	pattern	NOUN
fcis-6024	98	25	recognition	recognition	NOUN
fcis-6024	98	26	.	.	PUNCT
fcis-6024	99	1	las	las	PROPN
fcis-6024	99	2	vegas	vegas	PROPN
fcis-6024	99	3	,	,	PUNCT
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fcis-6024	99	7	:	:	PUNCT
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fcis-6024	99	9	-	-	SYM
fcis-6024	99	10	788	788	NUM
fcis-6024	99	11	[	[	X
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fcis-6024	99	13	:	:	PUNCT
fcis-6024	99	14	10	10	NUM
fcis-6024	99	15	.	.	X
fcis-6024	99	16	1109	1109	NUM
fcis-6024	99	17	/	/	SYM
fcis-6024	99	18	cvpr	cvpr	NOUN
fcis-6024	99	19	.	.	X
fcis-6024	100	1	2016	2016	NUM
fcis-6024	100	2	.	.	PUNCT
fcis-6024	101	1	91	91	NUM
fcis-6024	101	2	]	]	PUNCT
fcis-6024	101	3	.	.	PUNCT
fcis-6024	102	1	[	[	X
fcis-6024	102	2	9	9	NUM
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fcis-6024	102	5	j	j	PROPN
fcis-6024	102	6	,	,	PUNCT
fcis-6024	102	7	farhadi	farhadi	PROPN
fcis-6024	102	8	a.	a.	PROPN
fcis-6024	102	9	yolo9000	yolo9000	PROPN
fcis-6024	102	10	:	:	PUNCT
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fcis-6024	102	12	,	,	PUNCT
fcis-6024	102	13	faster	fast	ADV
fcis-6024	102	14	,	,	PUNCT
fcis-6024	102	15	stronger[c]//	stronger[c]//	PROPN
fcis-6024	102	16	ieee	ieee	PROPN
fcis-6024	102	17	conference	conference	NOUN
fcis-6024	102	18	on	on	ADP
fcis-6024	102	19	computer	computer	NOUN
fcis-6024	102	20	vision	vision	NOUN
fcis-6024	102	21	&	&	CCONJ
fcis-6024	102	22	pattern	pattern	NOUN
fcis-6024	102	23	recognition	recognition	NOUN
fcis-6024	102	24	.	.	PUNCT
fcis-6024	103	1	ieee	ieee	PROPN
fcis-6024	103	2	,	,	PUNCT
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fcis-6024	103	4	-	-	SYM
fcis-6024	103	5	6525	6525	NUM
fcis-6024	104	1	[	[	X
fcis-6024	104	2	10	10	NUM
fcis-6024	104	3	]	]	X
fcis-6024	104	4	redmon	redmon	PROPN
fcis-6024	104	5	j	j	PROPN
fcis-6024	104	6	,	,	PUNCT
fcis-6024	104	7	farhadi	farhadi	PROPN
fcis-6024	104	8	a.	a.	PROPN
fcis-6024	104	9	yolov3	yolov3	PROPN
fcis-6024	104	10	:	:	PUNCT
fcis-6024	105	1	an	an	DET
fcis-6024	105	2	incremental	incremental	ADJ
fcis-6024	105	3	improvement[j	improvement[j	NOUN
fcis-6024	105	4	]	]	PUNCT
fcis-6024	105	5	.	.	PUNCT
fcis-6024	106	1	arxiv	arxiv	PROPN
fcis-6024	106	2	e	e	PROPN
fcis-6024	106	3	-	-	NOUN
fcis-6024	106	4	prints	print	NOUN
fcis-6024	106	5	,	,	PUNCT
fcis-6024	106	6	2018	2018	NUM
fcis-6024	106	7	.	.	PUNCT
fcis-6024	107	1	[	[	X
fcis-6024	107	2	11	11	NUM
fcis-6024	107	3	]	]	PUNCT
fcis-6024	107	4	bochkovskiy	bochkovskiy	X
fcis-6024	107	5	a	a	PRON
fcis-6024	107	6	,	,	PUNCT
fcis-6024	107	7	wang	wang	PROPN
fcis-6024	107	8	c	c	PROPN
fcis-6024	107	9	y	y	PROPN
fcis-6024	107	10	and	and	CCONJ
fcis-6024	107	11	mark	mark	PROPN
fcis-6024	107	12	liao	liao	PROPN
fcis-6024	107	13	h	h	PROPN
fcis-6024	107	14	y.	y.	PROPN
fcis-6024	107	15	2020	2020	NUM
fcis-6024	107	16	.	.	PUNCT
fcis-6024	108	1	yolov4	yolov4	NOUN
fcis-6024	108	2	:	:	PUNCT
fcis-6024	108	3	optimal	optimal	ADJ
fcis-6024	108	4	speed	speed	NOUN
fcis-6024	108	5	and	and	CCONJ
fcis-6024	108	6	accuracy	accuracy	NOUN
fcis-6024	108	7	of	of	ADP
fcis-6024	108	8	object	object	NOUN
fcis-6024	108	9	detection	detection	NOUN
fcis-6024	108	10	[	[	PUNCT
fcis-6024	108	11	eb	eb	PROPN
fcis-6024	108	12	/	/	SYM
fcis-6024	108	13	ol	ol	PROPN
fcis-6024	108	14	]	]	PUNCT
fcis-6024	108	15	.	.	PUNCT
fcis-6024	109	1	[	[	X
fcis-6024	109	2	2020	2020	NUM
fcis-6024	109	3	-	-	SYM
fcis-6024	109	4	04	04	NUM
fcis-6024	109	5	-	-	PUNCT
fcis-6024	109	6	23	23	NUM
fcis-6024	109	7	]	]	PUNCT
fcis-6024	109	8	.	.	PUNCT
fcis-6024	110	1	https	https	NOUN
fcis-6024	110	2	:	:	PUNCT
fcis-6024	110	3	/	/	SYM
fcis-6024	110	4	/	/	SYM
fcis-6024	110	5	arxiv	arxiv	PROPN
fcis-6024	110	6	.	.	PUNCT
fcis-6024	111	1	org	org	PROPN
fcis-6024	111	2	/	/	SYM
fcis-6024	111	3	pdf	pdf	PROPN
fcis-6024	111	4	/	/	SYM
fcis-6024	111	5	2004	2004	NUM
fcis-6024	111	6	.	.	PUNCT
fcis-6024	112	1	10934	10934	NUM
fcis-6024	112	2	.	.	PUNCT
fcis-6024	113	1	pd	pd	X
fcis-6024	114	1	[	[	X
fcis-6024	114	2	12	12	NUM
fcis-6024	114	3	]	]	X
fcis-6024	114	4	pan	pan	NOUN
fcis-6024	114	5	x	x	SYM
fcis-6024	114	6	r	r	PROPN
fcis-6024	114	7	,	,	PUNCT
fcis-6024	114	8	ge	ge	PROPN
fcis-6024	114	9	c	c	PROPN
fcis-6024	114	10	j	j	PROPN
fcis-6024	114	11	,	,	PUNCT
fcis-6024	114	12	lu	lu	PROPN
fcis-6024	114	13	r	r	PROPN
fcis-6024	114	14	,	,	PUNCT
fcis-6024	114	15	et	et	PROPN
fcis-6024	114	16	al	al	PROPN
fcis-6024	114	17	.	.	PROPN
fcis-6024	115	1	on	on	ADP
fcis-6024	115	2	the	the	DET
fcis-6024	115	3	integration	integration	NOUN
fcis-6024	115	4	of	of	ADP
fcis-6024	115	5	selfattention	selfattention	NOUN
fcis-6024	115	6	and	and	CCONJ
fcis-6024	115	7	convolution	convolution	NOUN
fcis-6024	115	8	[	[	X
fcis-6024	115	9	c]//proceedings	c]//proceeding	NOUN
fcis-6024	115	10	of	of	ADP
fcis-6024	115	11	2022	2022	NUM
fcis-6024	115	12	ieee	ieee	NOUN
fcis-6024	115	13	/	/	SYM
fcis-6024	115	14	cvf	cvf	NOUN
fcis-6024	115	15	conference	conference	NOUN
fcis-6024	115	16	on	on	ADP
fcis-6024	115	17	computer	computer	NOUN
fcis-6024	115	18	vision	vision	NOUN
fcis-6024	115	19	and	and	CCONJ
fcis-6024	115	20	patternrecognition	patternrecognition	NOUN
fcis-6024	115	21	.	.	PUNCT
fcis-6024	116	1	washington	washington	PROPN
fcis-6024	116	2	d.	d.	PROPN
fcis-6024	116	3	c.	c.	PROPN
fcis-6024	116	4	,	,	PUNCT
fcis-6024	116	5	usa	usa	PROPN
fcis-6024	116	6	:	:	PUNCT
fcis-6024	116	7	ieee	ieee	NOUN
fcis-6024	116	8	press	press	NOUN
fcis-6024	116	9	,	,	PUNCT
fcis-6024	116	10	2022	2022	NUM
fcis-6024	116	11	:	:	PUNCT
fcis-6024	116	12	805	805	NUM
fcis-6024	116	13	-	-	SYM
fcis-6024	116	14	815	815	NUM
fcis-6024	116	15	.	.	PUNCT
fcis-6024	116	16	mailto	mailto	PROPN
fcis-6024	116	17	:	:	PUNCT
fcis-6024	116	18	map@.5:.0.95表示map	map@.5:.0.95表示map	PROPN
fcis-6024	116	19	mailto	mailto	PROPN
fcis-6024	116	20	:	:	PUNCT
fcis-6024	116	21	map@.5:.0.95表示map	map@.5:.0.95表示map	INTJ