id	sid	tid	token	lemma	pos
fcis-2486	1	1	frontiers	frontier	NOUN
fcis-2486	1	2	in	in	ADP
fcis-2486	1	3	computing	computing	NOUN
fcis-2486	1	4	and	and	CCONJ
fcis-2486	1	5	intelligent	intelligent	ADJ
fcis-2486	1	6	systems	system	NOUN
fcis-2486	1	7	issn	issn	VERB
fcis-2486	1	8	:	:	PUNCT
fcis-2486	1	9	2832	2832	NUM
fcis-2486	1	10	-	-	SYM
fcis-2486	1	11	6024	6024	NUM
fcis-2486	1	12	|	|	NOUN
fcis-2486	1	13	vol	vol	NOUN
fcis-2486	1	14	.	.	PROPN
fcis-2486	2	1	2	2	NUM
fcis-2486	2	2	,	,	PUNCT
fcis-2486	2	3	no	no	INTJ
fcis-2486	2	4	.	.	NOUN
fcis-2486	2	5	1	1	NUM
fcis-2486	2	6	,	,	PUNCT
fcis-2486	2	7	2022	2022	NUM
fcis-2486	2	8	13	13	NUM
fcis-2486	2	9	lightweight	lightweight	ADJ
fcis-2486	2	10	multi	multi	ADJ
fcis-2486	2	11	-	-	ADJ
fcis-2486	2	12	attention	attention	ADJ
fcis-2486	2	13	fusion	fusion	NOUN
fcis-2486	2	14	network	network	NOUN
fcis-2486	2	15	for	for	ADP
fcis-2486	2	16	image	image	NOUN
fcis-2486	2	17	super	super	NOUN
fcis-2486	2	18	-	-	NOUN
fcis-2486	2	19	resolution	resolution	NOUN
fcis-2486	2	20	xinyu	xinyu	PROPN
fcis-2486	2	21	wang	wang	PROPN
fcis-2486	2	22	,	,	PUNCT
fcis-2486	2	23	jing	jing	PROPN
fcis-2486	2	24	zhang	zhang	PROPN
fcis-2486	2	25	school	school	PROPN
fcis-2486	2	26	of	of	ADP
fcis-2486	2	27	computer	computer	NOUN
fcis-2486	2	28	science	science	NOUN
fcis-2486	2	29	and	and	CCONJ
fcis-2486	2	30	technology	technology	NOUN
fcis-2486	2	31	,	,	PUNCT
fcis-2486	2	32	tiangong	tiangong	PROPN
fcis-2486	2	33	university	university	PROPN
fcis-2486	2	34	,	,	PUNCT
fcis-2486	2	35	tianjin	tianjin	PROPN
fcis-2486	2	36	,	,	PUNCT
fcis-2486	2	37	china	china	PROPN
fcis-2486	2	38	abstract	abstract	PROPN
fcis-2486	2	39	:	:	PUNCT
fcis-2486	2	40	single	single	ADJ
fcis-2486	2	41	image	image	NOUN
fcis-2486	2	42	super	super	ADJ
fcis-2486	2	43	-	-	ADJ
fcis-2486	2	44	resolution	resolution	ADJ
fcis-2486	2	45	reconstruction	reconstruction	NOUN
fcis-2486	2	46	(	(	PUNCT
fcis-2486	2	47	sisr	sisr	NOUN
fcis-2486	2	48	)	)	PUNCT
fcis-2486	2	49	is	be	AUX
fcis-2486	2	50	one	one	NUM
fcis-2486	2	51	of	of	ADP
fcis-2486	2	52	the	the	DET
fcis-2486	2	53	important	important	ADJ
fcis-2486	2	54	techniques	technique	NOUN
fcis-2486	2	55	in	in	ADP
fcis-2486	2	56	computer	computer	NOUN
fcis-2486	2	57	vision	vision	NOUN
fcis-2486	2	58	and	and	CCONJ
fcis-2486	2	59	image	image	NOUN
fcis-2486	2	60	processing	processing	NOUN
fcis-2486	2	61	.	.	PUNCT
fcis-2486	3	1	most	most	ADJ
fcis-2486	3	2	of	of	ADP
fcis-2486	3	3	the	the	DET
fcis-2486	3	4	existing	exist	VERB
fcis-2486	3	5	sisr	sisr	NOUN
fcis-2486	3	6	methods	method	NOUN
fcis-2486	3	7	adopt	adopt	VERB
fcis-2486	3	8	equal	equal	ADJ
fcis-2486	3	9	processing	processing	NOUN
fcis-2486	3	10	for	for	ADP
fcis-2486	3	11	different	different	ADJ
fcis-2486	3	12	spatial	spatial	ADJ
fcis-2486	3	13	domains	domain	NOUN
fcis-2486	3	14	and	and	CCONJ
fcis-2486	3	15	channel	channel	NOUN
fcis-2486	3	16	domains	domain	NOUN
fcis-2486	3	17	,	,	PUNCT
fcis-2486	3	18	resulting	result	VERB
fcis-2486	3	19	in	in	ADP
fcis-2486	3	20	a	a	DET
fcis-2486	3	21	large	large	ADJ
fcis-2486	3	22	amount	amount	NOUN
fcis-2486	3	23	of	of	ADP
fcis-2486	3	24	computational	computational	ADJ
fcis-2486	3	25	resources	resource	NOUN
fcis-2486	3	26	wasted	waste	VERB
fcis-2486	3	27	on	on	ADP
fcis-2486	3	28	unimportant	unimportant	ADJ
fcis-2486	3	29	features	feature	NOUN
fcis-2486	3	30	.	.	PUNCT
fcis-2486	4	1	in	in	ADP
fcis-2486	4	2	order	order	NOUN
fcis-2486	4	3	to	to	PART
fcis-2486	4	4	address	address	VERB
fcis-2486	4	5	these	these	DET
fcis-2486	4	6	problems	problem	NOUN
fcis-2486	4	7	,	,	PUNCT
fcis-2486	4	8	a	a	DET
fcis-2486	4	9	novel	novel	ADJ
fcis-2486	4	10	lightweight	lightweight	ADJ
fcis-2486	4	11	multi	multi	ADJ
fcis-2486	4	12	-	-	ADJ
fcis-2486	4	13	attention	attention	ADJ
fcis-2486	4	14	fusion	fusion	NOUN
fcis-2486	4	15	network	network	NOUN
fcis-2486	4	16	(	(	PUNCT
fcis-2486	4	17	lmafn	lmafn	PROPN
fcis-2486	4	18	)	)	PUNCT
fcis-2486	4	19	is	be	AUX
fcis-2486	4	20	proposed	propose	VERB
fcis-2486	4	21	,	,	PUNCT
fcis-2486	4	22	in	in	ADP
fcis-2486	4	23	which	which	PRON
fcis-2486	4	24	the	the	DET
fcis-2486	4	25	multiple	multiple	ADJ
fcis-2486	4	26	attention	attention	NOUN
fcis-2486	4	27	fusion	fusion	NOUN
fcis-2486	4	28	block	block	NOUN
fcis-2486	4	29	allocates	allocate	VERB
fcis-2486	4	30	computational	computational	ADJ
fcis-2486	4	31	resources	resource	NOUN
fcis-2486	4	32	more	more	ADV
fcis-2486	4	33	efficiently	efficiently	ADV
fcis-2486	4	34	by	by	ADP
fcis-2486	4	35	capturing	capture	VERB
fcis-2486	4	36	the	the	DET
fcis-2486	4	37	weight	weight	NOUN
fcis-2486	4	38	information	information	NOUN
fcis-2486	4	39	implied	imply	VERB
fcis-2486	4	40	by	by	ADP
fcis-2486	4	41	the	the	DET
fcis-2486	4	42	channel	channel	NOUN
fcis-2486	4	43	domain	domain	NOUN
fcis-2486	4	44	and	and	CCONJ
fcis-2486	4	45	the	the	DET
fcis-2486	4	46	spatial	spatial	ADJ
fcis-2486	4	47	domain	domain	NOUN
fcis-2486	4	48	separately	separately	ADV
fcis-2486	4	49	,	,	PUNCT
fcis-2486	4	50	thus	thus	ADV
fcis-2486	4	51	effectively	effectively	ADV
fcis-2486	4	52	reducing	reduce	VERB
fcis-2486	4	53	the	the	DET
fcis-2486	4	54	number	number	NOUN
fcis-2486	4	55	of	of	ADP
fcis-2486	4	56	parameters	parameter	NOUN
fcis-2486	4	57	.	.	PUNCT
fcis-2486	5	1	the	the	DET
fcis-2486	5	2	synthetic	synthetic	ADJ
fcis-2486	5	3	channel	channel	NOUN
fcis-2486	5	4	attention	attention	NOUN
fcis-2486	5	5	block	block	NOUN
fcis-2486	5	6	in	in	ADP
fcis-2486	5	7	the	the	DET
fcis-2486	5	8	multiple	multiple	ADJ
fcis-2486	5	9	attention	attention	NOUN
fcis-2486	5	10	fusion	fusion	NOUN
fcis-2486	5	11	block	block	NOUN
fcis-2486	5	12	makes	make	VERB
fcis-2486	5	13	full	full	ADJ
fcis-2486	5	14	use	use	NOUN
fcis-2486	5	15	of	of	ADP
fcis-2486	5	16	inter	inter	ADJ
fcis-2486	5	17	-	-	ADJ
fcis-2486	5	18	channel	channel	ADJ
fcis-2486	5	19	correlation	correlation	NOUN
fcis-2486	5	20	by	by	ADP
fcis-2486	5	21	introducing	introduce	VERB
fcis-2486	5	22	both	both	CCONJ
fcis-2486	5	23	global	global	ADJ
fcis-2486	5	24	standard	standard	ADJ
fcis-2486	5	25	deviation	deviation	NOUN
fcis-2486	5	26	pooling	pooling	NOUN
fcis-2486	5	27	and	and	CCONJ
fcis-2486	5	28	maximum	maximum	ADJ
fcis-2486	5	29	pooling	pooling	NOUN
fcis-2486	5	30	.	.	PUNCT
fcis-2486	6	1	global	global	ADJ
fcis-2486	6	2	features	feature	NOUN
fcis-2486	6	3	are	be	AUX
fcis-2486	6	4	fused	fuse	VERB
fcis-2486	6	5	through	through	ADP
fcis-2486	6	6	residual	residual	ADJ
fcis-2486	6	7	linking	linking	NOUN
fcis-2486	6	8	to	to	PART
fcis-2486	6	9	alleviate	alleviate	VERB
fcis-2486	6	10	the	the	DET
fcis-2486	6	11	problem	problem	NOUN
fcis-2486	6	12	of	of	ADP
fcis-2486	6	13	high	high	ADJ
fcis-2486	6	14	frequency	frequency	NOUN
fcis-2486	6	15	information	information	NOUN
fcis-2486	6	16	loss	loss	NOUN
fcis-2486	6	17	.	.	PUNCT
fcis-2486	7	1	experimental	experimental	ADJ
fcis-2486	7	2	results	result	NOUN
fcis-2486	7	3	on	on	ADP
fcis-2486	7	4	several	several	ADJ
fcis-2486	7	5	benchmark	benchmark	NOUN
fcis-2486	7	6	datasets	dataset	NOUN
fcis-2486	7	7	show	show	VERB
fcis-2486	7	8	that	that	SCONJ
fcis-2486	7	9	the	the	DET
fcis-2486	7	10	proposed	propose	VERB
fcis-2486	7	11	method	method	NOUN
fcis-2486	7	12	effectively	effectively	ADV
fcis-2486	7	13	reduces	reduce	VERB
fcis-2486	7	14	the	the	DET
fcis-2486	7	15	number	number	NOUN
fcis-2486	7	16	of	of	ADP
fcis-2486	7	17	parameters	parameter	NOUN
fcis-2486	7	18	and	and	CCONJ
fcis-2486	7	19	computational	computational	ADJ
fcis-2486	7	20	effort	effort	NOUN
fcis-2486	7	21	without	without	ADP
fcis-2486	7	22	excessive	excessive	ADJ
fcis-2486	7	23	loss	loss	NOUN
fcis-2486	7	24	of	of	ADP
fcis-2486	7	25	reconstruction	reconstruction	NOUN
fcis-2486	7	26	performance	performance	NOUN
fcis-2486	7	27	,	,	PUNCT
fcis-2486	7	28	and	and	CCONJ
fcis-2486	7	29	achieves	achieve	VERB
fcis-2486	7	30	better	well	ADJ
fcis-2486	7	31	performance	performance	NOUN
fcis-2486	7	32	than	than	ADP
fcis-2486	7	33	the	the	DET
fcis-2486	7	34	compared	compare	VERB
fcis-2486	7	35	models	model	NOUN
fcis-2486	7	36	.	.	PUNCT
fcis-2486	8	1	keywords	keyword	NOUN
fcis-2486	8	2	:	:	PUNCT
fcis-2486	8	3	attention	attention	NOUN
fcis-2486	8	4	mechanism	mechanism	NOUN
fcis-2486	8	5	;	;	PUNCT
fcis-2486	8	6	image	image	NOUN
fcis-2486	8	7	super	super	NOUN
fcis-2486	8	8	-	-	NOUN
fcis-2486	8	9	resolution	resolution	NOUN
fcis-2486	8	10	;	;	PUNCT
fcis-2486	8	11	lightweight	lightweight	ADJ
fcis-2486	8	12	neural	neural	ADJ
fcis-2486	8	13	network	network	NOUN
fcis-2486	8	14	.	.	PUNCT
fcis-2486	9	1	1	1	X
fcis-2486	9	2	.	.	X
fcis-2486	9	3	introduction	introduction	NOUN
fcis-2486	9	4	image	image	NOUN
fcis-2486	9	5	super	super	ADJ
fcis-2486	9	6	-	-	ADJ
fcis-2486	9	7	resolution	resolution	ADJ
fcis-2486	9	8	reconstruction	reconstruction	NOUN
fcis-2486	9	9	is	be	AUX
fcis-2486	9	10	the	the	DET
fcis-2486	9	11	process	process	NOUN
fcis-2486	9	12	of	of	ADP
fcis-2486	9	13	restoring	restore	VERB
fcis-2486	9	14	a	a	DET
fcis-2486	9	15	low	low	ADJ
fcis-2486	9	16	-	-	PUNCT
fcis-2486	9	17	resolution	resolution	NOUN
fcis-2486	9	18	image	image	NOUN
fcis-2486	9	19	to	to	ADP
fcis-2486	9	20	a	a	DET
fcis-2486	9	21	high	high	ADJ
fcis-2486	9	22	-	-	PUNCT
fcis-2486	9	23	resolution	resolution	NOUN
fcis-2486	9	24	image	image	NOUN
fcis-2486	9	25	by	by	ADP
fcis-2486	9	26	algorithms	algorithm	NOUN
fcis-2486	9	27	and	and	CCONJ
fcis-2486	9	28	is	be	AUX
fcis-2486	9	29	one	one	NUM
fcis-2486	9	30	of	of	ADP
fcis-2486	9	31	the	the	DET
fcis-2486	9	32	key	key	ADJ
fcis-2486	9	33	techniques	technique	NOUN
fcis-2486	9	34	in	in	ADP
fcis-2486	9	35	computer	computer	NOUN
fcis-2486	9	36	vision	vision	NOUN
fcis-2486	9	37	and	and	CCONJ
fcis-2486	9	38	image	image	NOUN
fcis-2486	9	39	processing	processing	NOUN
fcis-2486	9	40	.	.	PUNCT
fcis-2486	10	1	the	the	DET
fcis-2486	10	2	concept	concept	NOUN
fcis-2486	10	3	has	have	AUX
fcis-2486	10	4	been	be	AUX
fcis-2486	10	5	of	of	ADP
fcis-2486	10	6	great	great	ADJ
fcis-2486	10	7	academic	academic	ADJ
fcis-2486	10	8	interest	interest	NOUN
fcis-2486	10	9	since	since	SCONJ
fcis-2486	10	10	it	it	PRON
fcis-2486	10	11	was	be	AUX
fcis-2486	10	12	proposed	propose	VERB
fcis-2486	10	13	.	.	PUNCT
fcis-2486	11	1	in	in	ADP
fcis-2486	11	2	2015	2015	NUM
fcis-2486	11	3	,	,	PUNCT
fcis-2486	11	4	dong	dong	PROPN
fcis-2486	11	5	et	et	PROPN
fcis-2486	11	6	al	al	PROPN
fcis-2486	11	7	.	.	PUNCT
fcis-2486	12	1	[	[	X
fcis-2486	12	2	1	1	X
fcis-2486	12	3	]	]	PUNCT
fcis-2486	12	4	proposed	propose	VERB
fcis-2486	12	5	srcnn	srcnn	NOUN
fcis-2486	12	6	by	by	ADP
fcis-2486	12	7	combining	combine	VERB
fcis-2486	12	8	sisr	sisr	NOUN
fcis-2486	12	9	with	with	ADP
fcis-2486	12	10	cnn	cnn	PROPN
fcis-2486	12	11	.	.	PUNCT
fcis-2486	13	1	and	and	CCONJ
fcis-2486	13	2	then	then	ADV
fcis-2486	13	3	more	more	ADV
fcis-2486	13	4	complex	complex	ADJ
fcis-2486	13	5	architectures	architecture	NOUN
fcis-2486	13	6	are	be	AUX
fcis-2486	13	7	proposed	propose	VERB
fcis-2486	13	8	to	to	PART
fcis-2486	13	9	improve	improve	VERB
fcis-2486	13	10	the	the	DET
fcis-2486	13	11	performance	performance	NOUN
fcis-2486	13	12	of	of	ADP
fcis-2486	13	13	sr	sr	PROPN
fcis-2486	13	14	methods	method	NOUN
fcis-2486	13	15	,	,	PUNCT
fcis-2486	13	16	such	such	ADJ
fcis-2486	13	17	as	as	ADP
fcis-2486	13	18	srgan	srgan	NOUN
fcis-2486	13	19	[	[	X
fcis-2486	13	20	2	2	NUM
fcis-2486	13	21	]	]	PUNCT
fcis-2486	13	22	.	.	PUNCT
fcis-2486	14	1	however	however	ADV
fcis-2486	14	2	,	,	PUNCT
fcis-2486	14	3	it	it	PRON
fcis-2486	14	4	is	be	AUX
fcis-2486	14	5	difficult	difficult	ADJ
fcis-2486	14	6	to	to	PART
fcis-2486	14	7	directly	directly	ADV
fcis-2486	14	8	apply	apply	VERB
fcis-2486	14	9	them	they	PRON
fcis-2486	14	10	in	in	ADP
fcis-2486	14	11	portable	portable	ADJ
fcis-2486	14	12	mobile	mobile	ADJ
fcis-2486	14	13	devices	device	NOUN
fcis-2486	14	14	due	due	ADP
fcis-2486	14	15	to	to	ADP
fcis-2486	14	16	their	their	PRON
fcis-2486	14	17	computational	computational	ADJ
fcis-2486	14	18	and	and	CCONJ
fcis-2486	14	19	parameter	parameter	NOUN
fcis-2486	14	20	constraints	constraint	NOUN
fcis-2486	14	21	.	.	PUNCT
fcis-2486	15	1	the	the	DET
fcis-2486	15	2	lightweight	lightweight	ADJ
fcis-2486	15	3	research	research	NOUN
fcis-2486	15	4	starts	start	VERB
fcis-2486	15	5	with	with	ADP
fcis-2486	15	6	fsrcnn	fsrcnn	NOUN
fcis-2486	15	7	[	[	X
fcis-2486	15	8	3	3	NUM
fcis-2486	15	9	]	]	PUNCT
fcis-2486	15	10	,	,	PUNCT
fcis-2486	15	11	which	which	PRON
fcis-2486	15	12	directly	directly	ADV
fcis-2486	15	13	applies	apply	VERB
fcis-2486	15	14	the	the	DET
fcis-2486	15	15	sr	sr	PROPN
fcis-2486	15	16	network	network	NOUN
fcis-2486	15	17	to	to	ADP
fcis-2486	15	18	the	the	DET
fcis-2486	15	19	lr	lr	NOUN
fcis-2486	15	20	image	image	NOUN
fcis-2486	15	21	with	with	ADP
fcis-2486	15	22	removing	remove	VERB
fcis-2486	15	23	costly	costly	ADJ
fcis-2486	15	24	up	up	ADP
fcis-2486	15	25	sampling	sample	VERB
fcis-2486	15	26	layers	layer	NOUN
fcis-2486	15	27	to	to	PART
fcis-2486	15	28	reduce	reduce	VERB
fcis-2486	15	29	training	training	NOUN
fcis-2486	15	30	time	time	NOUN
fcis-2486	15	31	.	.	PUNCT
fcis-2486	16	1	drcn	drcn	PROPN
fcis-2486	16	2	[	[	X
fcis-2486	16	3	4	4	NUM
fcis-2486	16	4	]	]	PUNCT
fcis-2486	16	5	was	be	AUX
fcis-2486	16	6	the	the	DET
fcis-2486	16	7	first	first	ADJ
fcis-2486	16	8	to	to	PART
fcis-2486	16	9	apply	apply	VERB
fcis-2486	16	10	a	a	DET
fcis-2486	16	11	recursive	recursive	ADJ
fcis-2486	16	12	algorithm	algorithm	NOUN
fcis-2486	16	13	to	to	ADP
fcis-2486	16	14	sisr	sisr	NOUN
fcis-2486	16	15	,	,	PUNCT
fcis-2486	16	16	and	and	CCONJ
fcis-2486	16	17	reduce	reduce	VERB
fcis-2486	16	18	parameters	parameter	NOUN
fcis-2486	16	19	by	by	ADP
fcis-2486	16	20	reusing	reuse	VERB
fcis-2486	16	21	part	part	NOUN
fcis-2486	16	22	of	of	ADP
fcis-2486	16	23	the	the	DET
fcis-2486	16	24	parameter	parameter	NOUN
fcis-2486	16	25	’s	’s	PART
fcis-2486	16	26	multiple	multiple	ADJ
fcis-2486	16	27	times	time	NOUN
fcis-2486	16	28	.	.	PUNCT
fcis-2486	17	1	later	later	ADV
fcis-2486	17	2	,	,	PUNCT
fcis-2486	17	3	drrn	drrn	VERB
fcis-2486	17	4	[	[	X
fcis-2486	17	5	5	5	NUM
fcis-2486	17	6	]	]	PUNCT
fcis-2486	17	7	utilizes	utilize	VERB
fcis-2486	17	8	recurrent	recurrent	ADJ
fcis-2486	17	9	layers	layer	NOUN
fcis-2486	17	10	to	to	PART
fcis-2486	17	11	reduce	reduce	VERB
fcis-2486	17	12	parameters	parameter	NOUN
fcis-2486	17	13	while	while	SCONJ
fcis-2486	17	14	maintaining	maintain	VERB
fcis-2486	17	15	the	the	DET
fcis-2486	17	16	depth	depth	NOUN
fcis-2486	17	17	of	of	ADP
fcis-2486	17	18	the	the	DET
fcis-2486	17	19	network	network	NOUN
fcis-2486	17	20	.	.	PUNCT
fcis-2486	18	1	edsr	edsr	PROPN
fcis-2486	18	2	[	[	X
fcis-2486	18	3	6	6	NUM
fcis-2486	18	4	]	]	PUNCT
fcis-2486	18	5	modifies	modify	VERB
fcis-2486	18	6	the	the	DET
fcis-2486	18	7	structure	structure	NOUN
fcis-2486	18	8	of	of	ADP
fcis-2486	18	9	the	the	DET
fcis-2486	18	10	residual	residual	ADJ
fcis-2486	18	11	block	block	NOUN
fcis-2486	18	12	by	by	ADP
fcis-2486	18	13	design	design	NOUN
fcis-2486	18	14	and	and	CCONJ
fcis-2486	18	15	removes	remove	VERB
fcis-2486	18	16	the	the	DET
fcis-2486	18	17	bn	bn	ADJ
fcis-2486	18	18	layer	layer	NOUN
fcis-2486	18	19	.	.	PUNCT
fcis-2486	19	1	it	it	PRON
fcis-2486	19	2	could	could	AUX
fcis-2486	19	3	save	save	VERB
fcis-2486	19	4	about	about	ADV
fcis-2486	19	5	40	40	NUM
fcis-2486	19	6	%	%	NOUN
fcis-2486	19	7	of	of	ADP
fcis-2486	19	8	gpu	gpu	NOUN
fcis-2486	19	9	memory	memory	NOUN
fcis-2486	19	10	space	space	NOUN
fcis-2486	19	11	.	.	PUNCT
fcis-2486	20	1	lapsrn	lapsrn	NOUN
fcis-2486	21	1	[	[	X
fcis-2486	21	2	7	7	X
fcis-2486	21	3	]	]	PUNCT
fcis-2486	21	4	uses	use	VERB
fcis-2486	21	5	a	a	DET
fcis-2486	21	6	pyramidal	pyramidal	ADJ
fcis-2486	21	7	framework	framework	NOUN
fcis-2486	21	8	to	to	PART
fcis-2486	21	9	gradually	gradually	ADV
fcis-2486	21	10	increase	increase	VERB
fcis-2486	21	11	the	the	DET
fcis-2486	21	12	image	image	NOUN
fcis-2486	21	13	size	size	NOUN
fcis-2486	21	14	so	so	SCONJ
fcis-2486	21	15	that	that	SCONJ
fcis-2486	21	16	sr	sr	PROPN
fcis-2486	21	17	image	image	NOUN
fcis-2486	21	18	can	can	AUX
fcis-2486	21	19	be	be	AUX
fcis-2486	21	20	performed	perform	VERB
fcis-2486	21	21	efficiently	efficiently	ADV
fcis-2486	21	22	at	at	ADP
fcis-2486	21	23	very	very	ADV
fcis-2486	21	24	low	low	ADJ
fcis-2486	21	25	resolution	resolution	NOUN
fcis-2486	21	26	.	.	PUNCT
fcis-2486	22	1	cbpn	cbpn	PROPN
fcis-2486	23	1	[	[	X
fcis-2486	23	2	8	8	NUM
fcis-2486	23	3	]	]	PUNCT
fcis-2486	23	4	is	be	AUX
fcis-2486	23	5	proposed	propose	VERB
fcis-2486	23	6	to	to	PART
fcis-2486	23	7	replace	replace	VERB
fcis-2486	23	8	the	the	DET
fcis-2486	23	9	previous	previous	ADJ
fcis-2486	23	10	up	up	ADP
fcis-2486	23	11	-	-	PUNCT
fcis-2486	23	12	down	down	ADP
fcis-2486	23	13	projection	projection	NOUN
fcis-2486	23	14	module	module	NOUN
fcis-2486	23	15	with	with	ADP
fcis-2486	23	16	a	a	DET
fcis-2486	23	17	pixel	pixel	ADJ
fcis-2486	23	18	shuffle	shuffle	NOUN
fcis-2486	23	19	layer	layer	NOUN
fcis-2486	23	20	.	.	PUNCT
fcis-2486	24	1	with	with	ADP
fcis-2486	24	2	the	the	DET
fcis-2486	24	3	further	further	ADJ
fcis-2486	24	4	development	development	NOUN
fcis-2486	24	5	of	of	ADP
fcis-2486	24	6	sisr	sisr	NOUN
fcis-2486	24	7	,	,	PUNCT
fcis-2486	24	8	many	many	ADJ
fcis-2486	24	9	issues	issue	NOUN
fcis-2486	24	10	arise	arise	VERB
fcis-2486	24	11	.	.	PUNCT
fcis-2486	25	1	firstly	firstly	ADV
fcis-2486	25	2	,	,	PUNCT
fcis-2486	25	3	most	most	ADV
fcis-2486	25	4	available	available	ADJ
fcis-2486	25	5	cnn	cnn	PROPN
fcis-2486	25	6	based	base	VERB
fcis-2486	25	7	models	model	NOUN
fcis-2486	25	8	are	be	AUX
fcis-2486	25	9	primarily	primarily	ADV
fcis-2486	25	10	use	use	VERB
fcis-2486	25	11	multiple	multiple	ADJ
fcis-2486	25	12	stacked	stack	VERB
fcis-2486	25	13	convolution	convolution	NOUN
fcis-2486	25	14	operations	operation	NOUN
fcis-2486	25	15	and	and	CCONJ
fcis-2486	25	16	increase	increase	VERB
fcis-2486	25	17	the	the	DET
fcis-2486	25	18	convolution	convolution	NOUN
fcis-2486	25	19	kernel	kernel	NOUN
fcis-2486	25	20	to	to	PART
fcis-2486	25	21	enhance	enhance	VERB
fcis-2486	25	22	the	the	DET
fcis-2486	25	23	image	image	NOUN
fcis-2486	25	24	reconstruction	reconstruction	NOUN
fcis-2486	25	25	’s	’s	PART
fcis-2486	25	26	quality	quality	NOUN
fcis-2486	25	27	,	,	PUNCT
fcis-2486	25	28	but	but	CCONJ
fcis-2486	25	29	at	at	ADP
fcis-2486	25	30	the	the	DET
fcis-2486	25	31	same	same	ADJ
fcis-2486	25	32	time	time	NOUN
fcis-2486	25	33	bring	bring	VERB
fcis-2486	25	34	a	a	DET
fcis-2486	25	35	large	large	ADJ
fcis-2486	25	36	amount	amount	NOUN
fcis-2486	25	37	of	of	ADP
fcis-2486	25	38	computation	computation	NOUN
fcis-2486	25	39	.	.	PUNCT
fcis-2486	26	1	secondly	secondly	ADV
fcis-2486	26	2	,	,	PUNCT
fcis-2486	26	3	the	the	DET
fcis-2486	26	4	majority	majority	NOUN
fcis-2486	26	5	of	of	ADP
fcis-2486	26	6	current	current	ADJ
fcis-2486	26	7	cnn	cnn	PROPN
fcis-2486	26	8	networks	network	NOUN
fcis-2486	26	9	obtain	obtain	VERB
fcis-2486	26	10	features	feature	NOUN
fcis-2486	26	11	by	by	ADP
fcis-2486	26	12	successive	successive	ADJ
fcis-2486	26	13	convolution	convolution	NOUN
fcis-2486	26	14	operations	operation	NOUN
fcis-2486	26	15	while	while	SCONJ
fcis-2486	26	16	using	use	VERB
fcis-2486	26	17	identical	identical	ADJ
fcis-2486	26	18	processing	processing	NOUN
fcis-2486	26	19	for	for	ADP
fcis-2486	26	20	feature	feature	NOUN
fcis-2486	26	21	of	of	ADP
fcis-2486	26	22	each	each	DET
fcis-2486	26	23	channel	channel	NOUN
fcis-2486	26	24	and	and	CCONJ
fcis-2486	26	25	position	position	NOUN
fcis-2486	26	26	,	,	PUNCT
fcis-2486	26	27	but	but	CCONJ
fcis-2486	26	28	the	the	DET
fcis-2486	26	29	importance	importance	NOUN
fcis-2486	26	30	of	of	ADP
fcis-2486	26	31	the	the	DET
fcis-2486	26	32	features	feature	NOUN
fcis-2486	26	33	is	be	AUX
fcis-2486	26	34	different	different	ADJ
fcis-2486	26	35	from	from	ADP
fcis-2486	26	36	each	each	DET
fcis-2486	26	37	other	other	ADJ
fcis-2486	26	38	,	,	PUNCT
fcis-2486	26	39	and	and	CCONJ
fcis-2486	26	40	equal	equal	ADJ
fcis-2486	26	41	processing	processing	NOUN
fcis-2486	26	42	causes	cause	VERB
fcis-2486	26	43	a	a	DET
fcis-2486	26	44	waste	waste	NOUN
fcis-2486	26	45	of	of	ADP
fcis-2486	26	46	computational	computational	ADJ
fcis-2486	26	47	resources	resource	NOUN
fcis-2486	26	48	and	and	CCONJ
fcis-2486	26	49	consequently	consequently	ADV
fcis-2486	26	50	a	a	DET
fcis-2486	26	51	huge	huge	ADJ
fcis-2486	26	52	memory	memory	NOUN
fcis-2486	26	53	consumption	consumption	NOUN
fcis-2486	26	54	.	.	PUNCT
fcis-2486	27	1	in	in	ADP
fcis-2486	27	2	view	view	NOUN
fcis-2486	27	3	of	of	ADP
fcis-2486	27	4	the	the	DET
fcis-2486	27	5	extant	extant	ADJ
fcis-2486	27	6	shortcomings	shortcoming	NOUN
fcis-2486	27	7	mentioned	mention	VERB
fcis-2486	27	8	above	above	ADV
fcis-2486	27	9	,	,	PUNCT
fcis-2486	27	10	inspired	inspire	VERB
fcis-2486	27	11	by	by	ADP
fcis-2486	27	12	[	[	X
fcis-2486	27	13	9	9	NUM
fcis-2486	27	14	]	]	PUNCT
fcis-2486	27	15	,	,	PUNCT
fcis-2486	27	16	we	we	PRON
fcis-2486	27	17	propose	propose	VERB
fcis-2486	27	18	an	an	DET
fcis-2486	27	19	improved	improved	ADJ
fcis-2486	27	20	lightweight	lightweight	ADJ
fcis-2486	27	21	network	network	NOUN
fcis-2486	27	22	.	.	PUNCT
fcis-2486	28	1	among	among	ADP
fcis-2486	28	2	them	they	PRON
fcis-2486	28	3	,	,	PUNCT
fcis-2486	28	4	the	the	DET
fcis-2486	28	5	multi	multi	ADJ
fcis-2486	28	6	-	-	ADJ
fcis-2486	28	7	attention	attention	ADJ
fcis-2486	28	8	fusion	fusion	NOUN
fcis-2486	28	9	block	block	NOUN
fcis-2486	28	10	(	(	PUNCT
fcis-2486	28	11	mafb	mafb	NOUN
fcis-2486	28	12	)	)	PUNCT
fcis-2486	28	13	focus	focus	NOUN
fcis-2486	28	14	on	on	ADP
fcis-2486	28	15	the	the	DET
fcis-2486	28	16	significance	significance	NOUN
fcis-2486	28	17	of	of	ADP
fcis-2486	28	18	distinct	distinct	ADJ
fcis-2486	28	19	channel	channel	NOUN
fcis-2486	28	20	and	and	CCONJ
fcis-2486	28	21	spatial	spatial	ADJ
fcis-2486	28	22	position	position	NOUN
fcis-2486	28	23	features	feature	VERB
fcis-2486	28	24	to	to	PART
fcis-2486	28	25	acquire	acquire	VERB
fcis-2486	28	26	the	the	DET
fcis-2486	28	27	corresponding	correspond	VERB
fcis-2486	28	28	position	position	NOUN
fcis-2486	28	29	’s	’s	PART
fcis-2486	28	30	weight	weight	NOUN
fcis-2486	28	31	parameters	parameter	NOUN
fcis-2486	28	32	.	.	PUNCT
fcis-2486	29	1	what	what	PRON
fcis-2486	29	2	’s	’	VERB
fcis-2486	29	3	more	more	ADJ
fcis-2486	29	4	,	,	PUNCT
fcis-2486	29	5	the	the	DET
fcis-2486	29	6	global	global	ADJ
fcis-2486	29	7	feature	feature	NOUN
fcis-2486	29	8	supervision	supervision	NOUN
fcis-2486	29	9	is	be	AUX
fcis-2486	29	10	established	establish	VERB
fcis-2486	29	11	via	via	ADP
fcis-2486	29	12	the	the	DET
fcis-2486	29	13	long	long	ADJ
fcis-2486	29	14	skip	skip	ADJ
fcis-2486	29	15	connections	connection	NOUN
fcis-2486	29	16	to	to	PART
fcis-2486	29	17	speed	speed	VERB
fcis-2486	29	18	up	up	ADP
fcis-2486	29	19	network	network	NOUN
fcis-2486	29	20	convergence	convergence	NOUN
fcis-2486	29	21	and	and	CCONJ
fcis-2486	29	22	enable	enable	VERB
fcis-2486	29	23	features	feature	NOUN
fcis-2486	29	24	to	to	PART
fcis-2486	29	25	be	be	AUX
fcis-2486	29	26	effectively	effectively	ADV
fcis-2486	29	27	utilized	utilize	VERB
fcis-2486	29	28	.	.	PUNCT
fcis-2486	30	1	for	for	ADP
fcis-2486	30	2	the	the	DET
fcis-2486	30	3	reconstruction	reconstruction	NOUN
fcis-2486	30	4	part	part	NOUN
fcis-2486	30	5	,	,	PUNCT
fcis-2486	30	6	shallow	shallow	ADJ
fcis-2486	30	7	and	and	CCONJ
fcis-2486	30	8	deep	deep	ADJ
fcis-2486	30	9	features	feature	NOUN
fcis-2486	30	10	are	be	AUX
fcis-2486	30	11	fused	fuse	VERB
fcis-2486	30	12	by	by	ADP
fcis-2486	30	13	residual	residual	ADJ
fcis-2486	30	14	learning	learning	NOUN
fcis-2486	30	15	and	and	CCONJ
fcis-2486	30	16	sub	sub	ADJ
fcis-2486	30	17	-	-	ADJ
fcis-2486	30	18	pixel	pixel	ADJ
fcis-2486	30	19	convolution	convolution	NOUN
fcis-2486	30	20	techniques	technique	NOUN
fcis-2486	30	21	,	,	PUNCT
fcis-2486	30	22	which	which	PRON
fcis-2486	30	23	are	be	AUX
fcis-2486	30	24	complementary	complementary	ADJ
fcis-2486	30	25	to	to	ADP
fcis-2486	30	26	the	the	DET
fcis-2486	30	27	previous	previous	ADJ
fcis-2486	30	28	feature	feature	NOUN
fcis-2486	30	29	extraction	extraction	NOUN
fcis-2486	30	30	and	and	CCONJ
fcis-2486	30	31	feature	feature	NOUN
fcis-2486	30	32	fusion	fusion	NOUN
fcis-2486	30	33	parts	part	NOUN
fcis-2486	30	34	.	.	PUNCT
fcis-2486	31	1	the	the	DET
fcis-2486	31	2	main	main	ADJ
fcis-2486	31	3	contributions	contribution	NOUN
fcis-2486	31	4	of	of	ADP
fcis-2486	31	5	this	this	DET
fcis-2486	31	6	paper	paper	NOUN
fcis-2486	31	7	are	be	AUX
fcis-2486	31	8	as	as	SCONJ
fcis-2486	31	9	follows	follow	VERB
fcis-2486	31	10	:	:	PUNCT
fcis-2486	31	11	(	(	PUNCT
fcis-2486	31	12	1	1	X
fcis-2486	31	13	)	)	PUNCT
fcis-2486	31	14	we	we	PRON
fcis-2486	31	15	proposes	propose	VERB
fcis-2486	31	16	an	an	DET
fcis-2486	31	17	improved	improved	ADJ
fcis-2486	31	18	lightweight	lightweight	ADJ
fcis-2486	31	19	multiple	multiple	ADJ
fcis-2486	31	20	attention	attention	NOUN
fcis-2486	31	21	image	image	NOUN
fcis-2486	31	22	super	super	ADJ
fcis-2486	31	23	-	-	ADJ
fcis-2486	31	24	resolution	resolution	ADJ
fcis-2486	31	25	reconstruction	reconstruction	NOUN
fcis-2486	31	26	network	network	NOUN
fcis-2486	31	27	(	(	PUNCT
fcis-2486	31	28	lafmn	lafmn	PROPN
fcis-2486	31	29	)	)	PUNCT
fcis-2486	31	30	which	which	PRON
fcis-2486	31	31	reduces	reduce	VERB
fcis-2486	31	32	the	the	DET
fcis-2486	31	33	number	number	NOUN
fcis-2486	31	34	of	of	ADP
fcis-2486	31	35	parameters	parameter	NOUN
fcis-2486	31	36	and	and	CCONJ
fcis-2486	31	37	computational	computational	ADJ
fcis-2486	31	38	effort	effort	NOUN
fcis-2486	31	39	without	without	ADP
fcis-2486	31	40	losing	lose	VERB
fcis-2486	31	41	too	too	ADV
fcis-2486	31	42	much	much	ADJ
fcis-2486	31	43	reconstruction	reconstruction	NOUN
fcis-2486	31	44	performance	performance	NOUN
fcis-2486	31	45	.	.	PUNCT
fcis-2486	32	1	(	(	PUNCT
fcis-2486	32	2	2	2	X
fcis-2486	32	3	)	)	PUNCT
fcis-2486	32	4	we	we	PRON
fcis-2486	32	5	propose	propose	VERB
fcis-2486	32	6	mafb	mafb	NOUN
fcis-2486	32	7	,	,	PUNCT
fcis-2486	32	8	a	a	DET
fcis-2486	32	9	module	module	NOUN
fcis-2486	32	10	that	that	PRON
fcis-2486	32	11	allocates	allocate	VERB
fcis-2486	32	12	computational	computational	ADJ
fcis-2486	32	13	resources	resource	NOUN
fcis-2486	32	14	according	accord	VERB
fcis-2486	32	15	to	to	ADP
fcis-2486	32	16	the	the	DET
fcis-2486	32	17	importance	importance	NOUN
fcis-2486	32	18	of	of	ADP
fcis-2486	32	19	features	feature	NOUN
fcis-2486	32	20	by	by	ADP
fcis-2486	32	21	applying	apply	VERB
fcis-2486	32	22	channel	channel	NOUN
fcis-2486	32	23	attention	attention	NOUN
fcis-2486	32	24	enhancement	enhancement	NOUN
fcis-2486	32	25	and	and	CCONJ
fcis-2486	32	26	spatial	spatial	ADJ
fcis-2486	32	27	attention	attention	NOUN
fcis-2486	32	28	enhancement	enhancement	NOUN
fcis-2486	32	29	to	to	ADP
fcis-2486	32	30	the	the	DET
fcis-2486	32	31	features	feature	NOUN
fcis-2486	32	32	respectively	respectively	ADV
fcis-2486	32	33	.	.	PUNCT
fcis-2486	33	1	the	the	DET
fcis-2486	33	2	synthetic	synthetic	ADJ
fcis-2486	33	3	channel	channel	NOUN
fcis-2486	33	4	attention	attention	NOUN
fcis-2486	33	5	block	block	NOUN
fcis-2486	33	6	(	(	PUNCT
fcis-2486	33	7	scab	scab	NOUN
fcis-2486	33	8	)	)	PUNCT
fcis-2486	33	9	introduces	introduce	VERB
fcis-2486	33	10	two	two	NUM
fcis-2486	33	11	different	different	ADJ
fcis-2486	33	12	pooling	pooling	NOUN
fcis-2486	33	13	,	,	PUNCT
fcis-2486	33	14	which	which	PRON
fcis-2486	33	15	makes	make	VERB
fcis-2486	33	16	full	full	ADJ
fcis-2486	33	17	use	use	NOUN
fcis-2486	33	18	of	of	ADP
fcis-2486	33	19	the	the	DET
fcis-2486	33	20	correlation	correlation	NOUN
fcis-2486	33	21	information	information	NOUN
fcis-2486	33	22	between	between	ADP
fcis-2486	33	23	channels	channel	NOUN
fcis-2486	33	24	.	.	PUNCT
fcis-2486	34	1	at	at	ADP
fcis-2486	34	2	the	the	DET
fcis-2486	34	3	same	same	ADJ
fcis-2486	34	4	time	time	NOUN
fcis-2486	34	5	,	,	PUNCT
fcis-2486	34	6	global	global	ADJ
fcis-2486	34	7	residual	residual	ADJ
fcis-2486	34	8	connectivity	connectivity	NOUN
fcis-2486	34	9	is	be	AUX
fcis-2486	34	10	added	add	VERB
fcis-2486	34	11	to	to	PART
fcis-2486	34	12	fuse	fuse	VERB
fcis-2486	34	13	low	low	ADJ
fcis-2486	34	14	-	-	PUNCT
fcis-2486	34	15	level	level	NOUN
fcis-2486	34	16	and	and	CCONJ
fcis-2486	34	17	high	high	ADJ
fcis-2486	34	18	-	-	PUNCT
fcis-2486	34	19	level	level	NOUN
fcis-2486	34	20	features	feature	VERB
fcis-2486	34	21	more	more	ADV
fcis-2486	34	22	effectively	effectively	ADV
fcis-2486	34	23	.	.	PUNCT
fcis-2486	35	1	(	(	PUNCT
fcis-2486	35	2	3	3	X
fcis-2486	35	3	)	)	PUNCT
fcis-2486	35	4	experimental	experimental	ADJ
fcis-2486	35	5	results	result	NOUN
fcis-2486	35	6	on	on	ADP
fcis-2486	35	7	several	several	ADJ
fcis-2486	35	8	benchmark	benchmark	NOUN
fcis-2486	35	9	datasets	dataset	NOUN
fcis-2486	35	10	show	show	VERB
fcis-2486	35	11	that	that	SCONJ
fcis-2486	35	12	the	the	DET
fcis-2486	35	13	proposed	propose	VERB
fcis-2486	35	14	method	method	NOUN
fcis-2486	35	15	in	in	ADP
fcis-2486	35	16	this	this	DET
fcis-2486	35	17	paper	paper	NOUN
fcis-2486	35	18	achieves	achieve	VERB
fcis-2486	35	19	better	well	ADJ
fcis-2486	35	20	performance	performance	NOUN
fcis-2486	35	21	compared	compare	VERB
fcis-2486	35	22	to	to	ADP
fcis-2486	35	23	other	other	ADJ
fcis-2486	35	24	existing	exist	VERB
fcis-2486	35	25	state	state	NOUN
fcis-2486	35	26	-	-	PUNCT
fcis-2486	35	27	of	of	ADP
fcis-2486	35	28	-	-	PUNCT
fcis-2486	35	29	the	the	DET
fcis-2486	35	30	-	-	PUNCT
fcis-2486	35	31	art	art	NOUN
fcis-2486	35	32	models	model	NOUN
fcis-2486	35	33	.	.	PUNCT
fcis-2486	36	1	2	2	X
fcis-2486	36	2	.	.	X
fcis-2486	36	3	related	relate	VERB
fcis-2486	36	4	work	work	NOUN
fcis-2486	36	5	2.1	2.1	NUM
fcis-2486	36	6	.	.	PUNCT
fcis-2486	37	1	single	single	ADJ
fcis-2486	37	2	image	image	NOUN
fcis-2486	37	3	super	super	ADJ
fcis-2486	37	4	-	-	ADJ
fcis-2486	37	5	resolution	resolution	ADJ
fcis-2486	37	6	reconstruction	reconstruction	NOUN
fcis-2486	37	7	based	base	VERB
fcis-2486	37	8	on	on	ADP
fcis-2486	37	9	deep	deep	ADJ
fcis-2486	37	10	learning	learning	NOUN
fcis-2486	37	11	with	with	ADP
fcis-2486	37	12	dong	dong	PROPN
fcis-2486	37	13	et	et	PROPN
fcis-2486	37	14	al	al	PROPN
fcis-2486	37	15	.	.	PUNCT
fcis-2486	38	1	[	[	X
fcis-2486	38	2	1	1	X
fcis-2486	38	3	]	]	X
fcis-2486	38	4	combining	combine	VERB
fcis-2486	38	5	cnn	cnn	PROPN
fcis-2486	38	6	with	with	ADP
fcis-2486	38	7	sisr	sisr	NOUN
fcis-2486	38	8	,	,	PUNCT
fcis-2486	38	9	more	more	ADJ
fcis-2486	38	10	and	and	CCONJ
fcis-2486	38	11	more	more	ADV
fcis-2486	38	12	neural	neural	ADJ
fcis-2486	38	13	network	network	NOUN
fcis-2486	38	14	-	-	PUNCT
fcis-2486	38	15	based	base	VERB
fcis-2486	38	16	sisr	sisr	NOUN
fcis-2486	38	17	models	model	NOUN
fcis-2486	38	18	have	have	AUX
fcis-2486	38	19	been	be	AUX
fcis-2486	38	20	proposed	propose	VERB
fcis-2486	38	21	.	.	PUNCT
fcis-2486	39	1	srcnn	srcnn	PROPN
fcis-2486	39	2	proposes	propose	VERB
fcis-2486	39	3	a	a	DET
fcis-2486	39	4	simple	simple	ADJ
fcis-2486	39	5	three	three	NUM
fcis-2486	39	6	-	-	PUNCT
fcis-2486	39	7	layer	layer	NOUN
fcis-2486	39	8	network	network	NOUN
fcis-2486	39	9	which	which	PRON
fcis-2486	39	10	first	first	ADJ
fcis-2486	39	11	up	up	ADP
fcis-2486	39	12	-	-	PUNCT
fcis-2486	39	13	samples	sample	VERB
fcis-2486	39	14	the	the	DET
fcis-2486	39	15	input	input	NOUN
fcis-2486	39	16	low	low	ADJ
fcis-2486	39	17	-	-	PUNCT
fcis-2486	39	18	resolution	resolution	NOUN
fcis-2486	39	19	lr	lr	NOUN
fcis-2486	39	20	image	image	NOUN
fcis-2486	39	21	14	14	NUM
fcis-2486	39	22	using	use	VERB
fcis-2486	39	23	a	a	DET
fcis-2486	39	24	bicubic	bicubic	ADJ
fcis-2486	39	25	interpolation	interpolation	NOUN
fcis-2486	39	26	algorithm	algorithm	NOUN
fcis-2486	39	27	to	to	PART
fcis-2486	39	28	obtain	obtain	VERB
fcis-2486	39	29	the	the	DET
fcis-2486	39	30	target	target	NOUN
fcis-2486	39	31	size	size	NOUN
fcis-2486	39	32	image	image	NOUN
fcis-2486	39	33	.	.	PUNCT
fcis-2486	40	1	the	the	DET
fcis-2486	40	2	next	next	ADJ
fcis-2486	40	3	step	step	NOUN
fcis-2486	40	4	is	be	AUX
fcis-2486	40	5	to	to	PART
fcis-2486	40	6	process	process	VERB
fcis-2486	40	7	the	the	DET
fcis-2486	40	8	input	input	NOUN
fcis-2486	40	9	lr	lr	NOUN
fcis-2486	40	10	image	image	NOUN
fcis-2486	40	11	through	through	ADP
fcis-2486	40	12	a	a	DET
fcis-2486	40	13	three	three	NUM
fcis-2486	40	14	-	-	PUNCT
fcis-2486	40	15	layer	layer	NOUN
fcis-2486	40	16	convolutional	convolutional	ADJ
fcis-2486	40	17	network	network	NOUN
fcis-2486	40	18	to	to	PART
fcis-2486	40	19	obtain	obtain	VERB
fcis-2486	40	20	a	a	DET
fcis-2486	40	21	highresolution	highresolution	NOUN
fcis-2486	40	22	sr	sr	PROPN
fcis-2486	40	23	image	image	NOUN
fcis-2486	40	24	,	,	PUNCT
fcis-2486	40	25	with	with	ADP
fcis-2486	40	26	the	the	DET
fcis-2486	40	27	goal	goal	NOUN
fcis-2486	40	28	of	of	ADP
fcis-2486	40	29	making	make	VERB
fcis-2486	40	30	it	it	PRON
fcis-2486	40	31	alike	alike	ADV
fcis-2486	40	32	as	as	ADP
fcis-2486	40	33	possible	possible	ADJ
fcis-2486	40	34	to	to	ADP
fcis-2486	40	35	the	the	DET
fcis-2486	40	36	original	original	ADJ
fcis-2486	40	37	hr	hr	NOUN
fcis-2486	40	38	image	image	NOUN
fcis-2486	40	39	.	.	PUNCT
fcis-2486	41	1	the	the	DET
fcis-2486	41	2	first	first	ADJ
fcis-2486	41	3	part	part	NOUN
fcis-2486	41	4	of	of	ADP
fcis-2486	41	5	the	the	DET
fcis-2486	41	6	network	network	NOUN
fcis-2486	41	7	extracts	extract	VERB
fcis-2486	41	8	multiple	multiple	ADJ
fcis-2486	41	9	patches	patch	NOUN
fcis-2486	41	10	of	of	ADP
fcis-2486	41	11	the	the	DET
fcis-2486	41	12	input	input	NOUN
fcis-2486	41	13	lr	lr	NOUN
fcis-2486	41	14	image	image	NOUN
fcis-2486	41	15	,	,	PUNCT
fcis-2486	41	16	each	each	PRON
fcis-2486	41	17	of	of	ADP
fcis-2486	41	18	which	which	PRON
fcis-2486	41	19	is	be	AUX
fcis-2486	41	20	represented	represent	VERB
fcis-2486	41	21	as	as	ADP
fcis-2486	41	22	a	a	DET
fcis-2486	41	23	multidimensional	multidimensional	ADJ
fcis-2486	41	24	vector	vector	NOUN
fcis-2486	41	25	by	by	ADP
fcis-2486	41	26	the	the	DET
fcis-2486	41	27	convolution	convolution	NOUN
fcis-2486	41	28	operation	operation	NOUN
fcis-2486	41	29	,	,	PUNCT
fcis-2486	41	30	and	and	CCONJ
fcis-2486	41	31	all	all	DET
fcis-2486	41	32	the	the	DET
fcis-2486	41	33	feature	feature	NOUN
fcis-2486	41	34	vectors	vector	NOUN
fcis-2486	41	35	form	form	VERB
fcis-2486	41	36	an	an	DET
fcis-2486	41	37	n1dimensional	n1dimensional	ADJ
fcis-2486	41	38	feature	feature	NOUN
fcis-2486	41	39	mapping	mapping	NOUN
fcis-2486	41	40	matrix	matrix	NOUN
fcis-2486	41	41	,	,	PUNCT
fcis-2486	41	42	and	and	CCONJ
fcis-2486	41	43	the	the	DET
fcis-2486	41	44	process	process	NOUN
fcis-2486	41	45	formula	formula	NOUN
fcis-2486	41	46	is	be	AUX
fcis-2486	41	47	expressed	express	VERB
fcis-2486	41	48	as	as	ADP
fcis-2486	41	49	:	:	PUNCT
fcis-2486	41	50	𝐹1(𝑌	𝐹1(𝑌	ADJ
fcis-2486	41	51	)	)	PUNCT
fcis-2486	41	52	=	=	PUNCT
fcis-2486	41	53	𝑚𝑎𝑥(0	𝑚𝑎𝑥(0	NOUN
fcis-2486	41	54	,	,	PUNCT
fcis-2486	41	55	𝑊1	𝑊1	PROPN
fcis-2486	41	56	∗	∗	VERB
fcis-2486	41	57	𝑌	𝑌	PROPN
fcis-2486	41	58	+	+	CCONJ
fcis-2486	41	59	𝐵1	𝐵1	NOUN
fcis-2486	41	60	)	)	PUNCT
fcis-2486	41	61	(	(	PUNCT
fcis-2486	41	62	1	1	X
fcis-2486	41	63	)	)	PUNCT
fcis-2486	41	64	in	in	ADP
fcis-2486	41	65	the	the	DET
fcis-2486	41	66	second	second	ADJ
fcis-2486	41	67	part	part	NOUN
fcis-2486	41	68	,	,	PUNCT
fcis-2486	41	69	the	the	DET
fcis-2486	41	70	n1	n1	NOUN
fcis-2486	41	71	-	-	PUNCT
fcis-2486	41	72	dimensional	dimensional	ADJ
fcis-2486	41	73	feature	feature	NOUN
fcis-2486	41	74	mapping	mapping	NOUN
fcis-2486	41	75	matrix	matrix	NOUN
fcis-2486	41	76	is	be	AUX
fcis-2486	41	77	non	non	ADJ
fcis-2486	41	78	-	-	ADJ
fcis-2486	41	79	linearly	linearly	ADV
fcis-2486	41	80	mapped	map	VERB
fcis-2486	41	81	by	by	ADP
fcis-2486	41	82	a	a	DET
fcis-2486	41	83	convolution	convolution	NOUN
fcis-2486	41	84	operation	operation	NOUN
fcis-2486	41	85	to	to	PART
fcis-2486	41	86	obtain	obtain	VERB
fcis-2486	41	87	an	an	DET
fcis-2486	41	88	n2	n2	ADJ
fcis-2486	41	89	-	-	PUNCT
fcis-2486	41	90	dimensional	dimensional	ADJ
fcis-2486	41	91	feature	feature	NOUN
fcis-2486	41	92	matrix	matrix	NOUN
fcis-2486	41	93	,	,	PUNCT
fcis-2486	41	94	which	which	PRON
fcis-2486	41	95	is	be	AUX
fcis-2486	41	96	expressed	express	VERB
fcis-2486	41	97	by	by	ADP
fcis-2486	41	98	the	the	DET
fcis-2486	41	99	following	follow	VERB
fcis-2486	41	100	equation	equation	NOUN
fcis-2486	41	101	.	.	PUNCT
fcis-2486	42	1	𝐹2(𝑌	𝐹2(𝑌	NOUN
fcis-2486	42	2	)	)	PUNCT
fcis-2486	43	1	=	=	PUNCT
fcis-2486	43	2	𝑚𝑎𝑥(0	𝑚𝑎𝑥(0	NOUN
fcis-2486	43	3	,	,	PUNCT
fcis-2486	43	4	𝑊2	𝑊2	PROPN
fcis-2486	43	5	∗	∗	NOUN
fcis-2486	43	6	𝐹1(𝑌	𝐹1(𝑌	PROPN
fcis-2486	43	7	)	)	PUNCT
fcis-2486	43	8	+	+	NUM
fcis-2486	43	9	𝐵2	𝐵2	NOUN
fcis-2486	43	10	)	)	PUNCT
fcis-2486	43	11	(	(	PUNCT
fcis-2486	43	12	2	2	X
fcis-2486	43	13	)	)	PUNCT
fcis-2486	43	14	the	the	DET
fcis-2486	43	15	last	last	ADJ
fcis-2486	43	16	step	step	NOUN
fcis-2486	43	17	of	of	ADP
fcis-2486	43	18	the	the	DET
fcis-2486	43	19	reconstruction	reconstruction	NOUN
fcis-2486	43	20	process	process	NOUN
fcis-2486	43	21	is	be	AUX
fcis-2486	43	22	equivalent	equivalent	ADJ
fcis-2486	43	23	to	to	ADP
fcis-2486	43	24	the	the	DET
fcis-2486	43	25	deconvolution	deconvolution	NOUN
fcis-2486	43	26	,	,	PUNCT
fcis-2486	43	27	that	that	ADV
fcis-2486	43	28	is	is	ADV
fcis-2486	43	29	,	,	PUNCT
fcis-2486	43	30	the	the	DET
fcis-2486	43	31	n2	n2	ADJ
fcis-2486	43	32	-	-	PUNCT
fcis-2486	43	33	dimensional	dimensional	ADJ
fcis-2486	43	34	characteristic	characteristic	ADJ
fcis-2486	43	35	matrix	matrix	NOUN
fcis-2486	43	36	is	be	AUX
fcis-2486	43	37	restored	restore	VERB
fcis-2486	43	38	to	to	ADP
fcis-2486	43	39	hr	hr	NOUN
fcis-2486	43	40	image	image	NOUN
fcis-2486	43	41	.	.	PUNCT
fcis-2486	44	1	it	it	PRON
fcis-2486	44	2	can	can	AUX
fcis-2486	44	3	be	be	AUX
fcis-2486	44	4	expressed	express	VERB
fcis-2486	44	5	as	as	SCONJ
fcis-2486	44	6	follows	follow	VERB
fcis-2486	44	7	:	:	PUNCT
fcis-2486	44	8	𝐹(𝑌	𝐹(𝑌	NOUN
fcis-2486	44	9	)	)	PUNCT
fcis-2486	45	1	=	=	SYM
fcis-2486	45	2	𝑊3	𝑊3	NOUN
fcis-2486	45	3	∗	∗	PROPN
fcis-2486	45	4	𝐹2(𝑌	𝐹2(𝑌	PROPN
fcis-2486	45	5	)	)	PUNCT
fcis-2486	46	1	+	+	NUM
fcis-2486	46	2	𝐵3	𝐵3	NOUN
fcis-2486	46	3	(	(	PUNCT
fcis-2486	46	4	3	3	NUM
fcis-2486	46	5	)	)	PUNCT
fcis-2486	46	6	2.2	2.2	NUM
fcis-2486	46	7	.	.	PUNCT
fcis-2486	47	1	image	image	NOUN
fcis-2486	47	2	super	super	NOUN
fcis-2486	47	3	-	-	NOUN
fcis-2486	47	4	resolution	resolution	NOUN
fcis-2486	47	5	based	base	VERB
fcis-2486	47	6	on	on	ADP
fcis-2486	47	7	attention	attention	NOUN
fcis-2486	47	8	mechanism	mechanism	NOUN
fcis-2486	47	9	the	the	DET
fcis-2486	47	10	visual	visual	ADJ
fcis-2486	47	11	attention	attention	NOUN
fcis-2486	47	12	mechanism	mechanism	NOUN
fcis-2486	47	13	is	be	AUX
fcis-2486	47	14	a	a	DET
fcis-2486	47	15	unique	unique	ADJ
fcis-2486	47	16	signal	signal	NOUN
fcis-2486	47	17	processing	processing	NOUN
fcis-2486	47	18	mechanism	mechanism	NOUN
fcis-2486	47	19	of	of	ADP
fcis-2486	47	20	the	the	DET
fcis-2486	47	21	human	human	ADJ
fcis-2486	47	22	brain	brain	NOUN
fcis-2486	47	23	.	.	PUNCT
fcis-2486	48	1	specifically	specifically	ADV
fcis-2486	48	2	,	,	PUNCT
fcis-2486	48	3	by	by	ADP
fcis-2486	48	4	observing	observe	VERB
fcis-2486	48	5	the	the	DET
fcis-2486	48	6	global	global	ADJ
fcis-2486	48	7	image	image	NOUN
fcis-2486	48	8	,	,	PUNCT
fcis-2486	48	9	selecting	select	VERB
fcis-2486	48	10	some	some	DET
fcis-2486	48	11	local	local	ADJ
fcis-2486	48	12	focus	focus	NOUN
fcis-2486	48	13	areas	area	NOUN
fcis-2486	48	14	,	,	PUNCT
fcis-2486	48	15	and	and	CCONJ
fcis-2486	48	16	then	then	ADV
fcis-2486	48	17	paying	pay	VERB
fcis-2486	48	18	more	more	ADJ
fcis-2486	48	19	attention	attention	NOUN
fcis-2486	48	20	to	to	ADP
fcis-2486	48	21	these	these	DET
fcis-2486	48	22	areas	area	NOUN
fcis-2486	48	23	to	to	PART
fcis-2486	48	24	obtain	obtain	VERB
fcis-2486	48	25	more	more	ADV
fcis-2486	48	26	detailed	detailed	ADJ
fcis-2486	48	27	information	information	NOUN
fcis-2486	48	28	and	and	CCONJ
fcis-2486	48	29	suppress	suppress	VERB
fcis-2486	48	30	other	other	ADJ
fcis-2486	48	31	useless	useless	ADJ
fcis-2486	48	32	information	information	NOUN
fcis-2486	48	33	.	.	PUNCT
fcis-2486	49	1	the	the	DET
fcis-2486	49	2	essence	essence	NOUN
fcis-2486	49	3	of	of	ADP
fcis-2486	49	4	it	it	PRON
fcis-2486	49	5	is	be	AUX
fcis-2486	49	6	to	to	PART
fcis-2486	49	7	learn	learn	VERB
fcis-2486	49	8	a	a	DET
fcis-2486	49	9	weight	weight	NOUN
fcis-2486	49	10	distribution	distribution	NOUN
fcis-2486	49	11	for	for	ADP
fcis-2486	49	12	image	image	NOUN
fcis-2486	49	13	features	feature	NOUN
fcis-2486	49	14	which	which	PRON
fcis-2486	49	15	will	will	AUX
fcis-2486	49	16	be	be	AUX
fcis-2486	49	17	applied	apply	VERB
fcis-2486	49	18	to	to	ADP
fcis-2486	49	19	the	the	DET
fcis-2486	49	20	original	original	ADJ
fcis-2486	49	21	features	feature	NOUN
fcis-2486	49	22	to	to	PART
fcis-2486	49	23	provide	provide	VERB
fcis-2486	49	24	different	different	ADJ
fcis-2486	49	25	feature	feature	NOUN
fcis-2486	49	26	influences	influence	NOUN
fcis-2486	49	27	for	for	ADP
fcis-2486	49	28	image	image	NOUN
fcis-2486	49	29	processing	processing	NOUN
fcis-2486	49	30	tasks	task	NOUN
fcis-2486	49	31	such	such	ADJ
fcis-2486	49	32	as	as	ADP
fcis-2486	49	33	image	image	NOUN
fcis-2486	49	34	classification	classification	NOUN
fcis-2486	49	35	,	,	PUNCT
fcis-2486	49	36	image	image	NOUN
fcis-2486	49	37	recognition	recognition	NOUN
fcis-2486	49	38	,	,	PUNCT
fcis-2486	49	39	etc	etc	X
fcis-2486	49	40	.	.	X
fcis-2486	49	41	considering	consider	VERB
fcis-2486	49	42	that	that	SCONJ
fcis-2486	49	43	the	the	DET
fcis-2486	49	44	feature	feature	NOUN
fcis-2486	49	45	importance	importance	NOUN
fcis-2486	49	46	extracted	extract	VERB
fcis-2486	49	47	by	by	ADP
fcis-2486	49	48	different	different	ADJ
fcis-2486	49	49	convolution	convolution	NOUN
fcis-2486	49	50	kernels	kernel	NOUN
fcis-2486	49	51	is	be	AUX
fcis-2486	49	52	different	different	ADJ
fcis-2486	49	53	,	,	PUNCT
fcis-2486	49	54	senet	senet	NOUN
fcis-2486	49	55	[	[	X
fcis-2486	49	56	10	10	NUM
fcis-2486	49	57	]	]	X
fcis-2486	49	58	network	network	NOUN
fcis-2486	49	59	introduce	introduce	VERB
fcis-2486	49	60	the	the	DET
fcis-2486	49	61	concept	concept	NOUN
fcis-2486	49	62	of	of	ADP
fcis-2486	49	63	channel	channel	NOUN
fcis-2486	49	64	attention	attention	NOUN
fcis-2486	49	65	to	to	ADP
fcis-2486	49	66	deep	deep	ADJ
fcis-2486	49	67	neural	neural	ADJ
fcis-2486	49	68	networks	network	NOUN
fcis-2486	49	69	initially	initially	ADV
fcis-2486	49	70	.	.	PUNCT
fcis-2486	50	1	it	it	PRON
fcis-2486	50	2	mainly	mainly	ADV
fcis-2486	50	3	enhanced	enhance	VERB
fcis-2486	50	4	the	the	DET
fcis-2486	50	5	valuable	valuable	ADJ
fcis-2486	50	6	features	feature	NOUN
fcis-2486	50	7	by	by	ADP
fcis-2486	50	8	learning	learn	VERB
fcis-2486	50	9	the	the	DET
fcis-2486	50	10	weight	weight	NOUN
fcis-2486	50	11	value	value	NOUN
fcis-2486	50	12	of	of	ADP
fcis-2486	50	13	each	each	DET
fcis-2486	50	14	channel	channel	NOUN
fcis-2486	50	15	,	,	PUNCT
fcis-2486	50	16	so	so	SCONJ
fcis-2486	50	17	as	as	SCONJ
fcis-2486	50	18	to	to	PART
fcis-2486	50	19	enhance	enhance	VERB
fcis-2486	50	20	the	the	DET
fcis-2486	50	21	learning	learning	NOUN
fcis-2486	50	22	capacity	capacity	NOUN
fcis-2486	50	23	of	of	ADP
fcis-2486	50	24	the	the	DET
fcis-2486	50	25	network	network	NOUN
fcis-2486	50	26	algorithm	algorithm	NOUN
fcis-2486	50	27	effectively	effectively	ADV
fcis-2486	50	28	while	while	SCONJ
fcis-2486	50	29	the	the	DET
fcis-2486	50	30	computing	computing	NOUN
fcis-2486	50	31	resources	resource	NOUN
fcis-2486	50	32	are	be	AUX
fcis-2486	50	33	limited	limit	VERB
fcis-2486	50	34	.	.	PUNCT
fcis-2486	51	1	simultaneously	simultaneously	ADV
fcis-2486	51	2	,	,	PUNCT
fcis-2486	51	3	it	it	PRON
fcis-2486	51	4	can	can	AUX
fcis-2486	51	5	be	be	AUX
fcis-2486	51	6	used	use	VERB
fcis-2486	51	7	for	for	ADP
fcis-2486	51	8	designing	design	VERB
fcis-2486	51	9	a	a	DET
fcis-2486	51	10	lightweight	lightweight	ADJ
fcis-2486	51	11	network	network	NOUN
fcis-2486	51	12	architecture	architecture	NOUN
fcis-2486	51	13	.	.	PUNCT
fcis-2486	52	1	non	non	ADJ
fcis-2486	52	2	-	-	ADJ
fcis-2486	52	3	local	local	ADJ
fcis-2486	52	4	[	[	X
fcis-2486	52	5	11	11	NUM
fcis-2486	52	6	]	]	PUNCT
fcis-2486	52	7	proposed	propose	VERB
fcis-2486	52	8	by	by	ADP
fcis-2486	52	9	liu	liu	PROPN
fcis-2486	52	10	et	et	PROPN
fcis-2486	52	11	al	al	PROPN
fcis-2486	52	12	.	.	PROPN
fcis-2486	52	13	aimed	aim	VERB
fcis-2486	52	14	to	to	PART
fcis-2486	52	15	generate	generate	VERB
fcis-2486	52	16	a	a	DET
fcis-2486	52	17	broad	broad	ADJ
fcis-2486	52	18	range	range	NOUN
fcis-2486	52	19	of	of	ADP
fcis-2486	52	20	attention	attention	NOUN
fcis-2486	52	21	maps	map	NOUN
fcis-2486	52	22	by	by	ADP
fcis-2486	52	23	calculating	calculate	VERB
fcis-2486	52	24	each	each	DET
fcis-2486	52	25	spatial	spatial	ADJ
fcis-2486	52	26	point	point	NOUN
fcis-2486	52	27	's	's	PART
fcis-2486	52	28	correlation	correlation	NOUN
fcis-2486	52	29	matrix	matrix	NOUN
fcis-2486	52	30	in	in	ADP
fcis-2486	52	31	the	the	DET
fcis-2486	52	32	feature	feature	NOUN
fcis-2486	52	33	map	map	NOUN
fcis-2486	52	34	,	,	PUNCT
fcis-2486	52	35	and	and	CCONJ
fcis-2486	52	36	then	then	ADV
fcis-2486	52	37	uses	use	VERB
fcis-2486	52	38	them	they	PRON
fcis-2486	52	39	to	to	PART
fcis-2486	52	40	instruct	instruct	VERB
fcis-2486	52	41	the	the	DET
fcis-2486	52	42	aggregation	aggregation	NOUN
fcis-2486	52	43	of	of	ADP
fcis-2486	52	44	intensive	intensive	ADJ
fcis-2486	52	45	context	context	NOUN
fcis-2486	52	46	information	information	NOUN
fcis-2486	52	47	.	.	PUNCT
fcis-2486	53	1	yet	yet	ADV
fcis-2486	53	2	,	,	PUNCT
fcis-2486	53	3	due	due	ADP
fcis-2486	53	4	to	to	ADP
fcis-2486	53	5	its	its	PRON
fcis-2486	53	6	large	large	ADJ
fcis-2486	53	7	calculation	calculation	NOUN
fcis-2486	53	8	,	,	PUNCT
fcis-2486	53	9	it	it	PRON
fcis-2486	53	10	is	be	AUX
fcis-2486	53	11	difficult	difficult	ADJ
fcis-2486	53	12	to	to	PART
fcis-2486	53	13	apply	apply	VERB
fcis-2486	53	14	in	in	ADP
fcis-2486	53	15	real	real	ADJ
fcis-2486	53	16	life	life	NOUN
fcis-2486	53	17	.	.	PUNCT
fcis-2486	54	1	rcan	rcan	PROPN
fcis-2486	55	1	[	[	X
fcis-2486	55	2	9	9	NUM
fcis-2486	55	3	]	]	X
fcis-2486	55	4	has	have	AUX
fcis-2486	55	5	significantly	significantly	ADV
fcis-2486	55	6	improved	improve	VERB
fcis-2486	55	7	the	the	DET
fcis-2486	55	8	reconstruction	reconstruction	NOUN
fcis-2486	55	9	effect	effect	NOUN
fcis-2486	55	10	via	via	ADP
fcis-2486	55	11	the	the	DET
fcis-2486	55	12	use	use	NOUN
fcis-2486	55	13	of	of	ADP
fcis-2486	55	14	channel	channel	NOUN
fcis-2486	55	15	attention	attention	NOUN
fcis-2486	55	16	in	in	ADP
fcis-2486	55	17	the	the	DET
fcis-2486	55	18	field	field	NOUN
fcis-2486	55	19	of	of	ADP
fcis-2486	55	20	sisr	sisr	NOUN
fcis-2486	55	21	.	.	PUNCT
fcis-2486	56	1	3	3	X
fcis-2486	56	2	.	.	X
fcis-2486	56	3	the	the	DET
fcis-2486	56	4	proposed	propose	VERB
fcis-2486	56	5	method	method	NOUN
fcis-2486	56	6	3.1	3.1	NUM
fcis-2486	56	7	.	.	PUNCT
fcis-2486	56	8	framework	framework	NOUN
fcis-2486	56	9	of	of	ADP
fcis-2486	56	10	the	the	DET
fcis-2486	56	11	proposed	propose	VERB
fcis-2486	56	12	model	model	NOUN
fcis-2486	56	13	in	in	ADP
fcis-2486	56	14	this	this	DET
fcis-2486	56	15	part	part	NOUN
fcis-2486	56	16	,	,	PUNCT
fcis-2486	56	17	we	we	PRON
fcis-2486	56	18	despict	despict	VERB
fcis-2486	56	19	the	the	DET
fcis-2486	56	20	details	detail	NOUN
fcis-2486	56	21	of	of	ADP
fcis-2486	56	22	the	the	DET
fcis-2486	56	23	proposed	propose	VERB
fcis-2486	56	24	model	model	NOUN
fcis-2486	56	25	in	in	ADP
fcis-2486	56	26	figure	figure	NOUN
fcis-2486	56	27	1	1	NUM
fcis-2486	56	28	.	.	PUNCT
fcis-2486	57	1	first	first	ADV
fcis-2486	57	2	,	,	PUNCT
fcis-2486	57	3	a	a	DET
fcis-2486	57	4	convolutional	convolutional	ADJ
fcis-2486	57	5	layer	layer	NOUN
fcis-2486	57	6	is	be	AUX
fcis-2486	57	7	used	use	VERB
fcis-2486	57	8	to	to	PART
fcis-2486	57	9	extract	extract	VERB
fcis-2486	57	10	the	the	DET
fcis-2486	57	11	shallow	shallow	ADJ
fcis-2486	57	12	features	feature	NOUN
fcis-2486	57	13	of	of	ADP
fcis-2486	57	14	the	the	DET
fcis-2486	57	15	lr	lr	NOUN
fcis-2486	57	16	image	image	NOUN
fcis-2486	57	17	,	,	PUNCT
fcis-2486	57	18	and	and	CCONJ
fcis-2486	57	19	then	then	ADV
fcis-2486	57	20	further	further	ADJ
fcis-2486	57	21	feature	feature	NOUN
fcis-2486	57	22	extraction	extraction	NOUN
fcis-2486	57	23	is	be	AUX
fcis-2486	57	24	performed	perform	VERB
fcis-2486	57	25	by	by	ADP
fcis-2486	57	26	multiple	multiple	ADJ
fcis-2486	57	27	stacked	stack	VERB
fcis-2486	57	28	mafbs	mafbs	PROPN
fcis-2486	57	29	.	.	PUNCT
fcis-2486	58	1	finally	finally	ADV
fcis-2486	58	2	,	,	PUNCT
fcis-2486	58	3	the	the	DET
fcis-2486	58	4	hr	hr	NOUN
fcis-2486	58	5	image	image	NOUN
fcis-2486	58	6	is	be	AUX
fcis-2486	58	7	reconstructed	reconstruct	VERB
fcis-2486	58	8	by	by	ADP
fcis-2486	58	9	an	an	DET
fcis-2486	58	10	upsampling	upsampling	NOUN
fcis-2486	58	11	module	module	NOUN
fcis-2486	58	12	.	.	PUNCT
fcis-2486	59	1	the	the	DET
fcis-2486	59	2	convolution	convolution	NOUN
fcis-2486	59	3	is	be	AUX
fcis-2486	59	4	fused	fuse	VERB
fcis-2486	59	5	and	and	CCONJ
fcis-2486	59	6	added	add	VERB
fcis-2486	59	7	with	with	ADP
fcis-2486	59	8	the	the	DET
fcis-2486	59	9	shallow	shallow	ADJ
fcis-2486	59	10	features	feature	NOUN
fcis-2486	59	11	to	to	PART
fcis-2486	59	12	obtain	obtain	VERB
fcis-2486	59	13	the	the	DET
fcis-2486	59	14	reconstructed	reconstructed	ADJ
fcis-2486	59	15	hr	hr	NOUN
fcis-2486	59	16	image	image	NOUN
fcis-2486	59	17	.	.	PUNCT
fcis-2486	60	1	furthermore	furthermore	ADV
fcis-2486	60	2	,	,	PUNCT
fcis-2486	60	3	we	we	PRON
fcis-2486	60	4	add	add	VERB
fcis-2486	60	5	the	the	DET
fcis-2486	60	6	features	feature	NOUN
fcis-2486	60	7	of	of	ADP
fcis-2486	60	8	the	the	DET
fcis-2486	60	9	first	first	ADJ
fcis-2486	60	10	layer	layer	NOUN
fcis-2486	60	11	and	and	CCONJ
fcis-2486	60	12	the	the	DET
fcis-2486	60	13	features	feature	NOUN
fcis-2486	60	14	of	of	ADP
fcis-2486	60	15	the	the	DET
fcis-2486	60	16	last	last	ADJ
fcis-2486	60	17	layer	layer	NOUN
fcis-2486	60	18	through	through	ADP
fcis-2486	60	19	residual	residual	ADJ
fcis-2486	60	20	connection	connection	NOUN
fcis-2486	60	21	,	,	PUNCT
fcis-2486	60	22	and	and	CCONJ
fcis-2486	60	23	fuse	fuse	VERB
fcis-2486	60	24	the	the	DET
fcis-2486	60	25	features	feature	NOUN
fcis-2486	60	26	of	of	ADP
fcis-2486	60	27	the	the	DET
fcis-2486	60	28	shallow	shallow	ADJ
fcis-2486	60	29	layer	layer	NOUN
fcis-2486	60	30	and	and	CCONJ
fcis-2486	60	31	the	the	DET
fcis-2486	60	32	deep	deep	ADJ
fcis-2486	60	33	layer	layer	NOUN
fcis-2486	60	34	,	,	PUNCT
fcis-2486	60	35	so	so	SCONJ
fcis-2486	60	36	as	as	SCONJ
fcis-2486	60	37	to	to	PART
fcis-2486	60	38	maintain	maintain	VERB
fcis-2486	60	39	the	the	DET
fcis-2486	60	40	influence	influence	NOUN
fcis-2486	60	41	of	of	ADP
fcis-2486	60	42	the	the	DET
fcis-2486	60	43	features	feature	NOUN
fcis-2486	60	44	of	of	ADP
fcis-2486	60	45	shallow	shallow	ADJ
fcis-2486	60	46	layer	layer	NOUN
fcis-2486	60	47	on	on	ADP
fcis-2486	60	48	the	the	DET
fcis-2486	60	49	deep	deep	ADJ
fcis-2486	60	50	layer	layer	NOUN
fcis-2486	60	51	to	to	ADP
fcis-2486	60	52	the	the	DET
fcis-2486	60	53	greatest	great	ADJ
fcis-2486	60	54	extent	extent	NOUN
fcis-2486	60	55	.	.	PUNCT
fcis-2486	61	1	as	as	SCONJ
fcis-2486	61	2	shown	show	VERB
fcis-2486	61	3	in	in	ADP
fcis-2486	61	4	fig	fig	NOUN
fcis-2486	61	5	.	.	PUNCT
fcis-2486	62	1	1	1	NUM
fcis-2486	62	2	,	,	PUNCT
fcis-2486	62	3	considering	consider	VERB
fcis-2486	62	4	the	the	DET
fcis-2486	62	5	lightweight	lightweight	ADJ
fcis-2486	62	6	design	design	NOUN
fcis-2486	62	7	of	of	ADP
fcis-2486	62	8	the	the	DET
fcis-2486	62	9	model	model	NOUN
fcis-2486	62	10	,	,	PUNCT
fcis-2486	62	11	this	this	DET
fcis-2486	62	12	part	part	NOUN
fcis-2486	62	13	only	only	ADV
fcis-2486	62	14	consists	consist	VERB
fcis-2486	62	15	of	of	ADP
fcis-2486	62	16	a	a	DET
fcis-2486	62	17	simple	simple	ADJ
fcis-2486	62	18	3×3	3×3	NUM
fcis-2486	62	19	convolution	convolution	NOUN
fcis-2486	62	20	.	.	PUNCT
fcis-2486	63	1	the	the	DET
fcis-2486	63	2	process	process	NOUN
fcis-2486	63	3	can	can	AUX
fcis-2486	63	4	be	be	AUX
fcis-2486	63	5	expressed	express	VERB
fcis-2486	63	6	as	as	ADP
fcis-2486	63	7	:	:	PUNCT
fcis-2486	63	8	𝑥0	𝑥0	PROPN
fcis-2486	63	9	=	=	SYM
fcis-2486	63	10	𝑓𝐹𝐸(𝐼𝐿𝑅	𝑓𝐹𝐸(𝐼𝐿𝑅	PROPN
fcis-2486	63	11	)	)	PUNCT
fcis-2486	63	12	(	(	PUNCT
fcis-2486	63	13	4	4	X
fcis-2486	63	14	)	)	PUNCT
fcis-2486	63	15	where	where	SCONJ
fcis-2486	63	16	ffe(∙	ffe(∙	NOUN
fcis-2486	63	17	)	)	PUNCT
fcis-2486	63	18	represents	represent	VERB
fcis-2486	63	19	the	the	DET
fcis-2486	63	20	3×3	3×3	NUM
fcis-2486	63	21	convolution	convolution	NOUN
fcis-2486	63	22	operation	operation	NOUN
fcis-2486	63	23	for	for	ADP
fcis-2486	63	24	extracting	extract	VERB
fcis-2486	63	25	features	feature	NOUN
fcis-2486	63	26	from	from	ADP
fcis-2486	63	27	the	the	DET
fcis-2486	63	28	input	input	NOUN
fcis-2486	63	29	lr	lr	NOUN
fcis-2486	63	30	image	image	NOUN
fcis-2486	63	31	and	and	CCONJ
fcis-2486	63	32	x0	x0	PROPN
fcis-2486	63	33	is	be	AUX
fcis-2486	63	34	the	the	DET
fcis-2486	63	35	output	output	NOUN
fcis-2486	63	36	of	of	ADP
fcis-2486	63	37	this	this	DET
fcis-2486	63	38	layer	layer	NOUN
fcis-2486	63	39	.	.	PUNCT
fcis-2486	64	1	then	then	ADV
fcis-2486	64	2	,	,	PUNCT
fcis-2486	64	3	we	we	PRON
fcis-2486	64	4	use	use	VERB
fcis-2486	64	5	a	a	DET
fcis-2486	64	6	nonlinear	nonlinear	ADJ
fcis-2486	64	7	mapping	mapping	NOUN
fcis-2486	64	8	module	module	NOUN
fcis-2486	64	9	consisting	consist	VERB
fcis-2486	64	10	of	of	ADP
fcis-2486	64	11	16	16	NUM
fcis-2486	64	12	stacked	stack	VERB
fcis-2486	64	13	mafbs	mafbs	PRON
fcis-2486	64	14	to	to	PART
fcis-2486	64	15	generate	generate	VERB
fcis-2486	64	16	new	new	ADJ
fcis-2486	64	17	feature	feature	NOUN
fcis-2486	64	18	representations	representation	NOUN
fcis-2486	64	19	,	,	PUNCT
fcis-2486	64	20	denoted	denote	VERB
fcis-2486	64	21	as	as	ADP
fcis-2486	64	22	:	:	PUNCT
fcis-2486	64	23	𝑥𝑛	𝑥𝑛	PROPN
fcis-2486	64	24	=	=	PUNCT
fcis-2486	64	25	𝑓𝑀𝐴𝐹𝐵	𝑓𝑀𝐴𝐹𝐵	PUNCT
fcis-2486	64	26	𝑛	𝑛	PRON
fcis-2486	64	27	(	(	PUNCT
fcis-2486	64	28	𝑓𝑀𝐴𝐹𝐵	𝑓𝑀𝐴𝐹𝐵	PROPN
fcis-2486	64	29	𝑛−1	𝑛−1	PROPN
fcis-2486	64	30	(	(	PUNCT
fcis-2486	64	31	.	.	PUNCT
fcis-2486	64	32	.	.	PUNCT
fcis-2486	64	33	.	.	PUNCT
fcis-2486	65	1	𝑓𝑀𝐴𝐹𝐵	𝑓𝑀𝐴𝐹𝐵	ADJ
fcis-2486	65	2	0	0	NUM
fcis-2486	65	3	(	(	PUNCT
fcis-2486	65	4	𝑥0	𝑥0	NOUN
fcis-2486	65	5	)	)	PUNCT
fcis-2486	65	6	.	.	PUNCT
fcis-2486	65	7	.	.	PUNCT
fcis-2486	65	8	.	.	PUNCT
fcis-2486	65	9	)	)	PUNCT
fcis-2486	65	10	)	)	PUNCT
fcis-2486	66	1	(	(	PUNCT
fcis-2486	66	2	5	5	X
fcis-2486	66	3	)	)	PUNCT
fcis-2486	66	4	where	where	SCONJ
fcis-2486	66	5	xn	xn	PROPN
fcis-2486	66	6	represents	represent	VERB
fcis-2486	66	7	the	the	DET
fcis-2486	66	8	the	the	DET
fcis-2486	66	9	n	n	ADV
fcis-2486	66	10	-	-	PUNCT
fcis-2486	66	11	th	th	X
fcis-2486	66	12	mafb	mafb	NOUN
fcis-2486	66	13	’s	’s	PART
fcis-2486	66	14	output	output	NOUN
fcis-2486	66	15	.	.	PUNCT
fcis-2486	67	1	finally	finally	ADV
fcis-2486	67	2	,	,	PUNCT
fcis-2486	67	3	the	the	DET
fcis-2486	67	4	output	output	NOUN
fcis-2486	67	5	feature	feature	NOUN
fcis-2486	67	6	map	map	NOUN
fcis-2486	67	7	x0	x0	PROPN
fcis-2486	67	8	and	and	CCONJ
fcis-2486	67	9	xn	xn	PROPN
fcis-2486	67	10	is	be	AUX
fcis-2486	67	11	used	use	VERB
fcis-2486	67	12	as	as	ADP
fcis-2486	67	13	the	the	DET
fcis-2486	67	14	input	input	NOUN
fcis-2486	67	15	of	of	ADP
fcis-2486	67	16	the	the	DET
fcis-2486	67	17	reconstruction	reconstruction	NOUN
fcis-2486	67	18	module	module	NOUN
fcis-2486	67	19	,	,	PUNCT
fcis-2486	67	20	and	and	CCONJ
fcis-2486	67	21	further	further	ADJ
fcis-2486	67	22	feature	feature	NOUN
fcis-2486	67	23	fusion	fusion	NOUN
fcis-2486	67	24	is	be	AUX
fcis-2486	67	25	performed	perform	VERB
fcis-2486	67	26	by	by	ADP
fcis-2486	67	27	a	a	DET
fcis-2486	67	28	3×3	3×3	NUM
fcis-2486	67	29	convolution	convolution	NOUN
fcis-2486	67	30	after	after	ADP
fcis-2486	67	31	the	the	DET
fcis-2486	67	32	reconstruction	reconstruction	NOUN
fcis-2486	67	33	module	module	NOUN
fcis-2486	67	34	additionally	additionally	ADV
fcis-2486	67	35	,	,	PUNCT
fcis-2486	67	36	we	we	PRON
fcis-2486	67	37	add	add	VERB
fcis-2486	67	38	a	a	DET
fcis-2486	67	39	global	global	ADJ
fcis-2486	67	40	residual	residual	ADJ
fcis-2486	67	41	connection	connection	NOUN
fcis-2486	67	42	in	in	ADP
fcis-2486	67	43	which	which	PRON
fcis-2486	67	44	performs	perform	VERB
fcis-2486	67	45	bilinear	bilinear	ADJ
fcis-2486	67	46	interpolation	interpolation	NOUN
fcis-2486	67	47	on	on	ADP
fcis-2486	67	48	the	the	DET
fcis-2486	67	49	input	input	NOUN
fcis-2486	67	50	,	,	PUNCT
fcis-2486	67	51	adding	add	VERB
fcis-2486	67	52	its	its	PRON
fcis-2486	67	53	output	output	NOUN
fcis-2486	67	54	to	to	ADP
fcis-2486	67	55	the	the	DET
fcis-2486	67	56	output	output	NOUN
fcis-2486	67	57	of	of	ADP
fcis-2486	67	58	the	the	DET
fcis-2486	67	59	reconstruction	reconstruction	NOUN
fcis-2486	67	60	module	module	NOUN
fcis-2486	67	61	.	.	PUNCT
fcis-2486	68	1	hr	hr	NOUN
fcis-2486	68	2	maps	map	NOUN
fcis-2486	68	3	that	that	PRON
fcis-2486	68	4	upsample	upsample	VERB
fcis-2486	68	5	the	the	DET
fcis-2486	68	6	features	feature	NOUN
fcis-2486	68	7	into	into	ADP
fcis-2486	68	8	the	the	DET
fcis-2486	68	9	target	target	NOUN
fcis-2486	68	10	size	size	NOUN
fcis-2486	68	11	.	.	PUNCT
fcis-2486	69	1	finally	finally	ADV
fcis-2486	69	2	,	,	PUNCT
fcis-2486	69	3	we	we	PRON
fcis-2486	69	4	will	will	AUX
fcis-2486	69	5	get	get	VERB
fcis-2486	69	6	:	:	PUNCT
fcis-2486	69	7	𝐼𝑆𝑅	𝐼𝑆𝑅	PROPN
fcis-2486	69	8	=	=	SYM
fcis-2486	69	9	𝑓𝐹𝑢𝑠𝑖𝑜𝑛(𝑆(𝑥0	𝑓𝐹𝑢𝑠𝑖𝑜𝑛(𝑆(𝑥0	ADJ
fcis-2486	69	10	+	+	NUM
fcis-2486	69	11	𝑥𝑛	𝑥𝑛	NOUN
fcis-2486	69	12	)	)	PUNCT
fcis-2486	69	13	)	)	PUNCT
fcis-2486	70	1	+	+	CCONJ
fcis-2486	70	2	𝑓𝑈𝑃(𝐼𝐿𝑅	𝑓𝑈𝑃(𝐼𝐿𝑅	X
fcis-2486	70	3	)	)	PUNCT
fcis-2486	70	4	(	(	PUNCT
fcis-2486	70	5	6	6	NUM
fcis-2486	70	6	)	)	PUNCT
fcis-2486	70	7	where	where	SCONJ
fcis-2486	70	8	𝑆(∙	𝑆(∙	ADV
fcis-2486	70	9	)	)	PUNCT
fcis-2486	70	10	denotes	denote	VERB
fcis-2486	70	11	the	the	DET
fcis-2486	70	12	operation	operation	NOUN
fcis-2486	70	13	of	of	ADP
fcis-2486	70	14	the	the	DET
fcis-2486	70	15	upsampling	upsampling	NOUN
fcis-2486	70	16	module	module	NOUN
fcis-2486	70	17	,	,	PUNCT
fcis-2486	70	18	ffusion	ffusion	NOUN
fcis-2486	70	19	(	(	PUNCT
fcis-2486	70	20	·	·	PUNCT
fcis-2486	70	21	)	)	PUNCT
fcis-2486	70	22	denotes	denote	VERB
fcis-2486	70	23	3×3	3×3	NUM
fcis-2486	70	24	convolution	convolution	NOUN
fcis-2486	70	25	,	,	PUNCT
fcis-2486	70	26	fup	fup	X
fcis-2486	70	27	(	(	PUNCT
fcis-2486	70	28	·	·	PUNCT
fcis-2486	70	29	)	)	PUNCT
fcis-2486	70	30	denotes	denote	NOUN
fcis-2486	70	31	interpolated	interpolate	VERB
fcis-2486	70	32	upsampling	upsampling	NOUN
fcis-2486	70	33	,	,	PUNCT
fcis-2486	70	34	and	and	CCONJ
fcis-2486	70	35	isr	isr	NOUN
fcis-2486	70	36	is	be	AUX
fcis-2486	70	37	the	the	DET
fcis-2486	70	38	final	final	ADJ
fcis-2486	70	39	output	output	NOUN
fcis-2486	70	40	of	of	ADP
fcis-2486	70	41	the	the	DET
fcis-2486	70	42	network	network	NOUN
fcis-2486	70	43	.	.	PUNCT
fcis-2486	71	1	fig.1	fig.1	PROPN
fcis-2486	71	2	the	the	DET
fcis-2486	71	3	proposed	propose	VERB
fcis-2486	71	4	lightweight	lightweight	ADJ
fcis-2486	71	5	multi	multi	ADJ
fcis-2486	71	6	-	-	ADJ
fcis-2486	71	7	attention	attention	ADJ
fcis-2486	71	8	fusion	fusion	NOUN
fcis-2486	71	9	network	network	NOUN
fcis-2486	71	10	’s	’s	PART
fcis-2486	71	11	structure	structure	NOUN
fcis-2486	71	12	3.2	3.2	NUM
fcis-2486	71	13	.	.	PUNCT
fcis-2486	72	1	multi	multi	ADJ
fcis-2486	72	2	-	-	ADJ
fcis-2486	72	3	attention	attention	ADJ
fcis-2486	72	4	fusion	fusion	NOUN
fcis-2486	72	5	block	block	NOUN
fcis-2486	72	6	inspired	inspire	VERB
fcis-2486	72	7	by	by	ADP
fcis-2486	72	8	scnet	scnet	NOUN
fcis-2486	73	1	[	[	X
fcis-2486	73	2	12	12	NUM
fcis-2486	73	3	]	]	PUNCT
fcis-2486	73	4	,	,	PUNCT
fcis-2486	73	5	its	its	PRON
fcis-2486	73	6	structure	structure	NOUN
fcis-2486	73	7	is	be	AUX
fcis-2486	73	8	improved	improve	VERB
fcis-2486	73	9	in	in	ADP
fcis-2486	73	10	this	this	DET
fcis-2486	73	11	paper	paper	NOUN
fcis-2486	73	12	.	.	PUNCT
fcis-2486	74	1	figure	figure	NOUN
fcis-2486	74	2	2	2	NUM
fcis-2486	74	3	shows	show	VERB
fcis-2486	74	4	that	that	SCONJ
fcis-2486	74	5	the	the	DET
fcis-2486	74	6	mafb	mafb	NOUN
fcis-2486	74	7	consists	consist	VERB
fcis-2486	74	8	of	of	ADP
fcis-2486	74	9	two	two	NUM
fcis-2486	74	10	parts	part	NOUN
fcis-2486	74	11	:	:	PUNCT
fcis-2486	74	12	the	the	DET
fcis-2486	74	13	upper	upper	ADJ
fcis-2486	74	14	layer	layer	NOUN
fcis-2486	74	15	performs	perform	VERB
fcis-2486	74	16	higher	high	ADJ
fcis-2486	74	17	-	-	PUNCT
fcis-2486	74	18	level	level	NOUN
fcis-2486	74	19	feature	feature	NOUN
fcis-2486	74	20	operations	operation	NOUN
fcis-2486	74	21	,	,	PUNCT
fcis-2486	74	22	that	that	ADV
fcis-2486	74	23	is	is	ADV
fcis-2486	74	24	,	,	PUNCT
fcis-2486	74	25	adding	add	VERB
fcis-2486	74	26	weight	weight	NOUN
fcis-2486	74	27	distribution	distribution	NOUN
fcis-2486	74	28	to	to	ADP
fcis-2486	74	29	the	the	DET
fcis-2486	74	30	features	feature	NOUN
fcis-2486	74	31	.	.	PUNCT
fcis-2486	75	1	and	and	CCONJ
fcis-2486	75	2	another	another	DET
fcis-2486	75	3	layer	layer	NOUN
fcis-2486	75	4	is	be	AUX
fcis-2486	75	5	used	use	VERB
fcis-2486	75	6	to	to	PART
fcis-2486	75	7	remain	remain	VERB
fcis-2486	75	8	the	the	DET
fcis-2486	75	9	primitive	primitive	ADJ
fcis-2486	75	10	information	information	NOUN
fcis-2486	75	11	.	.	PUNCT
fcis-2486	76	1	we	we	PRON
fcis-2486	76	2	adopt	adopt	VERB
fcis-2486	76	3	two	two	NUM
fcis-2486	76	4	attention	attention	NOUN
fcis-2486	76	5	modules	module	NOUN
fcis-2486	76	6	,	,	PUNCT
fcis-2486	76	7	namely	namely	ADV
fcis-2486	76	8	scab	scab	NOUN
fcis-2486	76	9	and	and	CCONJ
fcis-2486	76	10	modified	modify	VERB
fcis-2486	76	11	spatial	spatial	ADJ
fcis-2486	76	12	attention	attention	NOUN
fcis-2486	76	13	block	block	NOUN
fcis-2486	76	14	(	(	PUNCT
fcis-2486	76	15	msab	msab	NOUN
fcis-2486	76	16	)	)	PUNCT
fcis-2486	76	17	,	,	PUNCT
fcis-2486	76	18	respectively	respectively	ADV
fcis-2486	76	19	,	,	PUNCT
fcis-2486	76	20	in	in	ADP
fcis-2486	76	21	the	the	DET
fcis-2486	76	22	upper	upper	ADJ
fcis-2486	76	23	layer	layer	NOUN
fcis-2486	76	24	.	.	PUNCT
fcis-2486	77	1	fig	fig	NOUN
fcis-2486	77	2	.	.	PUNCT
fcis-2486	78	1	2	2	NUM
fcis-2486	78	2	multi	multi	ADJ
fcis-2486	78	3	-	-	ADJ
fcis-2486	78	4	attention	attention	ADJ
fcis-2486	78	5	fusion	fusion	NOUN
fcis-2486	78	6	block	block	NOUN
fcis-2486	78	7	the	the	DET
fcis-2486	78	8	xn−1	xn−1	PROPN
fcis-2486	78	9	and	and	CCONJ
fcis-2486	78	10	xn	xn	PROPN
fcis-2486	78	11	are	be	AUX
fcis-2486	78	12	defined	define	VERB
fcis-2486	78	13	as	as	ADP
fcis-2486	78	14	the	the	DET
fcis-2486	78	15	input	input	NOUN
fcis-2486	78	16	and	and	CCONJ
fcis-2486	78	17	output	output	NOUN
fcis-2486	78	18	of	of	ADP
fcis-2486	78	19	the	the	DET
fcis-2486	78	20	n	n	ADV
fcis-2486	78	21	-	-	PUNCT
fcis-2486	78	22	th	th	VERB
fcis-2486	78	23	mafb	mafb	NOUN
fcis-2486	78	24	separately	separately	ADV
fcis-2486	78	25	.	.	PUNCT
fcis-2486	79	1	similar	similar	ADJ
fcis-2486	79	2	to	to	ADP
fcis-2486	79	3	senet	senet	NOUN
fcis-2486	79	4	,	,	PUNCT
fcis-2486	79	5	the	the	DET
fcis-2486	79	6	two	two	NUM
fcis-2486	79	7	branches	branch	NOUN
fcis-2486	79	8	of	of	ADP
fcis-2486	79	9	mafb	mafb	NOUN
fcis-2486	79	10	are	be	AUX
fcis-2486	79	11	first	first	ADV
fcis-2486	79	12	subjected	subject	VERB
fcis-2486	79	13	to	to	ADP
fcis-2486	79	14	dimensionality	dimensionality	NOUN
fcis-2486	79	15	reduction	reduction	NOUN
fcis-2486	79	16	through	through	ADP
fcis-2486	79	17	a	a	DET
fcis-2486	79	18	1×1	1×1	ADJ
fcis-2486	79	19	convolutional	convolutional	ADJ
fcis-2486	79	20	layer	layer	NOUN
fcis-2486	79	21	which	which	PRON
fcis-2486	79	22	called	call	VERB
fcis-2486	79	23	fd−reduction(∙	fd−reduction(∙	NUM
fcis-2486	79	24	)	)	PUNCT
fcis-2486	79	25	.	.	PUNCT
fcis-2486	80	1	the	the	DET
fcis-2486	80	2	two	two	NUM
fcis-2486	80	3	pathways	pathway	NOUN
fcis-2486	80	4	in	in	ADP
fcis-2486	80	5	the	the	DET
fcis-2486	80	6	first	first	ADJ
fcis-2486	80	7	layer	layer	NOUN
fcis-2486	80	8	correspond	correspond	VERB
fcis-2486	80	9	to	to	ADP
fcis-2486	80	10	the	the	DET
fcis-2486	80	11	channel	channel	NOUN
fcis-2486	80	12	attention	attention	NOUN
fcis-2486	80	13	and	and	CCONJ
fcis-2486	80	14	spatial	spatial	ADJ
fcis-2486	80	15	attention	attention	NOUN
fcis-2486	80	16	modules	module	NOUN
fcis-2486	80	17	respectively	respectively	ADV
fcis-2486	80	18	.	.	PUNCT
fcis-2486	81	1	given	give	VERB
fcis-2486	81	2	input	input	NOUN
fcis-2486	81	3	features	feature	NOUN
fcis-2486	81	4	:	:	PUNCT
fcis-2486	81	5	𝑥𝑛−1	𝑥𝑛−1	NOUN
fcis-2486	81	6	′	′	NOUN
fcis-2486	81	7	=	=	SYM
fcis-2486	81	8	𝑓𝐷−𝑟𝑒𝑑𝑢𝑐𝑡𝑖𝑜𝑛	𝑓𝐷−𝑟𝑒𝑑𝑢𝑐𝑡𝑖𝑜𝑛	NUM
fcis-2486	82	1	′	′	NUM
fcis-2486	82	2	(	(	PUNCT
fcis-2486	82	3	𝑥𝑛−1	𝑥𝑛−1	NOUN
fcis-2486	82	4	)	)	PUNCT
fcis-2486	82	5	(	(	PUNCT
fcis-2486	82	6	7	7	X
fcis-2486	82	7	)	)	SYM
fcis-2486	82	8	15	15	NUM
fcis-2486	82	9	𝑥𝑛−1	𝑥𝑛−1	NOUN
fcis-2486	82	10	′′	′′	PROPN
fcis-2486	82	11	=	=	PUNCT
fcis-2486	82	12	𝑓𝐷−𝑟𝑒𝑑𝑢𝑐𝑡𝑖𝑜𝑛	𝑓𝐷−𝑟𝑒𝑑𝑢𝑐𝑡𝑖𝑜𝑛	X
fcis-2486	83	1	′′	′′	PROPN
fcis-2486	83	2	(	(	PUNCT
fcis-2486	83	3	𝑥𝑛−1	𝑥𝑛−1	PROPN
fcis-2486	83	4	)	)	PUNCT
fcis-2486	83	5	(	(	PUNCT
fcis-2486	83	6	8)	8)	NUM
fcis-2486	83	7	where	where	SCONJ
fcis-2486	83	8	the	the	DET
fcis-2486	83	9	number	number	NOUN
fcis-2486	83	10	of	of	ADP
fcis-2486	83	11	channels	channel	NOUN
fcis-2486	83	12	of	of	ADP
fcis-2486	83	13	xn−1	xn−1	PROPN
fcis-2486	83	14	′	′	NOUN
fcis-2486	83	15	and	and	CCONJ
fcis-2486	83	16	xn−1	xn−1	PROPN
fcis-2486	83	17	′′	′′	PROPN
fcis-2486	83	18	is	be	AUX
fcis-2486	83	19	only	only	ADV
fcis-2486	83	20	half	half	NOUN
fcis-2486	83	21	of	of	ADP
fcis-2486	83	22	that	that	PRON
fcis-2486	83	23	of	of	ADP
fcis-2486	83	24	xn−1	xn−1	PROPN
fcis-2486	83	25	.	.	PUNCT
fcis-2486	84	1	next	next	ADV
fcis-2486	84	2	,	,	PUNCT
fcis-2486	84	3	input	input	VERB
fcis-2486	84	4	the	the	DET
fcis-2486	84	5	output	output	NOUN
fcis-2486	84	6	of	of	ADP
fcis-2486	84	7	the	the	DET
fcis-2486	84	8	first	first	ADJ
fcis-2486	84	9	layer	layer	NOUN
fcis-2486	84	10	to	to	PART
fcis-2486	84	11	calculate	calculate	VERB
fcis-2486	84	12	the	the	DET
fcis-2486	84	13	channel	channel	NOUN
fcis-2486	84	14	and	and	CCONJ
fcis-2486	84	15	spatial	spatial	ADJ
fcis-2486	84	16	position	position	NOUN
fcis-2486	84	17	weights	weight	NOUN
fcis-2486	84	18	.	.	PUNCT
fcis-2486	85	1	finally	finally	ADV
fcis-2486	85	2	superimpose	superimpose	VERB
fcis-2486	85	3	the	the	DET
fcis-2486	85	4	learned	learn	VERB
fcis-2486	85	5	weight	weight	NOUN
fcis-2486	85	6	value	value	NOUN
fcis-2486	85	7	to	to	ADP
fcis-2486	85	8	each	each	DET
fcis-2486	85	9	corresponding	correspond	VERB
fcis-2486	85	10	feature	feature	NOUN
fcis-2486	85	11	point	point	NOUN
fcis-2486	85	12	position	position	NOUN
fcis-2486	85	13	by	by	ADP
fcis-2486	85	14	multiplying	multiply	VERB
fcis-2486	85	15	the	the	DET
fcis-2486	85	16	corresponding	corresponding	ADJ
fcis-2486	85	17	positions	position	NOUN
fcis-2486	85	18	,	,	PUNCT
fcis-2486	85	19	it	it	PRON
fcis-2486	85	20	can	can	AUX
fcis-2486	85	21	be	be	AUX
fcis-2486	85	22	expressed	express	VERB
fcis-2486	85	23	as	as	ADP
fcis-2486	85	24	:	:	PUNCT
fcis-2486	85	25	𝑢	𝑢	X
fcis-2486	85	26	=	=	NOUN
fcis-2486	85	27	𝑎⨂𝑏	𝑎⨂𝑏	NOUN
fcis-2486	85	28	(	(	PUNCT
fcis-2486	85	29	9	9	NUM
fcis-2486	85	30	)	)	PUNCT
fcis-2486	85	31	where	where	SCONJ
fcis-2486	85	32	a	a	PRON
fcis-2486	85	33	and	and	CCONJ
fcis-2486	85	34	b	b	NOUN
fcis-2486	85	35	represent	represent	VERB
fcis-2486	85	36	the	the	DET
fcis-2486	85	37	weight	weight	NOUN
fcis-2486	85	38	,	,	PUNCT
fcis-2486	85	39	values	value	NOUN
fcis-2486	85	40	output	output	NOUN
fcis-2486	85	41	by	by	ADP
fcis-2486	85	42	the	the	DET
fcis-2486	85	43	channel	channel	NOUN
fcis-2486	85	44	attention	attention	NOUN
fcis-2486	85	45	block	block	NOUN
fcis-2486	85	46	and	and	CCONJ
fcis-2486	85	47	the	the	DET
fcis-2486	85	48	spatial	spatial	ADJ
fcis-2486	85	49	attention	attention	NOUN
fcis-2486	85	50	block	block	NOUN
fcis-2486	85	51	separately	separately	ADV
fcis-2486	85	52	,	,	PUNCT
fcis-2486	85	53	⨂	⨂	PROPN
fcis-2486	85	54	is	be	AUX
fcis-2486	85	55	the	the	DET
fcis-2486	85	56	multiplication	multiplication	NOUN
fcis-2486	85	57	operation	operation	NOUN
fcis-2486	85	58	of	of	ADP
fcis-2486	85	59	the	the	DET
fcis-2486	85	60	corresponding	corresponding	ADJ
fcis-2486	85	61	position	position	NOUN
fcis-2486	85	62	weight	weight	NOUN
fcis-2486	85	63	information	information	NOUN
fcis-2486	85	64	,	,	PUNCT
fcis-2486	85	65	u	u	NOUN
fcis-2486	85	66	is	be	AUX
fcis-2486	85	67	the	the	DET
fcis-2486	85	68	output	output	NOUN
fcis-2486	85	69	of	of	ADP
fcis-2486	85	70	the	the	DET
fcis-2486	85	71	previous	previous	ADJ
fcis-2486	85	72	layer	layer	NOUN
fcis-2486	85	73	in	in	ADP
fcis-2486	85	74	mafb	mafb	NOUN
fcis-2486	85	75	which	which	PRON
fcis-2486	85	76	will	will	AUX
fcis-2486	85	77	be	be	AUX
fcis-2486	85	78	passed	pass	VERB
fcis-2486	85	79	through	through	ADP
fcis-2486	85	80	a	a	DET
fcis-2486	85	81	3×3	3×3	NUM
fcis-2486	85	82	convolution	convolution	NOUN
fcis-2486	85	83	and	and	CCONJ
fcis-2486	85	84	output	output	NOUN
fcis-2486	85	85	as	as	ADP
fcis-2486	85	86	𝑥′𝑛.	𝑥′𝑛.	DET
fcis-2486	85	87	the	the	DET
fcis-2486	85	88	operation	operation	NOUN
fcis-2486	85	89	of	of	ADP
fcis-2486	85	90	the	the	DET
fcis-2486	85	91	lower	low	ADJ
fcis-2486	85	92	layer	layer	NOUN
fcis-2486	85	93	is	be	AUX
fcis-2486	85	94	to	to	PART
fcis-2486	85	95	convert	convert	VERB
fcis-2486	85	96	xn−1	xn−1	PROPN
fcis-2486	85	97	′′	′′	PROPN
fcis-2486	85	98	to	to	ADP
fcis-2486	85	99	xn	xn	PROPN
fcis-2486	85	100	′′.	′′.	PROPN
fcis-2486	85	101	likewise	likewise	ADV
fcis-2486	85	102	,	,	PUNCT
fcis-2486	85	103	a	a	DET
fcis-2486	85	104	1×1	1×1	ADJ
fcis-2486	85	105	convolutional	convolutional	NOUN
fcis-2486	85	106	is	be	AUX
fcis-2486	85	107	used	use	VERB
fcis-2486	85	108	for	for	ADP
fcis-2486	85	109	dimensionality	dimensionality	NOUN
fcis-2486	85	110	reduction	reduction	NOUN
fcis-2486	85	111	,	,	PUNCT
fcis-2486	85	112	and	and	CCONJ
fcis-2486	85	113	then	then	ADV
fcis-2486	85	114	a	a	DET
fcis-2486	85	115	3×3	3×3	NUM
fcis-2486	85	116	convolutional	convolutional	ADJ
fcis-2486	85	117	layer	layer	NOUN
fcis-2486	85	118	for	for	ADP
fcis-2486	85	119	generating	generate	VERB
fcis-2486	85	120	xn	xn	PROPN
fcis-2486	86	1	′′	′′	PROPN
fcis-2486	86	2	to	to	PART
fcis-2486	86	3	preserve	preserve	VERB
fcis-2486	86	4	the	the	DET
fcis-2486	86	5	original	original	ADJ
fcis-2486	86	6	information	information	NOUN
fcis-2486	86	7	.	.	PUNCT
fcis-2486	87	1	finally	finally	ADV
fcis-2486	87	2	,	,	PUNCT
fcis-2486	87	3	concatenate	concatenate	VERB
fcis-2486	87	4	the	the	DET
fcis-2486	87	5	outputs	output	NOUN
fcis-2486	87	6	xn	xn	PROPN
fcis-2486	88	1	′	′	NUM
fcis-2486	89	1	and	and	CCONJ
fcis-2486	89	2	xn	xn	NUM
fcis-2486	90	1	′′	′′	PROPN
fcis-2486	90	2	of	of	ADP
fcis-2486	90	3	the	the	DET
fcis-2486	90	4	two	two	NUM
fcis-2486	90	5	layers	layer	NOUN
fcis-2486	90	6	.	.	PUNCT
fcis-2486	91	1	the	the	DET
fcis-2486	91	2	xn	xn	PROPN
fcis-2486	91	3	is	be	AUX
fcis-2486	91	4	then	then	ADV
fcis-2486	91	5	generated	generate	VERB
fcis-2486	91	6	by	by	ADP
fcis-2486	91	7	the	the	DET
fcis-2486	91	8	1×1	1×1	NUM
fcis-2486	91	9	convolution	convolution	NOUN
fcis-2486	91	10	.	.	PUNCT
fcis-2486	92	1	to	to	PART
fcis-2486	92	2	accelerate	accelerate	VERB
fcis-2486	92	3	training	training	NOUN
fcis-2486	92	4	,	,	PUNCT
fcis-2486	92	5	a	a	DET
fcis-2486	92	6	shortcut	shortcut	NOUN
fcis-2486	92	7	is	be	AUX
fcis-2486	92	8	added	add	VERB
fcis-2486	92	9	to	to	PART
fcis-2486	92	10	generate	generate	VERB
fcis-2486	92	11	the	the	DET
fcis-2486	92	12	output	output	NOUN
fcis-2486	92	13	features	feature	NOUN
fcis-2486	92	14	of	of	ADP
fcis-2486	92	15	this	this	DET
fcis-2486	92	16	part	part	NOUN
fcis-2486	92	17	.	.	PUNCT
fcis-2486	93	1	the	the	DET
fcis-2486	93	2	main	main	ADJ
fcis-2486	93	3	difference	difference	NOUN
fcis-2486	93	4	between	between	ADP
fcis-2486	93	5	this	this	DET
fcis-2486	93	6	structure	structure	NOUN
fcis-2486	93	7	and	and	CCONJ
fcis-2486	93	8	[	[	X
fcis-2486	93	9	12	12	NUM
fcis-2486	93	10	]	]	PUNCT
fcis-2486	93	11	is	be	AUX
fcis-2486	93	12	that	that	SCONJ
fcis-2486	93	13	we	we	PRON
fcis-2486	93	14	use	use	VERB
fcis-2486	93	15	two	two	NUM
fcis-2486	93	16	attention	attention	NOUN
fcis-2486	93	17	mechanisms	mechanism	NOUN
fcis-2486	93	18	instead	instead	ADV
fcis-2486	93	19	of	of	ADP
fcis-2486	93	20	the	the	DET
fcis-2486	93	21	pooling	pooling	NOUN
fcis-2486	93	22	and	and	CCONJ
fcis-2486	93	23	upsampling	upsample	VERB
fcis-2486	93	24	layers	layer	NOUN
fcis-2486	93	25	in	in	ADP
fcis-2486	93	26	it	it	PRON
fcis-2486	93	27	.	.	PUNCT
fcis-2486	94	1	it	it	PRON
fcis-2486	94	2	can	can	AUX
fcis-2486	94	3	be	be	AUX
fcis-2486	94	4	expressed	express	VERB
fcis-2486	94	5	as	as	ADP
fcis-2486	94	6	:	:	PUNCT
fcis-2486	94	7	𝑥𝑛	𝑥𝑛	PROPN
fcis-2486	94	8	=	=	PUNCT
fcis-2486	94	9	𝐶𝑜𝑛𝑐𝑎𝑡(𝑥𝑛	𝐶𝑜𝑛𝑐𝑎𝑡(𝑥𝑛	PROPN
fcis-2486	94	10	′	′	NUM
fcis-2486	94	11	,	,	PUNCT
fcis-2486	94	12	𝑥𝑛	𝑥𝑛	PROPN
fcis-2486	94	13	′′	′′	PROPN
fcis-2486	94	14	)	)	PUNCT
fcis-2486	95	1	+	+	CCONJ
fcis-2486	95	2	𝑥𝑛−1	𝑥𝑛−1	NOUN
fcis-2486	95	3	(	(	PUNCT
fcis-2486	95	4	10	10	NUM
fcis-2486	95	5	)	)	PUNCT
fcis-2486	95	6	where	where	SCONJ
fcis-2486	95	7	concat	concat	NOUN
fcis-2486	95	8	(	(	PUNCT
fcis-2486	95	9	·	·	PUNCT
fcis-2486	95	10	)	)	PUNCT
fcis-2486	95	11	is	be	AUX
fcis-2486	95	12	the	the	DET
fcis-2486	95	13	concatenate	concatenate	NOUN
fcis-2486	95	14	operation	operation	NOUN
fcis-2486	95	15	,	,	PUNCT
fcis-2486	95	16	and	and	CCONJ
fcis-2486	95	17	xn−1	xn−1	PROPN
fcis-2486	95	18	is	be	AUX
fcis-2486	95	19	the	the	DET
fcis-2486	95	20	input	input	NOUN
fcis-2486	95	21	(	(	PUNCT
fcis-2486	95	22	i.e.	i.e.	X
fcis-2486	95	23	,	,	PUNCT
fcis-2486	95	24	the	the	DET
fcis-2486	95	25	output	output	NOUN
fcis-2486	95	26	of	of	ADP
fcis-2486	95	27	the	the	DET
fcis-2486	95	28	previous	previous	ADJ
fcis-2486	95	29	mafb	mafb	NOUN
fcis-2486	95	30	)	)	PUNCT
fcis-2486	95	31	?	?	PUNCT
fcis-2486	96	1	3.2.1	3.2.1	X
fcis-2486	96	2	.	.	PUNCT
fcis-2486	96	3	synthetic	synthetic	ADJ
fcis-2486	96	4	channel	channel	NOUN
fcis-2486	96	5	attention	attention	NOUN
fcis-2486	96	6	block	block	NOUN
fcis-2486	96	7	inspired	inspire	VERB
fcis-2486	96	8	by	by	ADP
fcis-2486	96	9	[	[	PUNCT
fcis-2486	96	10	13	13	NUM
fcis-2486	96	11	]	]	PUNCT
fcis-2486	96	12	,	,	PUNCT
fcis-2486	96	13	scab	scab	NOUN
fcis-2486	96	14	first	first	ADV
fcis-2486	96	15	replaces	replace	VERB
fcis-2486	96	16	global	global	ADJ
fcis-2486	96	17	average	average	ADJ
fcis-2486	96	18	pooling	pooling	NOUN
fcis-2486	96	19	by	by	ADP
fcis-2486	96	20	global	global	ADJ
fcis-2486	96	21	standard	standard	ADJ
fcis-2486	96	22	deviation	deviation	NOUN
fcis-2486	96	23	pooling	pooling	NOUN
fcis-2486	96	24	and	and	CCONJ
fcis-2486	96	25	maximum	maximum	ADJ
fcis-2486	96	26	pooling	pooling	NOUN
fcis-2486	96	27	,	,	PUNCT
fcis-2486	96	28	and	and	CCONJ
fcis-2486	96	29	performs	perform	VERB
fcis-2486	96	30	feature	feature	NOUN
fcis-2486	96	31	concatenation	concatenation	NOUN
fcis-2486	96	32	on	on	ADP
fcis-2486	96	33	its	its	PRON
fcis-2486	96	34	output	output	NOUN
fcis-2486	96	35	.	.	PUNCT
fcis-2486	97	1	the	the	DET
fcis-2486	97	2	expression	expression	NOUN
fcis-2486	97	3	formula	formula	NOUN
fcis-2486	97	4	is	be	AUX
fcis-2486	97	5	:	:	PUNCT
fcis-2486	97	6	𝑧𝑐	𝑧𝑐	NOUN
fcis-2486	97	7	=	=	PUNCT
fcis-2486	97	8	𝐻𝑆𝐴𝑀(𝑥𝑐	𝐻𝑆𝐴𝑀(𝑥𝑐	PROPN
fcis-2486	97	9	)	)	PUNCT
fcis-2486	97	10	=	=	SYM
fcis-2486	97	11	𝐶𝑜𝑛𝑐𝑎𝑡(𝑠𝑐	𝐶𝑜𝑛𝑐𝑎𝑡(𝑠𝑐	NOUN
fcis-2486	97	12	,	,	PUNCT
fcis-2486	97	13	𝑀𝑎𝑥𝑐	𝑀𝑎𝑥𝑐	PROPN
fcis-2486	97	14	)	)	PUNCT
fcis-2486	97	15	(	(	PUNCT
fcis-2486	97	16	11	11	NUM
fcis-2486	97	17	)	)	PUNCT
fcis-2486	97	18	where	where	SCONJ
fcis-2486	97	19	zc	zc	NOUN
fcis-2486	97	20	and	and	CCONJ
fcis-2486	97	21	maxc	maxc	NOUN
fcis-2486	97	22	stand	stand	VERB
fcis-2486	97	23	for	for	ADP
fcis-2486	97	24	the	the	DET
fcis-2486	97	25	output	output	NOUN
fcis-2486	97	26	of	of	ADP
fcis-2486	97	27	the	the	DET
fcis-2486	97	28	c	c	NOUN
fcis-2486	97	29	-	-	PUNCT
fcis-2486	97	30	th	th	VERB
fcis-2486	97	31	element	element	NOUN
fcis-2486	97	32	and	and	CCONJ
fcis-2486	97	33	max	max	PROPN
fcis-2486	97	34	pooling	pool	VERB
fcis-2486	97	35	separately	separately	ADV
fcis-2486	97	36	.	.	PUNCT
fcis-2486	98	1	to	to	PART
fcis-2486	98	2	make	make	VERB
fcis-2486	98	3	the	the	DET
fcis-2486	98	4	most	most	ADJ
fcis-2486	98	5	of	of	ADP
fcis-2486	98	6	the	the	DET
fcis-2486	98	7	detailed	detailed	ADJ
fcis-2486	98	8	information	information	NOUN
fcis-2486	98	9	contained	contain	VERB
fcis-2486	98	10	in	in	ADP
fcis-2486	98	11	features	feature	NOUN
fcis-2486	98	12	,	,	PUNCT
fcis-2486	98	13	the	the	DET
fcis-2486	98	14	two	two	NUM
fcis-2486	98	15	one	one	NUM
fcis-2486	98	16	-	-	PUNCT
fcis-2486	98	17	dimensional	dimensional	ADJ
fcis-2486	98	18	vectors	vector	NOUN
fcis-2486	98	19	output	output	VERB
fcis-2486	98	20	by	by	ADP
fcis-2486	98	21	the	the	DET
fcis-2486	98	22	standard	standard	ADJ
fcis-2486	98	23	deviation	deviation	NOUN
fcis-2486	98	24	pooling	pool	VERB
fcis-2486	98	25	and	and	CCONJ
fcis-2486	98	26	the	the	DET
fcis-2486	98	27	maximum	maximum	ADJ
fcis-2486	98	28	pooling	pooling	NOUN
fcis-2486	98	29	are	be	AUX
fcis-2486	98	30	concatenated	concatenate	VERB
fcis-2486	98	31	into	into	ADP
fcis-2486	98	32	one	one	NUM
fcis-2486	98	33	two	two	NUM
fcis-2486	98	34	-	-	PUNCT
fcis-2486	98	35	dimensional	dimensional	ADJ
fcis-2486	98	36	matrix	matrix	NOUN
fcis-2486	98	37	.	.	PUNCT
fcis-2486	99	1	fig	fig	NOUN
fcis-2486	99	2	.	.	PUNCT
fcis-2486	100	1	3	3	NUM
fcis-2486	100	2	shows	show	VERB
fcis-2486	100	3	that	that	SCONJ
fcis-2486	100	4	the	the	DET
fcis-2486	100	5	dimensionality	dimensionality	NOUN
fcis-2486	100	6	reduction	reduction	NOUN
fcis-2486	100	7	and	and	CCONJ
fcis-2486	100	8	enhancement	enhancement	NOUN
fcis-2486	100	9	are	be	AUX
fcis-2486	100	10	carried	carry	VERB
fcis-2486	100	11	out	out	ADP
fcis-2486	100	12	respectively	respectively	ADV
fcis-2486	100	13	through	through	ADP
fcis-2486	100	14	two	two	NUM
fcis-2486	100	15	consecutives	consecutive	NOUN
fcis-2486	100	16	1×1	1×1	NUM
fcis-2486	100	17	convolution	convolution	NOUN
fcis-2486	100	18	,	,	PUNCT
fcis-2486	100	19	and	and	CCONJ
fcis-2486	100	20	then	then	ADV
fcis-2486	100	21	the	the	DET
fcis-2486	100	22	weights	weight	NOUN
fcis-2486	100	23	are	be	AUX
fcis-2486	100	24	normalized	normalize	VERB
fcis-2486	100	25	by	by	ADP
fcis-2486	100	26	a	a	DET
fcis-2486	100	27	sigmoid	sigmoid	NOUN
fcis-2486	100	28	activation	activation	NOUN
fcis-2486	100	29	function	function	VERB
fcis-2486	100	30	to	to	PART
fcis-2486	100	31	apply	apply	VERB
fcis-2486	100	32	the	the	DET
fcis-2486	100	33	weights	weight	NOUN
fcis-2486	100	34	to	to	ADP
fcis-2486	100	35	the	the	DET
fcis-2486	100	36	input	input	NOUN
fcis-2486	100	37	features	feature	NOUN
fcis-2486	100	38	.	.	PUNCT
fcis-2486	101	1	the	the	DET
fcis-2486	101	2	final	final	ADJ
fcis-2486	101	3	output	output	NOUN
fcis-2486	101	4	channel	channel	NOUN
fcis-2486	101	5	is	be	AUX
fcis-2486	101	6	1×1×c	1×1×c	NUM
fcis-2486	101	7	.	.	PUNCT
fcis-2486	102	1	the	the	DET
fcis-2486	102	2	process	process	NOUN
fcis-2486	102	3	is	be	AUX
fcis-2486	102	4	:	:	PUNCT
fcis-2486	102	5	𝑎	𝑎	PROPN
fcis-2486	102	6	=	=	PUNCT
fcis-2486	102	7	𝑓(𝑊𝑖(𝜎(𝑊𝑠(𝑧𝑐	𝑓(𝑊𝑖(𝜎(𝑊𝑠(𝑧𝑐	NOUN
fcis-2486	102	8	)	)	PUNCT
fcis-2486	102	9	)	)	PUNCT
fcis-2486	102	10	)	)	PUNCT
fcis-2486	102	11	)	)	PUNCT
fcis-2486	103	1	(	(	PUNCT
fcis-2486	103	2	12	12	NUM
fcis-2486	103	3	)	)	PUNCT
fcis-2486	103	4	where	where	SCONJ
fcis-2486	103	5	a	a	PRON
fcis-2486	103	6	and	and	CCONJ
fcis-2486	103	7	zc	zc	PROPN
fcis-2486	103	8	are	be	AUX
fcis-2486	103	9	the	the	DET
fcis-2486	103	10	output	output	NOUN
fcis-2486	103	11	of	of	ADP
fcis-2486	103	12	the	the	DET
fcis-2486	103	13	scab	scab	NOUN
fcis-2486	103	14	and	and	CCONJ
fcis-2486	103	15	the	the	DET
fcis-2486	103	16	result	result	NOUN
fcis-2486	103	17	of	of	ADP
fcis-2486	103	18	two	two	NUM
fcis-2486	103	19	pooling	pooling	NOUN
fcis-2486	103	20	and	and	CCONJ
fcis-2486	103	21	splicing	splicing	NOUN
fcis-2486	103	22	separately	separately	ADV
fcis-2486	103	23	,	,	PUNCT
fcis-2486	103	24	σ	σ	PROPN
fcis-2486	103	25	(	(	PUNCT
fcis-2486	103	26	·	·	PUNCT
fcis-2486	103	27	)	)	PUNCT
fcis-2486	103	28	and	and	CCONJ
fcis-2486	103	29	f	f	X
fcis-2486	103	30	(	(	PUNCT
fcis-2486	103	31	·	·	PUNCT
fcis-2486	103	32	)	)	PUNCT
fcis-2486	103	33	represent	represent	VERB
fcis-2486	103	34	the	the	DET
fcis-2486	103	35	relu	relu	NOUN
fcis-2486	103	36	function	function	NOUN
fcis-2486	103	37	and	and	CCONJ
fcis-2486	103	38	the	the	DET
fcis-2486	103	39	sigmoid	sigmoid	NOUN
fcis-2486	103	40	activation	activation	NOUN
fcis-2486	103	41	function	function	NOUN
fcis-2486	103	42	;	;	PUNCT
fcis-2486	103	43	ws	ws	PROPN
fcis-2486	103	44	and	and	CCONJ
fcis-2486	103	45	wi	wi	PROPN
fcis-2486	103	46	are	be	AUX
fcis-2486	103	47	1×1	1×1	NUM
fcis-2486	103	48	convolution	convolution	NOUN
fcis-2486	103	49	in	in	ADP
fcis-2486	103	50	which	which	PRON
fcis-2486	103	51	ws	ws	NOUN
fcis-2486	103	52	represents	represent	VERB
fcis-2486	103	53	the	the	DET
fcis-2486	103	54	operation	operation	NOUN
fcis-2486	103	55	to	to	PART
fcis-2486	103	56	be	be	AUX
fcis-2486	103	57	compressed	compress	VERB
fcis-2486	103	58	into	into	ADP
fcis-2486	103	59	one	one	NUM
fcis-2486	103	60	dimension	dimension	NOUN
fcis-2486	103	61	and	and	CCONJ
fcis-2486	103	62	wi	wi	PROPN
fcis-2486	103	63	is	be	AUX
fcis-2486	103	64	used	use	VERB
fcis-2486	103	65	to	to	PART
fcis-2486	103	66	increase	increase	VERB
fcis-2486	103	67	the	the	DET
fcis-2486	103	68	dimension	dimension	NOUN
fcis-2486	103	69	.	.	PUNCT
fcis-2486	104	1	they	they	PRON
fcis-2486	104	2	represent	represent	VERB
fcis-2486	104	3	compression	compression	NOUN
fcis-2486	104	4	and	and	CCONJ
fcis-2486	104	5	amplification	amplification	NOUN
fcis-2486	104	6	of	of	ADP
fcis-2486	104	7	feature	feature	NOUN
fcis-2486	104	8	channels	channel	NOUN
fcis-2486	104	9	according	accord	VERB
fcis-2486	104	10	to	to	ADP
fcis-2486	104	11	scale	scale	NOUN
fcis-2486	104	12	r	r	NOUN
fcis-2486	104	13	respectively	respectively	ADV
fcis-2486	104	14	.	.	PUNCT
fcis-2486	105	1	fig	fig	NOUN
fcis-2486	105	2	.	.	PUNCT
fcis-2486	106	1	3	3	NUM
fcis-2486	106	2	synthetic	synthetic	ADJ
fcis-2486	106	3	channel	channel	NOUN
fcis-2486	106	4	attention	attention	NOUN
fcis-2486	106	5	block	block	NOUN
fcis-2486	106	6	the	the	DET
fcis-2486	106	7	max	max	PROPN
fcis-2486	106	8	pooling	pooling	NOUN
fcis-2486	106	9	is	be	AUX
fcis-2486	106	10	to	to	PART
fcis-2486	106	11	take	take	VERB
fcis-2486	106	12	the	the	DET
fcis-2486	106	13	largest	large	ADJ
fcis-2486	106	14	feature	feature	NOUN
fcis-2486	106	15	point	point	NOUN
fcis-2486	106	16	in	in	ADP
fcis-2486	106	17	the	the	DET
fcis-2486	106	18	neighborhood	neighborhood	NOUN
fcis-2486	106	19	.	.	PUNCT
fcis-2486	107	1	it	it	PRON
fcis-2486	107	2	can	can	AUX
fcis-2486	107	3	learn	learn	VERB
fcis-2486	107	4	the	the	DET
fcis-2486	107	5	edge	edge	NOUN
fcis-2486	107	6	and	and	CCONJ
fcis-2486	107	7	texture	texture	ADJ
fcis-2486	107	8	structure	structure	NOUN
fcis-2486	107	9	of	of	ADP
fcis-2486	107	10	the	the	DET
fcis-2486	107	11	image	image	NOUN
fcis-2486	107	12	well	well	ADV
fcis-2486	107	13	.	.	PUNCT
fcis-2486	108	1	global	global	ADJ
fcis-2486	108	2	standard	standard	ADJ
fcis-2486	108	3	deviation	deviation	NOUN
fcis-2486	108	4	pooling	pool	VERB
fcis-2486	108	5	can	can	AUX
fcis-2486	108	6	provide	provide	VERB
fcis-2486	108	7	more	more	ADV
fcis-2486	108	8	effective	effective	ADJ
fcis-2486	108	9	information	information	NOUN
fcis-2486	108	10	for	for	ADP
fcis-2486	108	11	channel	channel	NOUN
fcis-2486	108	12	weight	weight	NOUN
fcis-2486	108	13	learning	learning	NOUN
fcis-2486	108	14	.	.	PUNCT
fcis-2486	109	1	let	let	VERB
fcis-2486	109	2	xn−1	xn−1	PROPN
fcis-2486	109	3	′	′	NUM
fcis-2486	110	1	=	=	PUNCT
fcis-2486	111	1	[	[	X
fcis-2486	111	2	x1	x1	X
fcis-2486	111	3	,	,	PUNCT
fcis-2486	111	4	.	.	PUNCT
fcis-2486	111	5	.	.	PUNCT
fcis-2486	111	6	.	.	PUNCT
fcis-2486	112	1	,	,	PUNCT
fcis-2486	112	2	xc	xc	PROPN
fcis-2486	112	3	,	,	PUNCT
fcis-2486	112	4	.	.	PUNCT
fcis-2486	112	5	.	.	PUNCT
fcis-2486	112	6	.	.	PUNCT
fcis-2486	113	1	,	,	PUNCT
fcis-2486	113	2	xc	xc	X
fcis-2486	113	3	]	]	X
fcis-2486	113	4	as	as	ADP
fcis-2486	113	5	the	the	DET
fcis-2486	113	6	input	input	NOUN
fcis-2486	113	7	,	,	PUNCT
fcis-2486	113	8	it	it	PRON
fcis-2486	113	9	has	have	VERB
fcis-2486	113	10	c	c	NOUN
fcis-2486	113	11	feature	feature	NOUN
fcis-2486	113	12	maps	map	NOUN
fcis-2486	113	13	of	of	ADP
fcis-2486	113	14	size	size	NOUN
fcis-2486	113	15	h×w	h×w	PROPN
fcis-2486	113	16	.	.	PUNCT
fcis-2486	114	1	the	the	DET
fcis-2486	114	2	formula	formula	NOUN
fcis-2486	114	3	for	for	ADP
fcis-2486	114	4	standard	standard	ADJ
fcis-2486	114	5	deviation	deviation	NOUN
fcis-2486	114	6	pooling	pool	VERB
fcis-2486	114	7	is	be	AUX
fcis-2486	114	8	:	:	PUNCT
fcis-2486	114	9	𝑠𝑡𝑑𝑐	𝑠𝑡𝑑𝑐	NOUN
fcis-2486	114	10	=	=	SYM
fcis-2486	114	11	𝐹𝑠𝑡𝑑(𝑥𝑐	𝐹𝑠𝑡𝑑(𝑥𝑐	X
fcis-2486	114	12	)	)	PUNCT
fcis-2486	114	13	=	=	SYM
fcis-2486	114	14	√	√	ADP
fcis-2486	114	15	1	1	NUM
fcis-2486	114	16	𝐻𝑊	𝐻𝑊	PROPN
fcis-2486	114	17	∑	∑	PUNCT
fcis-2486	114	18	(	(	PUNCT
fcis-2486	114	19	𝑥𝑐	𝑥𝑐	ADP
fcis-2486	114	20	𝑖,𝑗	𝑖,𝑗	PROPN
fcis-2486	114	21	−	−	PROPN
fcis-2486	114	22	1	1	NUM
fcis-2486	114	23	𝐻𝑊	𝐻𝑊	PROPN
fcis-2486	114	24	∑	∑	PROPN
fcis-2486	114	25	𝑥𝑐	𝑥𝑐	ADP
fcis-2486	114	26	𝑖,𝑗	𝑖,𝑗	PROPN
fcis-2486	114	27	(	(	PUNCT
fcis-2486	114	28	𝑖,𝑗)∈𝑥𝑐	𝑖,𝑗)∈𝑥𝑐	PROPN
fcis-2486	114	29	)	)	PUNCT
fcis-2486	114	30	2	2	NUM
fcis-2486	114	31	(	(	PUNCT
fcis-2486	114	32	𝑖,𝑗)∈𝑥𝑐	𝑖,𝑗)∈𝑥𝑐	PROPN
fcis-2486	114	33	(	(	PUNCT
fcis-2486	114	34	13	13	NUM
fcis-2486	114	35	)	)	PUNCT
fcis-2486	114	36	where	where	SCONJ
fcis-2486	114	37	stdc	stdc	NOUN
fcis-2486	114	38	is	be	AUX
fcis-2486	114	39	the	the	DET
fcis-2486	114	40	standard	standard	ADJ
fcis-2486	114	41	deviation	deviation	NOUN
fcis-2486	114	42	of	of	ADP
fcis-2486	114	43	the	the	DET
fcis-2486	114	44	c	c	NOUN
fcis-2486	114	45	-	-	PUNCT
fcis-2486	114	46	th	th	VERB
fcis-2486	114	47	channel	channel	NOUN
fcis-2486	114	48	?	?	PUNCT
fcis-2486	115	1	3.2.2	3.2.2	NUM
fcis-2486	115	2	.	.	PUNCT
fcis-2486	115	3	modified	modify	VERB
fcis-2486	115	4	spatial	spatial	ADJ
fcis-2486	115	5	attention	attention	NOUN
fcis-2486	115	6	block	block	NOUN
fcis-2486	115	7	texture	texture	NOUN
fcis-2486	115	8	details	detail	NOUN
fcis-2486	115	9	vary	vary	VERB
fcis-2486	115	10	at	at	ADP
fcis-2486	115	11	different	different	ADJ
fcis-2486	115	12	spatial	spatial	ADJ
fcis-2486	115	13	locations	location	NOUN
fcis-2486	115	14	.	.	PUNCT
fcis-2486	116	1	fig	fig	NOUN
fcis-2486	116	2	.	.	PUNCT
fcis-2486	117	1	4	4	NUM
fcis-2486	117	2	shows	show	VERB
fcis-2486	117	3	the	the	DET
fcis-2486	117	4	structure	structure	NOUN
fcis-2486	117	5	of	of	ADP
fcis-2486	117	6	the	the	DET
fcis-2486	117	7	proposed	propose	VERB
fcis-2486	117	8	msab.inspired	msab.inspire	VERB
fcis-2486	117	9	by	by	ADP
fcis-2486	117	10	senet	senet	NOUN
fcis-2486	117	11	[	[	X
fcis-2486	117	12	10	10	NUM
fcis-2486	117	13	]	]	PUNCT
fcis-2486	117	14	,	,	PUNCT
fcis-2486	117	15	we	we	PRON
fcis-2486	117	16	design	design	VERB
fcis-2486	117	17	a	a	DET
fcis-2486	117	18	structure	structure	NOUN
fcis-2486	117	19	for	for	ADP
fcis-2486	117	20	global	global	ADJ
fcis-2486	117	21	average	average	ADJ
fcis-2486	117	22	pooling	pooling	NOUN
fcis-2486	117	23	and	and	CCONJ
fcis-2486	117	24	standard	standard	ADJ
fcis-2486	117	25	deviation	deviation	NOUN
fcis-2486	117	26	pooling	pool	VERB
fcis-2486	117	27	along	along	ADP
fcis-2486	117	28	the	the	DET
fcis-2486	117	29	channel	channel	NOUN
fcis-2486	117	30	axis	axis	NOUN
fcis-2486	117	31	respectively	respectively	ADV
fcis-2486	117	32	when	when	SCONJ
fcis-2486	117	33	building	build	VERB
fcis-2486	117	34	a	a	DET
fcis-2486	117	35	spatial	spatial	ADJ
fcis-2486	117	36	attention	attention	NOUN
fcis-2486	117	37	mechanism	mechanism	NOUN
fcis-2486	117	38	.	.	PUNCT
fcis-2486	118	1	first	first	ADV
fcis-2486	118	2	,	,	PUNCT
fcis-2486	118	3	we	we	PRON
fcis-2486	118	4	perform	perform	VERB
fcis-2486	118	5	a	a	DET
fcis-2486	118	6	global	global	ADJ
fcis-2486	118	7	average	average	ADJ
fcis-2486	118	8	pooling	pool	VERB
fcis-2486	118	9	operation	operation	NOUN
fcis-2486	118	10	for	for	ADP
fcis-2486	118	11	each	each	DET
fcis-2486	118	12	channel	channel	NOUN
fcis-2486	118	13	of	of	ADP
fcis-2486	118	14	the	the	DET
fcis-2486	118	15	input	input	NOUN
fcis-2486	118	16	features	feature	NOUN
fcis-2486	118	17	.	.	PUNCT
fcis-2486	119	1	we	we	PRON
fcis-2486	119	2	assume	assume	VERB
fcis-2486	119	3	that	that	SCONJ
fcis-2486	119	4	the	the	DET
fcis-2486	119	5	dimension	dimension	NOUN
fcis-2486	119	6	of	of	ADP
fcis-2486	119	7	it	it	PRON
fcis-2486	119	8	is	be	AUX
fcis-2486	119	9	c×h×w	c×h×w	PROPN
fcis-2486	119	10	,	,	PUNCT
fcis-2486	119	11	and	and	CCONJ
fcis-2486	119	12	the	the	DET
fcis-2486	119	13	formula	formula	NOUN
fcis-2486	119	14	of	of	ADP
fcis-2486	119	15	the	the	DET
fcis-2486	119	16	c	c	NOUN
fcis-2486	119	17	-	-	PUNCT
fcis-2486	119	18	th	th	VERB
fcis-2486	119	19	channel	channel	NOUN
fcis-2486	119	20	feature	feature	NOUN
fcis-2486	119	21	is	be	AUX
fcis-2486	119	22	:	:	PUNCT
fcis-2486	119	23	𝑎𝑣𝑔𝑐	𝑎𝑣𝑔𝑐	PROPN
fcis-2486	119	24	=	=	SYM
fcis-2486	119	25	𝐹𝐺𝐴𝑃(𝑥𝑐	𝐹𝐺𝐴𝑃(𝑥𝑐	NOUN
fcis-2486	119	26	)	)	PUNCT
fcis-2486	120	1	=	=	SYM
fcis-2486	120	2	1	1	X
fcis-2486	120	3	𝐻𝑊	𝐻𝑊	PROPN
fcis-2486	120	4	∑	∑	PROPN
fcis-2486	120	5	∑	∑	PROPN
fcis-2486	120	6	𝑥𝑐	𝑥𝑐	INTJ
fcis-2486	120	7	𝑖,𝑗𝑊	𝑖,𝑗𝑊	VERB
fcis-2486	120	8	𝑗=1	𝑗=1	PROPN
fcis-2486	121	1	𝐻	𝐻	NOUN
fcis-2486	121	2	𝑖−1	𝑖−1	PROPN
fcis-2486	121	3	(	(	PUNCT
fcis-2486	121	4	14	14	NUM
fcis-2486	121	5	)	)	PUNCT
fcis-2486	121	6	where	where	SCONJ
fcis-2486	121	7	xc	xc	PROPN
fcis-2486	121	8	i	i	PROPN
fcis-2486	121	9	,	,	PUNCT
fcis-2486	121	10	j	j	PROPN
fcis-2486	121	11	represents	represent	VERB
fcis-2486	121	12	the	the	DET
fcis-2486	121	13	pixel	pixel	PROPN
fcis-2486	121	14	value	value	NOUN
fcis-2486	121	15	of	of	ADP
fcis-2486	121	16	the	the	DET
fcis-2486	121	17	position	position	NOUN
fcis-2486	121	18	(	(	PUNCT
fcis-2486	121	19	i	i	PROPN
fcis-2486	121	20	,	,	PUNCT
fcis-2486	121	21	j	j	PROPN
fcis-2486	121	22	)	)	PUNCT
fcis-2486	121	23	in	in	ADP
fcis-2486	121	24	the	the	DET
fcis-2486	121	25	c	c	NOUN
fcis-2486	121	26	-	-	PUNCT
fcis-2486	121	27	th	th	VERB
fcis-2486	121	28	channel	channel	NOUN
fcis-2486	121	29	of	of	ADP
fcis-2486	121	30	the	the	DET
fcis-2486	121	31	input	input	NOUN
fcis-2486	121	32	feature	feature	NOUN
fcis-2486	121	33	map	map	NOUN
fcis-2486	121	34	.	.	PUNCT
fcis-2486	122	1	fgap(∙	fgap(∙	PROPN
fcis-2486	122	2	)	)	PUNCT
fcis-2486	122	3	represents	represent	VERB
fcis-2486	122	4	the	the	DET
fcis-2486	122	5	global	global	ADJ
fcis-2486	122	6	average	average	ADJ
fcis-2486	122	7	pooling	pooling	NOUN
fcis-2486	122	8	operation	operation	NOUN
fcis-2486	122	9	,	,	PUNCT
fcis-2486	122	10	which	which	PRON
fcis-2486	122	11	is	be	AUX
fcis-2486	122	12	used	use	VERB
fcis-2486	122	13	to	to	PART
fcis-2486	122	14	generate	generate	VERB
fcis-2486	122	15	descriptors	descriptor	NOUN
fcis-2486	122	16	to	to	PART
fcis-2486	122	17	describe	describe	VERB
fcis-2486	122	18	the	the	DET
fcis-2486	122	19	significance	significance	NOUN
fcis-2486	122	20	of	of	ADP
fcis-2486	122	21	distinct	distinct	ADJ
fcis-2486	122	22	location	location	NOUN
fcis-2486	122	23	features	feature	NOUN
fcis-2486	122	24	.	.	PUNCT
fcis-2486	123	1	fig	fig	NOUN
fcis-2486	123	2	.	.	PUNCT
fcis-2486	124	1	4	4	NUM
fcis-2486	124	2	modified	modify	VERB
fcis-2486	124	3	spatial	spatial	ADJ
fcis-2486	124	4	attention	attention	NOUN
fcis-2486	124	5	block	block	NOUN
fcis-2486	124	6	the	the	DET
fcis-2486	124	7	pooled	pool	VERB
fcis-2486	124	8	features	feature	NOUN
fcis-2486	124	9	are	be	AUX
fcis-2486	124	10	concatenated	concatenate	VERB
fcis-2486	124	11	first	first	ADV
fcis-2486	124	12	,	,	PUNCT
fcis-2486	124	13	and	and	CCONJ
fcis-2486	124	14	the	the	DET
fcis-2486	124	15	number	number	NOUN
fcis-2486	124	16	of	of	ADP
fcis-2486	124	17	channels	channel	NOUN
fcis-2486	124	18	is	be	AUX
fcis-2486	124	19	compressed	compress	VERB
fcis-2486	124	20	by	by	ADP
fcis-2486	124	21	1×1	1×1	NUM
fcis-2486	124	22	convolution	convolution	NOUN
fcis-2486	124	23	.	.	PUNCT
fcis-2486	125	1	the	the	DET
fcis-2486	125	2	nonlinear	nonlinear	ADJ
fcis-2486	125	3	mapping	mapping	NOUN
fcis-2486	125	4	of	of	ADP
fcis-2486	125	5	spatial	spatial	ADJ
fcis-2486	125	6	weight	weight	NOUN
fcis-2486	125	7	information	information	NOUN
fcis-2486	125	8	is	be	AUX
fcis-2486	125	9	realized	realize	VERB
fcis-2486	125	10	by	by	ADP
fcis-2486	125	11	deconvolution	deconvolution	NOUN
fcis-2486	125	12	operation	operation	NOUN
fcis-2486	125	13	,	,	PUNCT
fcis-2486	125	14	which	which	PRON
fcis-2486	125	15	further	far	ADV
fcis-2486	125	16	reduces	reduce	VERB
fcis-2486	125	17	the	the	DET
fcis-2486	125	18	volume	volume	NOUN
fcis-2486	125	19	of	of	ADP
fcis-2486	125	20	calculation	calculation	NOUN
fcis-2486	125	21	and	and	CCONJ
fcis-2486	125	22	ensures	ensure	VERB
fcis-2486	125	23	that	that	SCONJ
fcis-2486	125	24	information	information	NOUN
fcis-2486	125	25	of	of	ADP
fcis-2486	125	26	multiple	multiple	ADJ
fcis-2486	125	27	spatial	spatial	ADJ
fcis-2486	125	28	position	position	NOUN
fcis-2486	125	29	are	be	AUX
fcis-2486	125	30	able	able	ADJ
fcis-2486	125	31	to	to	PART
fcis-2486	125	32	be	be	AUX
fcis-2486	125	33	strengthened	strengthen	VERB
fcis-2486	125	34	.	.	PUNCT
fcis-2486	126	1	ultimately	ultimately	ADV
fcis-2486	126	2	,	,	PUNCT
fcis-2486	126	3	the	the	DET
fcis-2486	126	4	calculated	calculated	ADJ
fcis-2486	126	5	spatial	spatial	ADJ
fcis-2486	126	6	weight	weight	NOUN
fcis-2486	126	7	feature	feature	NOUN
fcis-2486	126	8	maps	map	NOUN
fcis-2486	126	9	are	be	AUX
fcis-2486	126	10	normalized	normalize	VERB
fcis-2486	126	11	by	by	ADP
fcis-2486	126	12	the	the	DET
fcis-2486	126	13	sigmoid	sigmoid	NOUN
fcis-2486	126	14	activation	activation	NOUN
fcis-2486	126	15	function	function	NOUN
fcis-2486	126	16	.	.	PUNCT
fcis-2486	127	1	the	the	DET
fcis-2486	127	2	final	final	ADJ
fcis-2486	127	3	output	output	NOUN
fcis-2486	127	4	can	can	AUX
fcis-2486	127	5	be	be	AUX
fcis-2486	127	6	expressed	express	VERB
fcis-2486	127	7	as	as	ADP
fcis-2486	127	8	:	:	PUNCT
fcis-2486	127	9	b	b	X
fcis-2486	127	10	=	=	SYM
fcis-2486	127	11	f(wi(σ(wd(gc	f(wi(σ(wd(gc	NUM
fcis-2486	127	12	)	)	PUNCT
fcis-2486	127	13	)	)	PUNCT
fcis-2486	127	14	)	)	PUNCT
fcis-2486	127	15	)	)	PUNCT
fcis-2486	128	1	(	(	PUNCT
fcis-2486	128	2	15	15	NUM
fcis-2486	128	3	)	)	PUNCT
fcis-2486	128	4	where	where	SCONJ
fcis-2486	128	5	b	b	NOUN
fcis-2486	128	6	is	be	AUX
fcis-2486	128	7	the	the	DET
fcis-2486	128	8	output	output	NOUN
fcis-2486	128	9	feature	feature	NOUN
fcis-2486	128	10	of	of	ADP
fcis-2486	128	11	the	the	DET
fcis-2486	128	12	msab	msab	NOUN
fcis-2486	128	13	,	,	PUNCT
fcis-2486	128	14	and	and	CCONJ
fcis-2486	128	15	gc	gc	PROPN
fcis-2486	128	16	is	be	AUX
fcis-2486	128	17	the	the	DET
fcis-2486	128	18	concatenating	concatenating	NOUN
fcis-2486	128	19	result	result	NOUN
fcis-2486	128	20	of	of	ADP
fcis-2486	128	21	two	two	NUM
fcis-2486	128	22	pooling	pooling	NOUN
fcis-2486	128	23	.	.	PUNCT
fcis-2486	129	1	wi	wi	PROPN
fcis-2486	129	2	and	and	CCONJ
fcis-2486	129	3	wd	wd	PROPN
fcis-2486	129	4	denote	denote	VERB
fcis-2486	129	5	for	for	ADP
fcis-2486	129	6	1×1	1×1	NUM
fcis-2486	129	7	convolution	convolution	NOUN
fcis-2486	129	8	and	and	CCONJ
fcis-2486	129	9	deconvolution	deconvolution	NOUN
fcis-2486	129	10	respectively	respectively	ADV
fcis-2486	129	11	.	.	PUNCT
fcis-2486	130	1	3.2.3	3.2.3	X
fcis-2486	130	2	.	.	X
fcis-2486	130	3	up	up	ADP
fcis-2486	130	4	sample	sample	NOUN
fcis-2486	130	5	block	block	NOUN
fcis-2486	130	6	fig	fig	NOUN
fcis-2486	130	7	.	.	PUNCT
fcis-2486	131	1	5	5	NUM
fcis-2486	131	2	shows	show	VERB
fcis-2486	131	3	the	the	DET
fcis-2486	131	4	up	up	NOUN
fcis-2486	131	5	sample	sample	NOUN
fcis-2486	131	6	block	block	NOUN
fcis-2486	131	7	which	which	PRON
fcis-2486	131	8	uses	use	VERB
fcis-2486	131	9	sub	sub	ADJ
fcis-2486	131	10	-	-	ADJ
fcis-2486	131	11	pixel	pixel	ADJ
fcis-2486	131	12	convolution	convolution	NOUN
fcis-2486	131	13	[	[	X
fcis-2486	131	14	14	14	NUM
fcis-2486	131	15	]	]	PUNCT
fcis-2486	131	16	to	to	PART
fcis-2486	131	17	transform	transform	VERB
fcis-2486	131	18	the	the	DET
fcis-2486	131	19	feature	feature	NOUN
fcis-2486	131	20	in	in	ADP
fcis-2486	131	21	low	low	ADJ
fcis-2486	131	22	-	-	PUNCT
fcis-2486	131	23	frequency	frequency	NOUN
fcis-2486	131	24	into	into	ADP
fcis-2486	131	25	features	feature	NOUN
fcis-2486	131	26	in	in	ADP
fcis-2486	131	27	high	high	ADJ
fcis-2486	131	28	-	-	PUNCT
fcis-2486	131	29	frequency	frequency	NOUN
fcis-2486	131	30	in	in	ADP
fcis-2486	131	31	reconstruction	reconstruction	NOUN
fcis-2486	131	32	block	block	NOUN
fcis-2486	131	33	.	.	PUNCT
fcis-2486	132	1	it	it	PRON
fcis-2486	132	2	mainly	mainly	ADV
fcis-2486	132	3	consists	consist	VERB
fcis-2486	132	4	of	of	ADP
fcis-2486	132	5	conv	conv	ADJ
fcis-2486	132	6	with	with	ADP
fcis-2486	132	7	shuffle	shuffle	PROPN
fcis-2486	132	8	×2	×2	PROPN
fcis-2486	132	9	represents	represent	VERB
fcis-2486	132	10	a	a	DET
fcis-2486	132	11	3×3	3×3	NUM
fcis-2486	132	12	convolution	convolution	NOUN
fcis-2486	132	13	with	with	ADP
fcis-2486	132	14	shuffle	shuffle	NOUN
fcis-2486	132	15	in	in	ADP
fcis-2486	132	16	scale	scale	NOUN
fcis-2486	132	17	of	of	ADP
fcis-2486	132	18	2	2	NUM
fcis-2486	132	19	,	,	PUNCT
fcis-2486	132	20	and	and	CCONJ
fcis-2486	132	21	conv	conv	ADJ
fcis-2486	132	22	with	with	ADP
fcis-2486	132	23	shuffle	shuffle	NOUN
fcis-2486	132	24	×3	×3	NOUN
fcis-2486	132	25	represents	represent	VERB
fcis-2486	132	26	a	a	DET
fcis-2486	132	27	3×3	3×3	NUM
fcis-2486	132	28	convolution	convolution	NOUN
fcis-2486	132	29	with	with	ADP
fcis-2486	132	30	shuffle	shuffle	NOUN
fcis-2486	132	31	in	in	ADP
fcis-2486	132	32	scale	scale	NOUN
fcis-2486	132	33	of	of	ADP
fcis-2486	132	34	3	3	NUM
fcis-2486	132	35	.	.	PUNCT
fcis-2486	133	1	conv	conv	ADJ
fcis-2486	133	2	with	with	ADP
fcis-2486	133	3	shuffle	shuffle	PROPN
fcis-2486	133	4	×2	×2	PROPN
fcis-2486	133	5	and	and	CCONJ
fcis-2486	133	6	two	two	NUM
fcis-2486	133	7	conv	conv	ADJ
fcis-2486	133	8	with	with	ADP
fcis-2486	133	9	shuffle	shuffle	PROPN
fcis-2486	133	10	×2	×2	PROPN
fcis-2486	133	11	are	be	AUX
fcis-2486	133	12	used	use	VERB
fcis-2486	133	13	for	for	ADP
fcis-2486	133	14	×2	×2	PROPN
fcis-2486	133	15	and	and	CCONJ
fcis-2486	133	16	×4	×4	PRON
fcis-2486	133	17	scales	scale	VERB
fcis-2486	133	18	separately	separately	ADV
fcis-2486	133	19	,	,	PUNCT
fcis-2486	133	20	and	and	CCONJ
fcis-2486	133	21	conv	conv	ADJ
fcis-2486	133	22	with	with	ADP
fcis-2486	133	23	shuffle	shuffle	NOUN
fcis-2486	133	24	×3	×3	NOUN
fcis-2486	133	25	is	be	AUX
fcis-2486	133	26	used	use	VERB
fcis-2486	133	27	for	for	ADP
fcis-2486	133	28	scale	scale	NOUN
fcis-2486	133	29	of	of	ADP
fcis-2486	133	30	3	3	NUM
fcis-2486	133	31	.	.	PUNCT
fcis-2486	133	32	16	16	NUM
fcis-2486	133	33	fig	fig	NOUN
fcis-2486	133	34	.	.	PUNCT
fcis-2486	134	1	5	5	NUM
fcis-2486	134	2	upsample	upsample	NOUN
fcis-2486	134	3	block	block	NOUN
fcis-2486	134	4	when	when	SCONJ
fcis-2486	134	5	a	a	DET
fcis-2486	134	6	sr	sr	PROPN
fcis-2486	134	7	model	model	NOUN
fcis-2486	134	8	is	be	AUX
fcis-2486	134	9	trained	train	VERB
fcis-2486	134	10	on	on	ADP
fcis-2486	134	11	a	a	DET
fcis-2486	134	12	single	single	ADJ
fcis-2486	134	13	scale	scale	NOUN
fcis-2486	134	14	,	,	PUNCT
fcis-2486	134	15	the	the	DET
fcis-2486	134	16	upsampling	upsample	VERB
fcis-2486	134	17	channels	channel	NOUN
fcis-2486	134	18	of	of	ADP
fcis-2486	134	19	the	the	DET
fcis-2486	134	20	corresponding	corresponding	ADJ
fcis-2486	134	21	scale	scale	NOUN
fcis-2486	134	22	can	can	AUX
fcis-2486	134	23	be	be	AUX
fcis-2486	134	24	selected	select	VERB
fcis-2486	134	25	separately	separately	ADV
fcis-2486	134	26	.	.	PUNCT
fcis-2486	135	1	the	the	DET
fcis-2486	135	2	output	output	NOUN
fcis-2486	135	3	of	of	ADP
fcis-2486	135	4	the	the	DET
fcis-2486	135	5	previous	previous	ADJ
fcis-2486	135	6	step	step	NOUN
fcis-2486	135	7	is	be	AUX
fcis-2486	135	8	upsampled	upsample	VERB
fcis-2486	135	9	by	by	ADP
fcis-2486	135	10	sub	sub	ADJ
fcis-2486	135	11	-	-	ADJ
fcis-2486	135	12	pixel	pixel	ADJ
fcis-2486	135	13	convolution	convolution	NOUN
fcis-2486	135	14	,	,	PUNCT
fcis-2486	135	15	and	and	CCONJ
fcis-2486	135	16	the	the	DET
fcis-2486	135	17	features	feature	NOUN
fcis-2486	135	18	are	be	AUX
fcis-2486	135	19	further	far	ADV
fcis-2486	135	20	fused	fuse	VERB
fcis-2486	135	21	by	by	ADP
fcis-2486	135	22	a	a	DET
fcis-2486	135	23	3×3	3×3	NUM
fcis-2486	135	24	convolution	convolution	NOUN
fcis-2486	135	25	.	.	PUNCT
fcis-2486	136	1	it	it	PRON
fcis-2486	136	2	is	be	AUX
fcis-2486	136	3	then	then	ADV
fcis-2486	136	4	added	add	VERB
fcis-2486	136	5	to	to	ADP
fcis-2486	136	6	the	the	DET
fcis-2486	136	7	result	result	NOUN
fcis-2486	136	8	of	of	ADP
fcis-2486	136	9	upsampling	upsample	VERB
fcis-2486	136	10	by	by	ADP
fcis-2486	136	11	interpolation	interpolation	NOUN
fcis-2486	136	12	with	with	ADP
fcis-2486	136	13	the	the	DET
fcis-2486	136	14	features	feature	NOUN
fcis-2486	136	15	of	of	ADP
fcis-2486	136	16	the	the	DET
fcis-2486	136	17	input	input	NOUN
fcis-2486	136	18	𝐼𝐿𝑅.	𝐼𝐿𝑅.	X
fcis-2486	136	19	0rb	0rb	NOUN
fcis-2486	136	20	=	=	PUNCT
fcis-2486	137	1	s(x0	s(x0	NOUN
fcis-2486	138	1	+	+	NUM
fcis-2486	139	1	xn	xn	X
fcis-2486	139	2	)	)	PUNCT
fcis-2486	139	3	(	(	PUNCT
fcis-2486	139	4	16	16	NUM
fcis-2486	139	5	)	)	PUNCT
fcis-2486	139	6	where	where	SCONJ
fcis-2486	139	7	0rb	0rb	NOUN
fcis-2486	139	8	and	and	CCONJ
fcis-2486	139	9	s	s	PROPN
fcis-2486	139	10	(	(	PUNCT
fcis-2486	139	11	·	·	PUNCT
fcis-2486	139	12	)	)	PUNCT
fcis-2486	139	13	denote	denote	VERB
fcis-2486	139	14	the	the	DET
fcis-2486	139	15	output	output	NOUN
fcis-2486	139	16	of	of	ADP
fcis-2486	139	17	the	the	DET
fcis-2486	139	18	reconstruction	reconstruction	NOUN
fcis-2486	139	19	module	module	NOUN
fcis-2486	139	20	and	and	CCONJ
fcis-2486	139	21	the	the	DET
fcis-2486	139	22	sub	sub	ADJ
fcis-2486	139	23	-	-	ADJ
fcis-2486	139	24	pixel	pixel	ADJ
fcis-2486	139	25	convolution	convolution	NOUN
fcis-2486	139	26	operation	operation	NOUN
fcis-2486	139	27	respectively	respectively	ADV
fcis-2486	139	28	,	,	PUNCT
fcis-2486	139	29	while	while	SCONJ
fcis-2486	139	30	x0	x0	PROPN
fcis-2486	139	31	and	and	CCONJ
fcis-2486	139	32	xn	xn	PROPN
fcis-2486	139	33	denote	denote	VERB
fcis-2486	139	34	the	the	DET
fcis-2486	139	35	shallow	shallow	ADJ
fcis-2486	139	36	and	and	CCONJ
fcis-2486	139	37	deep	deep	ADJ
fcis-2486	139	38	features	feature	NOUN
fcis-2486	139	39	respectively	respectively	ADV
fcis-2486	139	40	.	.	PUNCT
fcis-2486	140	1	4	4	X
fcis-2486	140	2	.	.	X
fcis-2486	140	3	experiment	experiment	NOUN
fcis-2486	140	4	4.1	4.1	NUM
fcis-2486	140	5	.	.	PUNCT
fcis-2486	141	1	datasets	dataset	NOUN
fcis-2486	141	2	and	and	CCONJ
fcis-2486	141	3	metrics	metric	NOUN
fcis-2486	141	4	the	the	DET
fcis-2486	141	5	training	training	NOUN
fcis-2486	141	6	process	process	NOUN
fcis-2486	141	7	uses	use	VERB
fcis-2486	141	8	the	the	DET
fcis-2486	141	9	div2k	div2k	PROPN
fcis-2486	142	1	[	[	X
fcis-2486	142	2	15	15	NUM
fcis-2486	142	3	]	]	X
fcis-2486	142	4	dataset	dataset	NOUN
fcis-2486	142	5	,	,	PUNCT
fcis-2486	142	6	which	which	PRON
fcis-2486	142	7	contains	contain	VERB
fcis-2486	142	8	1000	1000	NUM
fcis-2486	142	9	high	high	ADJ
fcis-2486	142	10	resolution	resolution	NOUN
fcis-2486	142	11	images	image	NOUN
fcis-2486	142	12	with	with	ADP
fcis-2486	142	13	rich	rich	ADJ
fcis-2486	142	14	scene	scene	NOUN
fcis-2486	142	15	,	,	PUNCT
fcis-2486	142	16	edge	edge	NOUN
fcis-2486	142	17	and	and	CCONJ
fcis-2486	142	18	texture	texture	ADJ
fcis-2486	142	19	detail	detail	NOUN
fcis-2486	142	20	.	.	PUNCT
fcis-2486	143	1	we	we	PRON
fcis-2486	143	2	selected	select	VERB
fcis-2486	143	3	800	800	NUM
fcis-2486	143	4	of	of	ADP
fcis-2486	143	5	them	they	PRON
fcis-2486	143	6	for	for	ADP
fcis-2486	143	7	training	training	NOUN
fcis-2486	143	8	.	.	PUNCT
fcis-2486	144	1	the	the	DET
fcis-2486	144	2	lr	lr	PROPN
fcis-2486	144	3	images	image	NOUN
fcis-2486	144	4	were	be	AUX
fcis-2486	144	5	downsampled	downsample	VERB
fcis-2486	144	6	by	by	ADP
fcis-2486	144	7	bicubic	bicubic	NOUN
fcis-2486	144	8	to	to	PART
fcis-2486	144	9	obtain	obtain	VERB
fcis-2486	144	10	lowresolution	lowresolution	NOUN
fcis-2486	144	11	images	image	NOUN
fcis-2486	144	12	at	at	ADP
fcis-2486	144	13	×2	×2	NOUN
fcis-2486	144	14	,	,	PUNCT
fcis-2486	144	15	×3	×3	NOUN
fcis-2486	144	16	and	and	CCONJ
fcis-2486	144	17	×4	×4	NOUN
fcis-2486	144	18	scales	scale	NOUN
fcis-2486	144	19	.	.	PUNCT
fcis-2486	145	1	five	five	NUM
fcis-2486	145	2	standard	standard	ADJ
fcis-2486	145	3	benchmark	benchmark	ADJ
fcis-2486	145	4	datasets	dataset	NOUN
fcis-2486	145	5	,	,	PUNCT
fcis-2486	145	6	set5	set5	PROPN
fcis-2486	146	1	[	[	X
fcis-2486	146	2	16	16	NUM
fcis-2486	146	3	]	]	PUNCT
fcis-2486	146	4	,	,	PUNCT
fcis-2486	146	5	set14	set14	NOUN
fcis-2486	146	6	[	[	X
fcis-2486	146	7	8	8	NUM
fcis-2486	146	8	]	]	PUNCT
fcis-2486	146	9	,	,	PUNCT
fcis-2486	146	10	b100	b100	PROPN
fcis-2486	147	1	[	[	X
fcis-2486	147	2	4	4	NUM
fcis-2486	147	3	]	]	PUNCT
fcis-2486	147	4	,	,	PUNCT
fcis-2486	147	5	urban100	urban100	PROPN
fcis-2486	148	1	[	[	X
fcis-2486	148	2	17	17	NUM
fcis-2486	148	3	]	]	PUNCT
fcis-2486	148	4	and	and	CCONJ
fcis-2486	148	5	manga109	manga109	PROPN
fcis-2486	149	1	[	[	X
fcis-2486	149	2	18	18	NUM
fcis-2486	149	3	]	]	PUNCT
fcis-2486	149	4	,	,	PUNCT
fcis-2486	149	5	were	be	AUX
fcis-2486	149	6	used	use	VERB
fcis-2486	149	7	as	as	ADP
fcis-2486	149	8	the	the	DET
fcis-2486	149	9	test	test	NOUN
fcis-2486	149	10	set	set	VERB
fcis-2486	149	11	for	for	ADP
fcis-2486	149	12	the	the	DET
fcis-2486	149	13	testing	testing	NOUN
fcis-2486	149	14	process	process	NOUN
fcis-2486	149	15	.	.	PUNCT
fcis-2486	150	1	peak	peak	NOUN
fcis-2486	150	2	signal	signal	NOUN
fcis-2486	150	3	-	-	PUNCT
fcis-2486	150	4	to	to	ADP
fcis-2486	150	5	-	-	PUNCT
fcis-2486	150	6	noise	noise	NOUN
fcis-2486	150	7	ratio	ratio	NOUN
fcis-2486	150	8	(	(	PUNCT
fcis-2486	150	9	psnr	psnr	NOUN
fcis-2486	150	10	)	)	PUNCT
fcis-2486	150	11	and	and	CCONJ
fcis-2486	150	12	structural	structural	ADJ
fcis-2486	150	13	similarity	similarity	NOUN
fcis-2486	150	14	index	index	NOUN
fcis-2486	150	15	[	[	X
fcis-2486	150	16	19	19	NUM
fcis-2486	150	17	]	]	X
fcis-2486	150	18	(	(	PUNCT
fcis-2486	150	19	ssim	ssim	NOUN
fcis-2486	150	20	)	)	PUNCT
fcis-2486	150	21	are	be	AUX
fcis-2486	150	22	used	use	VERB
fcis-2486	150	23	as	as	ADP
fcis-2486	150	24	evaluation	evaluation	NOUN
fcis-2486	150	25	metrics	metric	NOUN
fcis-2486	150	26	.	.	PUNCT
fcis-2486	151	1	in	in	ADP
fcis-2486	151	2	this	this	DET
fcis-2486	151	3	paper	paper	NOUN
fcis-2486	151	4	,	,	PUNCT
fcis-2486	151	5	the	the	DET
fcis-2486	151	6	reconstructed	reconstruct	VERB
fcis-2486	151	7	images	image	NOUN
fcis-2486	151	8	are	be	AUX
fcis-2486	151	9	converted	convert	VERB
fcis-2486	151	10	to	to	ADP
fcis-2486	151	11	ycbcr	ycbcr	NOUN
fcis-2486	151	12	space	space	NOUN
fcis-2486	151	13	and	and	CCONJ
fcis-2486	151	14	compared	compare	VERB
fcis-2486	151	15	in	in	ADP
fcis-2486	151	16	the	the	DET
fcis-2486	151	17	y	y	PROPN
fcis-2486	151	18	channel	channel	NOUN
fcis-2486	151	19	.	.	PUNCT
fcis-2486	152	1	we	we	PRON
fcis-2486	152	2	use	use	VERB
fcis-2486	152	3	parameters	parameter	NOUN
fcis-2486	152	4	and	and	CCONJ
fcis-2486	152	5	multi	multi	NOUN
fcis-2486	152	6	-	-	ADJ
fcis-2486	152	7	adds	add	VERB
fcis-2486	152	8	to	to	PART
fcis-2486	152	9	measure	measure	VERB
fcis-2486	152	10	the	the	DET
fcis-2486	152	11	lightness	lightness	NOUN
fcis-2486	152	12	of	of	ADP
fcis-2486	152	13	the	the	DET
fcis-2486	152	14	model	model	NOUN
fcis-2486	152	15	and	and	CCONJ
fcis-2486	152	16	compare	compare	VERB
fcis-2486	152	17	it	it	PRON
fcis-2486	152	18	with	with	ADP
fcis-2486	152	19	the	the	DET
fcis-2486	152	20	existing	exist	VERB
fcis-2486	152	21	mainstream	mainstream	NOUN
fcis-2486	152	22	models	model	NOUN
fcis-2486	152	23	.	.	PUNCT
fcis-2486	153	1	4.2	4.2	NUM
fcis-2486	153	2	.	.	PUNCT
fcis-2486	154	1	implementation	implementation	NOUN
fcis-2486	154	2	details	detail	NOUN
fcis-2486	154	3	in	in	ADP
fcis-2486	154	4	order	order	NOUN
fcis-2486	154	5	to	to	PART
fcis-2486	154	6	avoid	avoid	VERB
fcis-2486	154	7	under	under	ADP
fcis-2486	154	8	-	-	PUNCT
fcis-2486	154	9	fitting	fit	VERB
fcis-2486	154	10	phenomenon	phenomenon	NOUN
fcis-2486	154	11	during	during	ADP
fcis-2486	154	12	the	the	DET
fcis-2486	154	13	training	training	NOUN
fcis-2486	154	14	process	process	NOUN
fcis-2486	154	15	,	,	PUNCT
fcis-2486	154	16	the	the	DET
fcis-2486	154	17	training	training	NOUN
fcis-2486	154	18	datasets	dataset	NOUN
fcis-2486	154	19	are	be	AUX
fcis-2486	154	20	augmented	augment	VERB
fcis-2486	154	21	by	by	ADP
fcis-2486	154	22	random	random	ADJ
fcis-2486	154	23	rotations	rotation	NOUN
fcis-2486	154	24	of	of	ADP
fcis-2486	154	25	90	90	NUM
fcis-2486	154	26	°	°	NOUN
fcis-2486	154	27	,	,	PUNCT
fcis-2486	154	28	180	180	NUM
fcis-2486	154	29	°	°	NOUN
fcis-2486	154	30	,	,	PUNCT
fcis-2486	154	31	270	270	NUM
fcis-2486	154	32	°	°	NUM
fcis-2486	154	33	and	and	CCONJ
fcis-2486	154	34	horizontal	horizontal	ADJ
fcis-2486	154	35	flipping	flipping	NOUN
fcis-2486	154	36	to	to	PART
fcis-2486	154	37	make	make	VERB
fcis-2486	154	38	it	it	PRON
fcis-2486	154	39	8	8	NUM
fcis-2486	154	40	times	time	NOUN
fcis-2486	154	41	larger	large	ADJ
fcis-2486	154	42	than	than	ADP
fcis-2486	154	43	the	the	DET
fcis-2486	154	44	original	original	NOUN
fcis-2486	154	45	.	.	PUNCT
fcis-2486	155	1	during	during	ADP
fcis-2486	155	2	the	the	DET
fcis-2486	155	3	training	training	NOUN
fcis-2486	155	4	of	of	ADP
fcis-2486	155	5	the	the	DET
fcis-2486	155	6	model	model	NOUN
fcis-2486	155	7	at	at	ADP
fcis-2486	155	8	three	three	NUM
fcis-2486	155	9	scales	scale	NOUN
fcis-2486	155	10	(	(	PUNCT
fcis-2486	155	11	×2	×2	NOUN
fcis-2486	155	12	,	,	PUNCT
fcis-2486	155	13	×3	×3	NOUN
fcis-2486	155	14	and	and	CCONJ
fcis-2486	155	15	×4	×4	NOUN
fcis-2486	155	16	)	)	PUNCT
fcis-2486	155	17	,	,	PUNCT
fcis-2486	155	18	32	32	NUM
fcis-2486	155	19	image	image	NOUN
fcis-2486	155	20	blocks	block	NOUN
fcis-2486	155	21	of	of	ADP
fcis-2486	155	22	sizes	size	NOUN
fcis-2486	155	23	128	128	NUM
fcis-2486	155	24	×	×	NOUN
fcis-2486	155	25	128	128	NUM
fcis-2486	155	26	,	,	PUNCT
fcis-2486	155	27	192	192	NUM
fcis-2486	155	28	×	×	NOUN
fcis-2486	155	29	192	192	NUM
fcis-2486	155	30	and	and	CCONJ
fcis-2486	155	31	256	256	NUM
fcis-2486	155	32	×	×	NOUN
fcis-2486	155	33	256	256	NUM
fcis-2486	155	34	were	be	AUX
fcis-2486	155	35	randomly	randomly	ADV
fcis-2486	155	36	cropped	crop	VERB
fcis-2486	155	37	for	for	ADP
fcis-2486	155	38	each	each	DET
fcis-2486	155	39	batch	batch	NOUN
fcis-2486	155	40	as	as	ADP
fcis-2486	155	41	input	input	NOUN
fcis-2486	155	42	.	.	PUNCT
fcis-2486	156	1	the	the	DET
fcis-2486	156	2	l1	l1	PROPN
fcis-2486	156	3	loss	loss	NOUN
fcis-2486	156	4	function	function	NOUN
fcis-2486	156	5	[	[	X
fcis-2486	156	6	19	19	NUM
fcis-2486	156	7	]	]	PUNCT
fcis-2486	156	8	and	and	CCONJ
fcis-2486	156	9	adam	adam	PROPN
fcis-2486	156	10	optimizer	optimizer	NOUN
fcis-2486	156	11	are	be	AUX
fcis-2486	156	12	chosen	choose	VERB
fcis-2486	156	13	for	for	ADP
fcis-2486	156	14	the	the	DET
fcis-2486	156	15	training	training	NOUN
fcis-2486	156	16	.	.	PUNCT
fcis-2486	157	1	we	we	PRON
fcis-2486	157	2	adapt	adapt	VERB
fcis-2486	157	3	the	the	DET
fcis-2486	157	4	cosine	cosine	NOUN
fcis-2486	157	5	annealing	anneal	VERB
fcis-2486	157	6	learning	learn	VERB
fcis-2486	157	7	scheme	scheme	NOUN
fcis-2486	157	8	,	,	PUNCT
fcis-2486	157	9	and	and	CCONJ
fcis-2486	157	10	the	the	DET
fcis-2486	157	11	initial	initial	ADJ
fcis-2486	157	12	maximum	maximum	ADJ
fcis-2486	157	13	and	and	CCONJ
fcis-2486	157	14	minimum	minimum	ADJ
fcis-2486	157	15	learning	learning	NOUN
fcis-2486	157	16	rates	rate	NOUN
fcis-2486	157	17	were	be	AUX
fcis-2486	157	18	set	set	VERB
fcis-2486	157	19	to	to	ADP
fcis-2486	157	20	1e-3	1e-3	PROPN
fcis-2486	157	21	and	and	CCONJ
fcis-2486	157	22	1e-7	1e-7	NUM
fcis-2486	157	23	,	,	PUNCT
fcis-2486	157	24	respectively	respectively	ADV
fcis-2486	157	25	.	.	PUNCT
fcis-2486	158	1	the	the	DET
fcis-2486	158	2	cosine	cosine	NOUN
fcis-2486	158	3	period	period	NOUN
fcis-2486	158	4	is	be	AUX
fcis-2486	158	5	250k	250k	NUM
fcis-2486	158	6	iterations	iteration	NOUN
fcis-2486	158	7	.	.	PUNCT
fcis-2486	159	1	our	our	PRON
fcis-2486	159	2	model	model	NOUN
fcis-2486	159	3	is	be	AUX
fcis-2486	159	4	based	base	VERB
fcis-2486	159	5	on	on	ADP
fcis-2486	159	6	the	the	DET
fcis-2486	159	7	pytorch	pytorch	NOUN
fcis-2486	159	8	framework	framework	NOUN
fcis-2486	159	9	,	,	PUNCT
fcis-2486	159	10	and	and	CCONJ
fcis-2486	159	11	an	an	DET
fcis-2486	159	12	nvidia	nvidia	PROPN
fcis-2486	159	13	tesla	tesla	NOUN
fcis-2486	159	14	v100	v100	PROPN
fcis-2486	159	15	gpu	gpu	NOUN
fcis-2486	159	16	with	with	ADP
fcis-2486	159	17	32	32	NUM
fcis-2486	159	18	gb	gb	NOUN
fcis-2486	159	19	of	of	ADP
fcis-2486	159	20	memory	memory	NOUN
fcis-2486	159	21	was	be	AUX
fcis-2486	159	22	selected	select	VERB
fcis-2486	159	23	for	for	ADP
fcis-2486	159	24	training	training	NOUN
fcis-2486	159	25	acceleration	acceleration	NOUN
fcis-2486	159	26	.	.	PUNCT
fcis-2486	160	1	4.3	4.3	NUM
fcis-2486	160	2	.	.	PUNCT
fcis-2486	160	3	ablation	ablation	NOUN
fcis-2486	160	4	experiments	experiment	NOUN
fcis-2486	160	5	4.3.1	4.3.1	VERB
fcis-2486	160	6	.	.	PUNCT
fcis-2486	161	1	the	the	DET
fcis-2486	161	2	influence	influence	NOUN
fcis-2486	161	3	of	of	ADP
fcis-2486	161	4	standard	standard	ADJ
fcis-2486	161	5	deviation	deviation	NOUN
fcis-2486	161	6	pooling	pooling	NOUN
fcis-2486	161	7	and	and	CCONJ
fcis-2486	161	8	max	max	PROPN
fcis-2486	161	9	pooling	pooling	NOUN
fcis-2486	161	10	to	to	PART
fcis-2486	161	11	demonstrate	demonstrate	VERB
fcis-2486	161	12	the	the	DET
fcis-2486	161	13	effectiveness	effectiveness	NOUN
fcis-2486	161	14	of	of	ADP
fcis-2486	161	15	the	the	DET
fcis-2486	161	16	designed	design	VERB
fcis-2486	161	17	structure	structure	NOUN
fcis-2486	161	18	,	,	PUNCT
fcis-2486	161	19	three	three	NUM
fcis-2486	161	20	sets	set	NOUN
fcis-2486	161	21	of	of	ADP
fcis-2486	161	22	experiments	experiment	NOUN
fcis-2486	161	23	were	be	AUX
fcis-2486	161	24	conducted	conduct	VERB
fcis-2486	161	25	as	as	SCONJ
fcis-2486	161	26	follows	follow	VERB
fcis-2486	161	27	:	:	PUNCT
fcis-2486	161	28	(	(	PUNCT
fcis-2486	161	29	1	1	X
fcis-2486	161	30	)	)	PUNCT
fcis-2486	161	31	removing	remove	VERB
fcis-2486	161	32	maximum	maximum	ADJ
fcis-2486	161	33	pooling	pooling	NOUN
fcis-2486	161	34	from	from	ADP
fcis-2486	161	35	scab	scab	NOUN
fcis-2486	161	36	;	;	PUNCT
fcis-2486	161	37	(	(	PUNCT
fcis-2486	161	38	2	2	X
fcis-2486	161	39	)	)	PUNCT
fcis-2486	161	40	removing	remove	VERB
fcis-2486	161	41	global	global	ADJ
fcis-2486	161	42	standard	standard	ADJ
fcis-2486	161	43	deviation	deviation	NOUN
fcis-2486	161	44	pooling	pool	VERB
fcis-2486	161	45	from	from	ADP
fcis-2486	161	46	scab	scab	NOUN
fcis-2486	161	47	;	;	PUNCT
fcis-2486	161	48	(	(	PUNCT
fcis-2486	161	49	3	3	X
fcis-2486	161	50	)	)	PUNCT
fcis-2486	161	51	the	the	DET
fcis-2486	161	52	combination	combination	NOUN
fcis-2486	161	53	of	of	ADP
fcis-2486	161	54	global	global	ADJ
fcis-2486	161	55	standard	standard	ADJ
fcis-2486	161	56	deviation	deviation	NOUN
fcis-2486	161	57	pooling	pooling	NOUN
fcis-2486	161	58	and	and	CCONJ
fcis-2486	161	59	maximum	maximum	ADJ
fcis-2486	161	60	pooling	pooling	NOUN
fcis-2486	161	61	.	.	PUNCT
fcis-2486	162	1	the	the	DET
fcis-2486	162	2	results	result	NOUN
fcis-2486	162	3	of	of	ADP
fcis-2486	162	4	the	the	DET
fcis-2486	162	5	experiments	experiment	NOUN
fcis-2486	162	6	are	be	AUX
fcis-2486	162	7	shown	show	VERB
fcis-2486	162	8	in	in	ADP
fcis-2486	162	9	table	table	NOUN
fcis-2486	162	10	1	1	NUM
fcis-2486	162	11	.	.	PUNCT
fcis-2486	162	12	table	table	NOUN
fcis-2486	162	13	1	1	NUM
fcis-2486	162	14	.	.	PUNCT
fcis-2486	162	15	comparison	comparison	NOUN
fcis-2486	162	16	of	of	ADP
fcis-2486	162	17	different	different	ADJ
fcis-2486	162	18	pooling	pooling	NOUN
fcis-2486	162	19	methods	method	NOUN
fcis-2486	162	20	in	in	ADP
fcis-2486	162	21	scab	scab	NOUN
fcis-2486	162	22	in	in	ADP
fcis-2486	162	23	set5	set5	PROPN
fcis-2486	162	24	dataset	dataset	PROPN
fcis-2486	162	25	(	(	PUNCT
fcis-2486	162	26	×3	×3	NOUN
fcis-2486	162	27	)	)	PUNCT
fcis-2486	162	28	standard	standard	ADJ
fcis-2486	162	29	deviation	deviation	NOUN
fcis-2486	162	30	pooling	pool	VERB
fcis-2486	162	31	max	max	PROPN
fcis-2486	162	32	pooling	pool	VERB
fcis-2486	162	33	psnr	psnr	NOUN
fcis-2486	162	34	(	(	PUNCT
fcis-2486	162	35	db)/ssim	db)/ssim	NOUN
fcis-2486	162	36	√	√	NUM
fcis-2486	162	37	×	×	PROPN
fcis-2486	162	38	34.32/0.9254	34.32/0.9254	NUM
fcis-2486	162	39	×	×	NOUN
fcis-2486	162	40	√	√	NUM
fcis-2486	162	41	34.28/0/9251	34.28/0/9251	NUM
fcis-2486	162	42	√	√	NOUN
fcis-2486	163	1	√	√	NUM
fcis-2486	163	2	34.37/0.9256	34.37/0.9256	NUM
fcis-2486	163	3	table	table	NOUN
fcis-2486	163	4	1	1	NUM
fcis-2486	163	5	shows	show	VERB
fcis-2486	163	6	the	the	DET
fcis-2486	163	7	comparison	comparison	NOUN
fcis-2486	163	8	of	of	ADP
fcis-2486	163	9	the	the	DET
fcis-2486	163	10	evaluation	evaluation	NOUN
fcis-2486	163	11	indicators	indicator	NOUN
fcis-2486	163	12	of	of	ADP
fcis-2486	163	13	the	the	DET
fcis-2486	163	14	set5	set5	PROPN
fcis-2486	163	15	dataset	dataset	NOUN
fcis-2486	163	16	(	(	PUNCT
fcis-2486	163	17	×3	×3	NOUN
fcis-2486	163	18	)	)	PUNCT
fcis-2486	163	19	.	.	PUNCT
fcis-2486	164	1	we	we	PRON
fcis-2486	164	2	can	can	AUX
fcis-2486	164	3	see	see	VERB
fcis-2486	164	4	that	that	PRON
fcis-2486	164	5	(	(	PUNCT
fcis-2486	164	6	1	1	X
fcis-2486	164	7	)	)	PUNCT
fcis-2486	164	8	with	with	ADP
fcis-2486	164	9	standard	standard	ADJ
fcis-2486	164	10	deviation	deviation	NOUN
fcis-2486	164	11	pooling	pooling	NOUN
fcis-2486	164	12	has	have	VERB
fcis-2486	164	13	a	a	DET
fcis-2486	164	14	0.04db	0.04db	NUM
fcis-2486	164	15	improvement	improvement	NOUN
fcis-2486	164	16	in	in	ADP
fcis-2486	164	17	the	the	DET
fcis-2486	164	18	psnr	psnr	NOUN
fcis-2486	164	19	and	and	CCONJ
fcis-2486	164	20	a	a	DET
fcis-2486	164	21	0.0003	0.0003	NUM
fcis-2486	164	22	improvement	improvement	NOUN
fcis-2486	164	23	in	in	ADP
fcis-2486	164	24	the	the	DET
fcis-2486	164	25	ssim	ssim	NOUN
fcis-2486	164	26	relative	relative	ADJ
fcis-2486	164	27	to	to	ADP
fcis-2486	164	28	(	(	PUNCT
fcis-2486	164	29	2	2	NUM
fcis-2486	164	30	)	)	PUNCT
fcis-2486	164	31	with	with	ADP
fcis-2486	164	32	maximum	maximum	ADJ
fcis-2486	164	33	pooling	pooling	NOUN
fcis-2486	164	34	.	.	PUNCT
fcis-2486	165	1	whereas	whereas	SCONJ
fcis-2486	165	2	(	(	PUNCT
fcis-2486	165	3	3	3	NUM
fcis-2486	165	4	)	)	PUNCT
fcis-2486	165	5	,	,	PUNCT
fcis-2486	165	6	which	which	PRON
fcis-2486	165	7	adaptively	adaptively	ADV
fcis-2486	165	8	combines	combine	VERB
fcis-2486	165	9	maximum	maximum	ADJ
fcis-2486	165	10	pooling	pooling	NOUN
fcis-2486	165	11	and	and	CCONJ
fcis-2486	165	12	global	global	ADJ
fcis-2486	165	13	standard	standard	ADJ
fcis-2486	165	14	deviation	deviation	NOUN
fcis-2486	165	15	pooling	pooling	NOUN
fcis-2486	165	16	,	,	PUNCT
fcis-2486	165	17	has	have	VERB
fcis-2486	165	18	a	a	DET
fcis-2486	165	19	0.05db	0.05db	ADJ
fcis-2486	165	20	improvement	improvement	NOUN
fcis-2486	165	21	in	in	ADP
fcis-2486	165	22	psnr	psnr	NOUN
fcis-2486	165	23	and	and	CCONJ
fcis-2486	165	24	a	a	DET
fcis-2486	165	25	0.0002	0.0002	NUM
fcis-2486	165	26	improvement	improvement	NOUN
fcis-2486	165	27	in	in	ADP
fcis-2486	165	28	ssim	ssim	NOUN
fcis-2486	165	29	relative	relative	ADJ
fcis-2486	165	30	to	to	ADP
fcis-2486	165	31	(	(	PUNCT
fcis-2486	165	32	1	1	NUM
fcis-2486	165	33	)	)	PUNCT
fcis-2486	165	34	.	.	PUNCT
fcis-2486	166	1	it	it	PRON
fcis-2486	166	2	is	be	AUX
fcis-2486	166	3	easy	easy	ADJ
fcis-2486	166	4	to	to	PART
fcis-2486	166	5	see	see	VERB
fcis-2486	166	6	that	that	SCONJ
fcis-2486	166	7	global	global	ADJ
fcis-2486	166	8	standard	standard	ADJ
fcis-2486	166	9	deviation	deviation	NOUN
fcis-2486	166	10	pooling	pooling	NOUN
fcis-2486	166	11	has	have	VERB
fcis-2486	166	12	a	a	DET
fcis-2486	166	13	more	more	ADV
fcis-2486	166	14	significant	significant	ADJ
fcis-2486	166	15	effect	effect	NOUN
fcis-2486	166	16	on	on	ADP
fcis-2486	166	17	the	the	DET
fcis-2486	166	18	psnr	psnr	NOUN
fcis-2486	166	19	values	value	NOUN
fcis-2486	166	20	of	of	ADP
fcis-2486	166	21	the	the	DET
fcis-2486	166	22	model	model	NOUN
fcis-2486	166	23	than	than	ADP
fcis-2486	166	24	maximum	maximum	ADJ
fcis-2486	166	25	pooling	pooling	NOUN
fcis-2486	166	26	.	.	PUNCT
fcis-2486	167	1	the	the	DET
fcis-2486	167	2	experimental	experimental	ADJ
fcis-2486	167	3	results	result	NOUN
fcis-2486	167	4	show	show	VERB
fcis-2486	167	5	that	that	SCONJ
fcis-2486	167	6	the	the	DET
fcis-2486	167	7	designed	design	VERB
fcis-2486	167	8	double	double	ADJ
fcis-2486	167	9	pooling	pooling	NOUN
fcis-2486	167	10	structure	structure	NOUN
fcis-2486	167	11	has	have	VERB
fcis-2486	167	12	better	well	ADJ
fcis-2486	167	13	impact	impact	NOUN
fcis-2486	167	14	on	on	ADP
fcis-2486	167	15	learning	learn	VERB
fcis-2486	167	16	channel	channel	NOUN
fcis-2486	167	17	weight	weight	NOUN
fcis-2486	167	18	,	,	PUNCT
fcis-2486	167	19	which	which	PRON
fcis-2486	167	20	proves	prove	VERB
fcis-2486	167	21	the	the	DET
fcis-2486	167	22	effectiveness	effectiveness	NOUN
fcis-2486	167	23	of	of	ADP
fcis-2486	167	24	the	the	DET
fcis-2486	167	25	structure	structure	NOUN
fcis-2486	167	26	.	.	PUNCT
fcis-2486	168	1	4.3.2	4.3.2	X
fcis-2486	168	2	.	.	PUNCT
fcis-2486	169	1	influence	influence	NOUN
fcis-2486	169	2	of	of	ADP
fcis-2486	169	3	channel	channel	NOUN
fcis-2486	169	4	attention	attention	NOUN
fcis-2486	169	5	mechanism	mechanism	NOUN
fcis-2486	169	6	and	and	CCONJ
fcis-2486	169	7	spatial	spatial	ADJ
fcis-2486	169	8	attention	attention	NOUN
fcis-2486	169	9	mechanism	mechanism	NOUN
fcis-2486	169	10	table	table	NOUN
fcis-2486	169	11	2	2	NUM
fcis-2486	169	12	shows	show	VERB
fcis-2486	169	13	the	the	DET
fcis-2486	169	14	comparison	comparison	NOUN
fcis-2486	169	15	of	of	ADP
fcis-2486	169	16	the	the	DET
fcis-2486	169	17	evaluation	evaluation	NOUN
fcis-2486	169	18	indicators	indicator	NOUN
fcis-2486	169	19	of	of	ADP
fcis-2486	169	20	the	the	DET
fcis-2486	169	21	set5	set5	PROPN
fcis-2486	169	22	dataset	dataset	NOUN
fcis-2486	169	23	(	(	PUNCT
fcis-2486	169	24	×2	×2	PROPN
fcis-2486	169	25	)	)	PUNCT
fcis-2486	169	26	.	.	PUNCT
fcis-2486	170	1	the	the	DET
fcis-2486	170	2	effectiveness	effectiveness	NOUN
fcis-2486	170	3	of	of	ADP
fcis-2486	170	4	the	the	DET
fcis-2486	170	5	proposed	propose	VERB
fcis-2486	170	6	mafb	mafb	NOUN
fcis-2486	170	7	is	be	AUX
fcis-2486	170	8	verified	verify	VERB
fcis-2486	170	9	by	by	ADP
fcis-2486	170	10	ablation	ablation	NOUN
fcis-2486	170	11	experiments	experiment	NOUN
fcis-2486	170	12	.	.	PUNCT
fcis-2486	171	1	we	we	PRON
fcis-2486	171	2	first	first	ADV
fcis-2486	171	3	remove	remove	VERB
fcis-2486	171	4	scab	scab	NOUN
fcis-2486	171	5	and	and	CCONJ
fcis-2486	171	6	then	then	ADV
fcis-2486	171	7	keep	keep	VERB
fcis-2486	171	8	the	the	DET
fcis-2486	171	9	rest	rest	NOUN
fcis-2486	171	10	the	the	DET
fcis-2486	171	11	same	same	ADJ
fcis-2486	171	12	.	.	PUNCT
fcis-2486	172	1	the	the	DET
fcis-2486	172	2	removed	removed	ADJ
fcis-2486	172	3	model	model	NOUN
fcis-2486	172	4	is	be	AUX
fcis-2486	172	5	trained	train	VERB
fcis-2486	172	6	and	and	CCONJ
fcis-2486	172	7	tested	test	VERB
fcis-2486	172	8	with	with	ADP
fcis-2486	172	9	the	the	DET
fcis-2486	172	10	same	same	ADJ
fcis-2486	172	11	set5	set5	NOUN
fcis-2486	172	12	dataset	dataset	NOUN
fcis-2486	172	13	(	(	PUNCT
fcis-2486	172	14	×2	×2	NOUN
fcis-2486	172	15	)	)	PUNCT
fcis-2486	172	16	.	.	PUNCT
fcis-2486	173	1	we	we	PRON
fcis-2486	173	2	can	can	AUX
fcis-2486	173	3	find	find	VERB
fcis-2486	173	4	that	that	SCONJ
fcis-2486	173	5	after	after	ADP
fcis-2486	173	6	removing	remove	VERB
fcis-2486	173	7	the	the	DET
fcis-2486	173	8	scab	scab	NOUN
fcis-2486	173	9	,	,	PUNCT
fcis-2486	173	10	the	the	DET
fcis-2486	173	11	psnr	psnr	NOUN
fcis-2486	173	12	decreased	decrease	VERB
fcis-2486	173	13	by	by	ADP
fcis-2486	173	14	0.08db	0.08db	NUM
fcis-2486	173	15	,	,	PUNCT
fcis-2486	173	16	and	and	CCONJ
fcis-2486	173	17	the	the	DET
fcis-2486	173	18	ssim	ssim	NOUN
fcis-2486	173	19	value	value	NOUN
fcis-2486	173	20	also	also	ADV
fcis-2486	173	21	decreased	decrease	VERB
fcis-2486	173	22	,	,	PUNCT
fcis-2486	173	23	which	which	PRON
fcis-2486	173	24	prove	prove	VERB
fcis-2486	173	25	the	the	DET
fcis-2486	173	26	effectiveness	effectiveness	NOUN
fcis-2486	173	27	of	of	ADP
fcis-2486	173	28	the	the	DET
fcis-2486	173	29	module	module	NOUN
fcis-2486	173	30	.	.	PUNCT
fcis-2486	174	1	then	then	ADV
fcis-2486	174	2	the	the	DET
fcis-2486	174	3	msab	msab	NOUN
fcis-2486	174	4	is	be	AUX
fcis-2486	174	5	removed	remove	VERB
fcis-2486	174	6	for	for	ADP
fcis-2486	174	7	training	training	NOUN
fcis-2486	174	8	,	,	PUNCT
fcis-2486	174	9	and	and	CCONJ
fcis-2486	174	10	both	both	DET
fcis-2486	174	11	psnr	psnr	NOUN
fcis-2486	174	12	and	and	CCONJ
fcis-2486	174	13	ssim	ssim	NOUN
fcis-2486	174	14	values	value	NOUN
fcis-2486	174	15	decrease	decrease	NOUN
fcis-2486	174	16	.	.	PUNCT
fcis-2486	175	1	the	the	DET
fcis-2486	175	2	comparison	comparison	NOUN
fcis-2486	175	3	results	result	NOUN
fcis-2486	175	4	are	be	AUX
fcis-2486	175	5	shown	show	VERB
fcis-2486	175	6	in	in	ADP
fcis-2486	175	7	table	table	NOUN
fcis-2486	175	8	2	2	NUM
fcis-2486	175	9	.	.	PUNCT
fcis-2486	176	1	it	it	PRON
fcis-2486	176	2	can	can	AUX
fcis-2486	176	3	be	be	AUX
fcis-2486	176	4	seen	see	VERB
fcis-2486	176	5	that	that	SCONJ
fcis-2486	176	6	the	the	DET
fcis-2486	176	7	effect	effect	NOUN
fcis-2486	176	8	of	of	ADP
fcis-2486	176	9	mafb	mafb	NOUN
fcis-2486	176	10	proposed	propose	VERB
fcis-2486	176	11	in	in	ADP
fcis-2486	176	12	this	this	DET
fcis-2486	176	13	paper	paper	NOUN
fcis-2486	176	14	is	be	AUX
fcis-2486	176	15	significantly	significantly	ADV
fcis-2486	176	16	better	well	ADJ
fcis-2486	176	17	than	than	ADP
fcis-2486	176	18	that	that	PRON
fcis-2486	176	19	with	with	ADP
fcis-2486	176	20	only	only	ADJ
fcis-2486	176	21	single	single	ADJ
fcis-2486	176	22	attention	attention	NOUN
fcis-2486	176	23	.	.	PUNCT
fcis-2486	177	1	and	and	CCONJ
fcis-2486	177	2	the	the	DET
fcis-2486	177	3	psnr	psnr	NOUN
fcis-2486	177	4	is	be	AUX
fcis-2486	177	5	improved	improve	VERB
fcis-2486	177	6	by	by	ADP
fcis-2486	177	7	about	about	ADV
fcis-2486	177	8	0.06~0.08db	0.06~0.08db	NUM
fcis-2486	177	9	,	,	PUNCT
fcis-2486	177	10	which	which	PRON
fcis-2486	177	11	also	also	ADV
fcis-2486	177	12	proves	prove	VERB
fcis-2486	177	13	that	that	SCONJ
fcis-2486	177	14	mafb	mafb	NOUN
fcis-2486	177	15	has	have	VERB
fcis-2486	177	16	better	well	ADJ
fcis-2486	177	17	effect	effect	NOUN
fcis-2486	177	18	on	on	ADP
fcis-2486	177	19	sisr	sisr	PROPN
fcis-2486	177	20	.	.	PUNCT
fcis-2486	177	21	table	table	NOUN
fcis-2486	177	22	2	2	NUM
fcis-2486	177	23	.	.	PUNCT
fcis-2486	177	24	comparison	comparison	NOUN
fcis-2486	177	25	of	of	ADP
fcis-2486	177	26	different	different	ADJ
fcis-2486	177	27	pooling	pooling	NOUN
fcis-2486	177	28	methods	method	NOUN
fcis-2486	177	29	in	in	ADP
fcis-2486	177	30	scab	scab	NOUN
fcis-2486	177	31	in	in	ADP
fcis-2486	177	32	set5	set5	PROPN
fcis-2486	177	33	dataset	dataset	PROPN
fcis-2486	177	34	(	(	PUNCT
fcis-2486	177	35	×3	×3	NOUN
fcis-2486	177	36	)	)	PUNCT
fcis-2486	177	37	scab	scab	NOUN
fcis-2486	177	38	msab	msab	VERB
fcis-2486	177	39	psnr	psnr	NOUN
fcis-2486	177	40	(	(	PUNCT
fcis-2486	177	41	db)/ssim	db)/ssim	NOUN
fcis-2486	177	42	√	√	NUM
fcis-2486	177	43	×	×	NOUN
fcis-2486	177	44	37.87/0.9605	37.87/0.9605	NUM
fcis-2486	177	45	×	×	NOUN
fcis-2486	177	46	√	√	NOUN
fcis-2486	177	47	37.85/0.9604	37.85/0.9604	NUM
fcis-2486	177	48	√	√	NUM
fcis-2486	177	49	√	√	NUM
fcis-2486	177	50	37.93/0.9607	37.93/0.9607	NUM
fcis-2486	177	51	4.4	4.4	NUM
fcis-2486	177	52	.	.	PUNCT
fcis-2486	178	1	comparison	comparison	NOUN
fcis-2486	178	2	with	with	ADP
fcis-2486	178	3	state	state	NOUN
fcis-2486	178	4	-	-	PUNCT
fcis-2486	178	5	of	of	ADP
fcis-2486	178	6	-	-	PUNCT
fcis-2486	178	7	the	the	DET
fcis-2486	178	8	-	-	PUNCT
fcis-2486	178	9	art	art	NOUN
fcis-2486	178	10	methods	method	NOUN
fcis-2486	178	11	to	to	PART
fcis-2486	178	12	prove	prove	VERB
fcis-2486	178	13	the	the	DET
fcis-2486	178	14	effectiveness	effectiveness	NOUN
fcis-2486	178	15	of	of	ADP
fcis-2486	178	16	the	the	DET
fcis-2486	178	17	proposed	propose	VERB
fcis-2486	178	18	method	method	NOUN
fcis-2486	178	19	for	for	ADP
fcis-2486	178	20	image	image	NOUN
fcis-2486	178	21	super	super	NOUN
fcis-2486	178	22	-	-	NOUN
fcis-2486	178	23	resolution	resolution	NOUN
fcis-2486	178	24	,	,	PUNCT
fcis-2486	178	25	we	we	PRON
fcis-2486	178	26	visualize	visualize	VERB
fcis-2486	178	27	the	the	DET
fcis-2486	178	28	partially	partially	ADV
fcis-2486	178	29	reconstructed	reconstruct	VERB
fcis-2486	178	30	images	image	NOUN
fcis-2486	178	31	of	of	ADP
fcis-2486	178	32	b100	b100	PROPN
fcis-2486	178	33	and	and	CCONJ
fcis-2486	178	34	urban100	urban100	PROPN
fcis-2486	178	35	datasets	dataset	NOUN
fcis-2486	178	36	.	.	PUNCT
fcis-2486	179	1	we	we	PRON
fcis-2486	179	2	select	select	VERB
fcis-2486	179	3	three	three	NUM
fcis-2486	179	4	groups	group	NOUN
fcis-2486	179	5	of	of	ADP
fcis-2486	179	6	images	image	NOUN
fcis-2486	179	7	for	for	ADP
fcis-2486	179	8	visual	visual	ADJ
fcis-2486	179	9	display	display	NOUN
fcis-2486	179	10	at	at	ADP
fcis-2486	179	11	the	the	DET
fcis-2486	179	12	scale	scale	NOUN
fcis-2486	179	13	of	of	ADP
fcis-2486	179	14	×2	×2	PROPN
fcis-2486	179	15	,	,	PUNCT
fcis-2486	179	16	×3	×3	NOUN
fcis-2486	179	17	,	,	PUNCT
fcis-2486	179	18	and	and	CCONJ
fcis-2486	179	19	×4	×4	NOUN
fcis-2486	179	20	.	.	PUNCT
fcis-2486	180	1	as	as	SCONJ
fcis-2486	180	2	shown	show	VERB
fcis-2486	180	3	in	in	ADP
fcis-2486	180	4	fig	fig	NOUN
fcis-2486	180	5	.	.	PUNCT
fcis-2486	181	1	6	6	NUM
fcis-2486	181	2	,	,	PUNCT
fcis-2486	181	3	the	the	DET
fcis-2486	181	4	blue	blue	ADJ
fcis-2486	181	5	tall	tall	ADJ
fcis-2486	181	6	building	building	NOUN
fcis-2486	181	7	(	(	PUNCT
fcis-2486	181	8	img012	img012	PROPN
fcis-2486	181	9	)	)	PUNCT
fcis-2486	181	10	and	and	CCONJ
fcis-2486	181	11	the	the	DET
fcis-2486	181	12	gray	gray	ADJ
fcis-2486	181	13	building	building	NOUN
fcis-2486	181	14	(	(	PUNCT
fcis-2486	181	15	img001	img001	PROPN
fcis-2486	181	16	)	)	PUNCT
fcis-2486	181	17	in	in	ADP
fcis-2486	181	18	the	the	DET
fcis-2486	181	19	urban100	urban100	PROPN
fcis-2486	181	20	dataset	dataset	NOUN
fcis-2486	181	21	are	be	AUX
fcis-2486	181	22	selected	select	VERB
fcis-2486	181	23	for	for	ADP
fcis-2486	181	24	visualization	visualization	NOUN
fcis-2486	181	25	at	at	ADP
fcis-2486	181	26	×2	×2	PROPN
fcis-2486	181	27	and	and	CCONJ
fcis-2486	181	28	×3	×3	NOUN
fcis-2486	181	29	scale	scale	NOUN
fcis-2486	181	30	,	,	PUNCT
fcis-2486	181	31	and	and	CCONJ
fcis-2486	181	32	the	the	DET
fcis-2486	181	33	bird	bird	NOUN
fcis-2486	181	34	(	(	PUNCT
fcis-2486	181	35	img_8023	img_8023	NUM
fcis-2486	181	36	)	)	PUNCT
fcis-2486	181	37	in	in	ADP
fcis-2486	181	38	the	the	DET
fcis-2486	181	39	b100	b100	PROPN
fcis-2486	181	40	dataset	dataset	NOUN
fcis-2486	181	41	is	be	AUX
fcis-2486	181	42	visualized	visualize	VERB
fcis-2486	181	43	at	at	ADP
fcis-2486	181	44	×4	×4	NOUN
fcis-2486	181	45	scale	scale	NOUN
fcis-2486	181	46	visualization	visualization	NOUN
fcis-2486	181	47	.	.	PUNCT
fcis-2486	182	1	the	the	DET
fcis-2486	182	2	reconstructed	reconstructed	ADJ
fcis-2486	182	3	images	image	NOUN
fcis-2486	182	4	are	be	AUX
fcis-2486	182	5	compared	compare	VERB
fcis-2486	182	6	with	with	ADP
fcis-2486	182	7	existing	exist	VERB
fcis-2486	182	8	models	model	NOUN
fcis-2486	182	9	such	such	ADJ
fcis-2486	182	10	as	as	ADP
fcis-2486	182	11	lapsrn	lapsrn	NOUN
fcis-2486	182	12	[	[	X
fcis-2486	182	13	7	7	NUM
fcis-2486	182	14	]	]	PUNCT
fcis-2486	182	15	,	,	PUNCT
fcis-2486	182	16	drrn	drrn	VERB
fcis-2486	182	17	[	[	X
fcis-2486	182	18	5	5	NUM
fcis-2486	182	19	]	]	PUNCT
fcis-2486	182	20	,	,	PUNCT
fcis-2486	182	21	msrn	msrn	NOUN
fcis-2486	182	22	[	[	X
fcis-2486	182	23	20	20	NUM
fcis-2486	182	24	]	]	PUNCT
fcis-2486	182	25	.	.	PUNCT
fcis-2486	183	1	fig	fig	NOUN
fcis-2486	183	2	.	.	PUNCT
fcis-2486	184	1	6	6	NUM
fcis-2486	184	2	first	first	ADV
fcis-2486	184	3	shows	show	VERB
fcis-2486	184	4	img012	img012	PROPN
fcis-2486	184	5	(	(	PUNCT
fcis-2486	184	6	×2	×2	PROPN
fcis-2486	184	7	)	)	PUNCT
fcis-2486	184	8	in	in	ADP
fcis-2486	184	9	urban100	urban100	PROPN
fcis-2486	184	10	dataset	dataset	NOUN
fcis-2486	184	11	.	.	PUNCT
fcis-2486	185	1	we	we	PRON
fcis-2486	185	2	can	can	AUX
fcis-2486	185	3	see	see	VERB
fcis-2486	185	4	that	that	SCONJ
fcis-2486	185	5	lmafn	lmafn	PROPN
fcis-2486	185	6	can	can	AUX
fcis-2486	185	7	restore	restore	VERB
fcis-2486	185	8	the	the	DET
fcis-2486	185	9	detailed	detailed	ADJ
fcis-2486	185	10	texture	texture	NOUN
fcis-2486	185	11	and	and	CCONJ
fcis-2486	185	12	edge	edge	NOUN
fcis-2486	185	13	information	information	NOUN
fcis-2486	185	14	of	of	ADP
fcis-2486	185	15	the	the	DET
fcis-2486	185	16	image	image	NOUN
fcis-2486	185	17	very	very	ADV
fcis-2486	185	18	well	well	ADV
fcis-2486	185	19	.	.	PUNCT
fcis-2486	186	1	for	for	ADP
fcis-2486	186	2	the	the	DET
fcis-2486	186	3	texture	texture	NOUN
fcis-2486	186	4	of	of	ADP
fcis-2486	186	5	the	the	DET
fcis-2486	186	6	window	window	NOUN
fcis-2486	186	7	in	in	ADP
fcis-2486	186	8	img012	img012	PROPN
fcis-2486	186	9	,	,	PUNCT
fcis-2486	186	10	most	most	ADJ
fcis-2486	186	11	existing	exist	VERB
fcis-2486	186	12	models	model	NOUN
fcis-2486	186	13	will	will	AUX
fcis-2486	186	14	generate	generate	VERB
fcis-2486	186	15	abnormal	abnormal	ADJ
fcis-2486	186	16	texture	texture	NOUN
fcis-2486	186	17	which	which	PRON
fcis-2486	186	18	is	be	AUX
fcis-2486	186	19	different	different	ADJ
fcis-2486	186	20	from	from	ADP
fcis-2486	186	21	the	the	DET
fcis-2486	186	22	original	original	ADJ
fcis-2486	186	23	image	image	NOUN
fcis-2486	186	24	,	,	PUNCT
fcis-2486	186	25	and	and	CCONJ
fcis-2486	186	26	the	the	DET
fcis-2486	186	27	edge	edge	NOUN
fcis-2486	186	28	of	of	ADP
fcis-2486	186	29	the	the	DET
fcis-2486	186	30	window	window	NOUN
fcis-2486	186	31	is	be	AUX
fcis-2486	186	32	basically	basically	ADV
fcis-2486	186	33	blurred	blur	VERB
fcis-2486	186	34	.	.	PUNCT
fcis-2486	187	1	taking	take	VERB
fcis-2486	187	2	lapsrn	lapsrn	NOUN
fcis-2486	187	3	[	[	X
fcis-2486	187	4	7	7	X
fcis-2486	187	5	]	]	PUNCT
fcis-2486	187	6	and	and	CCONJ
fcis-2486	187	7	memnet	memnet	NOUN
fcis-2486	187	8	[	[	X
fcis-2486	187	9	27	27	NUM
fcis-2486	187	10	]	]	PUNCT
fcis-2486	187	11	as	as	ADP
fcis-2486	187	12	examples	example	NOUN
fcis-2486	187	13	,	,	PUNCT
fcis-2486	187	14	the	the	DET
fcis-2486	187	15	reconstructed	reconstructed	ADJ
fcis-2486	187	16	17	17	NUM
fcis-2486	187	17	images	image	NOUN
fcis-2486	187	18	generated	generate	VERB
fcis-2486	187	19	by	by	ADP
fcis-2486	187	20	them	they	PRON
fcis-2486	187	21	have	have	VERB
fcis-2486	187	22	poor	poor	ADJ
fcis-2486	187	23	linear	linear	ADJ
fcis-2486	187	24	details	detail	NOUN
fcis-2486	187	25	for	for	ADP
fcis-2486	187	26	the	the	DET
fcis-2486	187	27	brown	brown	ADJ
fcis-2486	187	28	-	-	PUNCT
fcis-2486	187	29	yellow	yellow	ADJ
fcis-2486	187	30	window	window	NOUN
fcis-2486	187	31	part	part	NOUN
fcis-2486	187	32	,	,	PUNCT
fcis-2486	187	33	while	while	SCONJ
fcis-2486	187	34	maffsrn	maffsrn	NOUN
fcis-2486	187	35	[	[	X
fcis-2486	187	36	28	28	NUM
fcis-2486	187	37	]	]	PUNCT
fcis-2486	187	38	and	and	CCONJ
fcis-2486	187	39	msrn	msrn	NOUN
fcis-2486	187	40	[	[	X
fcis-2486	187	41	20	20	NUM
fcis-2486	187	42	]	]	PUNCT
fcis-2486	187	43	,	,	PUNCT
fcis-2486	187	44	which	which	PRON
fcis-2486	187	45	have	have	VERB
fcis-2486	187	46	relatively	relatively	ADV
fcis-2486	187	47	well	well	ADV
fcis-2486	187	48	,	,	PUNCT
fcis-2486	187	49	can	can	AUX
fcis-2486	187	50	not	not	PART
fcis-2486	187	51	fully	fully	ADV
fcis-2486	187	52	recover	recover	VERB
fcis-2486	187	53	most	most	ADJ
fcis-2486	187	54	of	of	ADP
fcis-2486	187	55	the	the	DET
fcis-2486	187	56	straight	straight	ADJ
fcis-2486	187	57	lines	line	NOUN
fcis-2486	187	58	in	in	ADP
fcis-2486	187	59	the	the	DET
fcis-2486	187	60	image	image	NOUN
fcis-2486	187	61	(	(	PUNCT
fcis-2486	187	62	especially	especially	ADV
fcis-2486	187	63	the	the	DET
fcis-2486	187	64	blue	blue	ADJ
fcis-2486	187	65	window	window	NOUN
fcis-2486	187	66	part	part	NOUN
fcis-2486	187	67	)	)	PUNCT
fcis-2486	187	68	.	.	PUNCT
fcis-2486	188	1	lmafn	lmafn	PROPN
fcis-2486	188	2	achieves	achieve	VERB
fcis-2486	188	3	a	a	DET
fcis-2486	188	4	better	well	ADJ
fcis-2486	188	5	visual	visual	ADJ
fcis-2486	188	6	effect	effect	NOUN
fcis-2486	188	7	while	while	SCONJ
fcis-2486	188	8	restoring	restore	VERB
fcis-2486	188	9	the	the	DET
fcis-2486	188	10	straight	straight	ADJ
fcis-2486	188	11	-	-	PUNCT
fcis-2486	188	12	line	line	NOUN
fcis-2486	188	13	texture	texture	NOUN
fcis-2486	188	14	of	of	ADP
fcis-2486	188	15	the	the	DET
fcis-2486	188	16	whole	whole	ADJ
fcis-2486	188	17	image	image	NOUN
fcis-2486	188	18	,	,	PUNCT
fcis-2486	188	19	which	which	PRON
fcis-2486	188	20	is	be	AUX
fcis-2486	188	21	closer	close	ADJ
fcis-2486	188	22	to	to	ADP
fcis-2486	188	23	the	the	DET
fcis-2486	188	24	gt	gt	PROPN
fcis-2486	188	25	image	image	NOUN
fcis-2486	188	26	.	.	PUNCT
fcis-2486	189	1	the	the	DET
fcis-2486	189	2	second	second	ADJ
fcis-2486	189	3	part	part	NOUN
fcis-2486	189	4	of	of	ADP
fcis-2486	189	5	fig	fig	NOUN
fcis-2486	189	6	.	.	PUNCT
fcis-2486	190	1	6	6	NUM
fcis-2486	190	2	shows	show	VERB
fcis-2486	190	3	img001	img001	PROPN
fcis-2486	190	4	(	(	PUNCT
fcis-2486	190	5	×3	×3	NOUN
fcis-2486	190	6	)	)	PUNCT
fcis-2486	190	7	in	in	ADP
fcis-2486	190	8	urban100	urban100	PROPN
fcis-2486	190	9	dataset	dataset	NOUN
fcis-2486	190	10	,	,	PUNCT
fcis-2486	190	11	which	which	PRON
fcis-2486	190	12	mainly	mainly	ADV
fcis-2486	190	13	compares	compare	VERB
fcis-2486	190	14	the	the	DET
fcis-2486	190	15	details	detail	NOUN
fcis-2486	190	16	of	of	ADP
fcis-2486	190	17	the	the	DET
fcis-2486	190	18	window	window	NOUN
fcis-2486	190	19	frame	frame	NOUN
fcis-2486	190	20	in	in	ADP
fcis-2486	190	21	the	the	DET
fcis-2486	190	22	middle	middle	NOUN
fcis-2486	190	23	.	.	PUNCT
fcis-2486	191	1	it	it	PRON
fcis-2486	191	2	can	can	AUX
fcis-2486	191	3	be	be	AUX
fcis-2486	191	4	seen	see	VERB
fcis-2486	191	5	that	that	SCONJ
fcis-2486	191	6	for	for	ADP
fcis-2486	191	7	the	the	DET
fcis-2486	191	8	oblique	oblique	ADJ
fcis-2486	191	9	frame	frame	NOUN
fcis-2486	191	10	in	in	ADP
fcis-2486	191	11	the	the	DET
fcis-2486	191	12	middle	middle	NOUN
fcis-2486	191	13	,	,	PUNCT
fcis-2486	191	14	most	most	ADJ
fcis-2486	191	15	of	of	ADP
fcis-2486	191	16	the	the	DET
fcis-2486	191	17	existing	exist	VERB
fcis-2486	191	18	models	model	NOUN
fcis-2486	191	19	can	can	AUX
fcis-2486	191	20	not	not	PART
fcis-2486	191	21	recover	recover	VERB
fcis-2486	191	22	the	the	DET
fcis-2486	191	23	edge	edge	NOUN
fcis-2486	191	24	information	information	NOUN
fcis-2486	191	25	of	of	ADP
fcis-2486	191	26	the	the	DET
fcis-2486	191	27	details	detail	NOUN
fcis-2486	191	28	well	well	ADV
fcis-2486	191	29	,	,	PUNCT
fcis-2486	191	30	and	and	CCONJ
fcis-2486	191	31	the	the	DET
fcis-2486	191	32	reconstructed	reconstructed	ADJ
fcis-2486	191	33	part	part	NOUN
fcis-2486	191	34	is	be	AUX
fcis-2486	191	35	very	very	ADV
fcis-2486	191	36	blurred	blurred	ADJ
fcis-2486	191	37	.	.	PUNCT
fcis-2486	192	1	for	for	ADP
fcis-2486	192	2	drrn	drrn	ADJ
fcis-2486	192	3	[	[	X
fcis-2486	192	4	5	5	NUM
fcis-2486	192	5	]	]	PUNCT
fcis-2486	192	6	and	and	CCONJ
fcis-2486	192	7	memnet	memnet	NOUN
fcis-2486	192	8	[	[	X
fcis-2486	192	9	27	27	NUM
fcis-2486	192	10	]	]	PUNCT
fcis-2486	192	11	,	,	PUNCT
fcis-2486	192	12	which	which	PRON
fcis-2486	192	13	can	can	AUX
fcis-2486	192	14	recover	recover	VERB
fcis-2486	192	15	the	the	DET
fcis-2486	192	16	oblique	oblique	ADJ
fcis-2486	192	17	border	border	NOUN
fcis-2486	192	18	,	,	PUNCT
fcis-2486	192	19	can	can	AUX
fcis-2486	192	20	not	not	PART
fcis-2486	192	21	recover	recover	VERB
fcis-2486	192	22	the	the	DET
fcis-2486	192	23	details	detail	NOUN
fcis-2486	192	24	of	of	ADP
fcis-2486	192	25	the	the	DET
fcis-2486	192	26	double	double	ADJ
fcis-2486	192	27	-	-	PUNCT
fcis-2486	192	28	striped	striped	ADJ
fcis-2486	192	29	border	border	NOUN
fcis-2486	192	30	on	on	ADP
fcis-2486	192	31	the	the	DET
fcis-2486	192	32	right	right	ADJ
fcis-2486	192	33	side	side	NOUN
fcis-2486	192	34	of	of	ADP
fcis-2486	192	35	the	the	DET
fcis-2486	192	36	image	image	NOUN
fcis-2486	192	37	very	very	ADV
fcis-2486	192	38	well	well	ADV
fcis-2486	192	39	.	.	PUNCT
fcis-2486	193	1	compared	compare	VERB
fcis-2486	193	2	with	with	ADP
fcis-2486	193	3	gt	gt	PROPN
fcis-2486	193	4	images	image	NOUN
fcis-2486	193	5	,	,	PUNCT
fcis-2486	193	6	maffsrn	maffsrn	NOUN
fcis-2486	194	1	[	[	X
fcis-2486	194	2	28	28	NUM
fcis-2486	194	3	]	]	PUNCT
fcis-2486	194	4	and	and	CCONJ
fcis-2486	194	5	msrn	msrn	NOUN
fcis-2486	195	1	[	[	X
fcis-2486	195	2	20	20	NUM
fcis-2486	195	3	]	]	PUNCT
fcis-2486	195	4	obtain	obtain	VERB
fcis-2486	195	5	more	more	ADV
fcis-2486	195	6	realistic	realistic	ADJ
fcis-2486	195	7	results	result	NOUN
fcis-2486	195	8	and	and	CCONJ
fcis-2486	195	9	recover	recover	VERB
fcis-2486	195	10	more	more	ADJ
fcis-2486	195	11	image	image	NOUN
fcis-2486	195	12	details	detail	NOUN
fcis-2486	195	13	,	,	PUNCT
fcis-2486	195	14	but	but	CCONJ
fcis-2486	195	15	lmafn	lmafn	PROPN
fcis-2486	195	16	can	can	AUX
fcis-2486	195	17	recover	recover	VERB
fcis-2486	195	18	the	the	DET
fcis-2486	195	19	edge	edge	NOUN
fcis-2486	195	20	information	information	NOUN
fcis-2486	195	21	of	of	ADP
fcis-2486	195	22	the	the	DET
fcis-2486	195	23	window	window	NOUN
fcis-2486	195	24	frame	frame	NOUN
fcis-2486	195	25	well	well	ADV
fcis-2486	195	26	without	without	ADP
fcis-2486	195	27	introducing	introduce	VERB
fcis-2486	195	28	blur	blur	NOUN
fcis-2486	195	29	artifacts	artifact	NOUN
fcis-2486	195	30	,	,	PUNCT
fcis-2486	195	31	and	and	CCONJ
fcis-2486	195	32	the	the	DET
fcis-2486	195	33	results	result	NOUN
fcis-2486	195	34	are	be	AUX
fcis-2486	195	35	clearer	clear	ADJ
fcis-2486	195	36	compared	compare	VERB
fcis-2486	195	37	to	to	ADP
fcis-2486	195	38	several	several	ADJ
fcis-2486	195	39	other	other	ADJ
fcis-2486	195	40	models	model	NOUN
fcis-2486	195	41	.	.	PUNCT
fcis-2486	196	1	these	these	DET
fcis-2486	196	2	results	result	NOUN
fcis-2486	196	3	verify	verify	VERB
fcis-2486	196	4	that	that	SCONJ
fcis-2486	196	5	lmafn	lmafn	PROPN
fcis-2486	196	6	has	have	VERB
fcis-2486	196	7	more	more	ADJ
fcis-2486	196	8	powerful	powerful	ADJ
fcis-2486	196	9	representation	representation	NOUN
fcis-2486	196	10	ability	ability	NOUN
fcis-2486	196	11	.	.	PUNCT
fcis-2486	197	1	the	the	DET
fcis-2486	197	2	last	last	ADJ
fcis-2486	197	3	part	part	NOUN
fcis-2486	197	4	of	of	ADP
fcis-2486	197	5	fig	fig	NOUN
fcis-2486	197	6	.	.	PUNCT
fcis-2486	198	1	6	6	NUM
fcis-2486	198	2	shows	show	VERB
fcis-2486	198	3	img_8023	img_8023	NUM
fcis-2486	198	4	(	(	PUNCT
fcis-2486	198	5	×4	×4	PROPN
fcis-2486	198	6	)	)	PUNCT
fcis-2486	198	7	in	in	ADP
fcis-2486	198	8	b100	b100	PROPN
fcis-2486	198	9	dataset	dataset	NOUN
fcis-2486	198	10	.	.	PUNCT
fcis-2486	199	1	focus	focus	VERB
fcis-2486	199	2	on	on	ADP
fcis-2486	199	3	contrasting	contrast	VERB
fcis-2486	199	4	the	the	DET
fcis-2486	199	5	details	detail	NOUN
fcis-2486	199	6	of	of	ADP
fcis-2486	199	7	the	the	DET
fcis-2486	199	8	bird	bird	NOUN
fcis-2486	199	9	feather	feather	NOUN
fcis-2486	199	10	texture	texture	NOUN
fcis-2486	199	11	in	in	ADP
fcis-2486	199	12	the	the	DET
fcis-2486	199	13	picture	picture	NOUN
fcis-2486	199	14	,	,	PUNCT
fcis-2486	199	15	among	among	ADP
fcis-2486	199	16	them	they	PRON
fcis-2486	199	17	,	,	PUNCT
fcis-2486	199	18	the	the	DET
fcis-2486	199	19	reconstructed	reconstructed	ADJ
fcis-2486	199	20	feather	feather	NOUN
fcis-2486	199	21	texture	texture	NOUN
fcis-2486	199	22	of	of	ADP
fcis-2486	199	23	lapsrn[7	lapsrn[7	PROPN
fcis-2486	199	24	]	]	X
fcis-2486	199	25	is	be	AUX
fcis-2486	199	26	very	very	ADV
fcis-2486	199	27	fuzzy	fuzzy	ADJ
fcis-2486	199	28	,	,	PUNCT
fcis-2486	199	29	drcn[4	drcn[4	NOUN
fcis-2486	199	30	]	]	PUNCT
fcis-2486	199	31	and	and	CCONJ
fcis-2486	199	32	drrn[5	drrn[5	NOUN
fcis-2486	199	33	]	]	PUNCT
fcis-2486	199	34	can	can	AUX
fcis-2486	199	35	retain	retain	VERB
fcis-2486	199	36	some	some	DET
fcis-2486	199	37	striped	striped	ADJ
fcis-2486	199	38	information	information	NOUN
fcis-2486	199	39	,	,	PUNCT
fcis-2486	199	40	but	but	CCONJ
fcis-2486	199	41	the	the	DET
fcis-2486	199	42	edge	edge	NOUN
fcis-2486	199	43	is	be	AUX
fcis-2486	199	44	very	very	ADV
fcis-2486	199	45	fuzzy	fuzzy	ADJ
fcis-2486	199	46	,	,	PUNCT
fcis-2486	199	47	and	and	CCONJ
fcis-2486	199	48	several	several	ADJ
fcis-2486	199	49	other	other	ADJ
fcis-2486	199	50	models	model	NOUN
fcis-2486	199	51	will	will	AUX
fcis-2486	199	52	produce	produce	VERB
fcis-2486	199	53	some	some	DET
fcis-2486	199	54	wrong	wrong	ADJ
fcis-2486	199	55	mesh	mesh	NOUN
fcis-2486	199	56	information	information	NOUN
fcis-2486	199	57	.	.	PUNCT
fcis-2486	200	1	lmafn	lmafn	PROPN
fcis-2486	200	2	can	can	AUX
fcis-2486	200	3	recover	recover	VERB
fcis-2486	200	4	the	the	DET
fcis-2486	200	5	texture	texture	ADJ
fcis-2486	200	6	information	information	NOUN
fcis-2486	200	7	of	of	ADP
fcis-2486	200	8	bird	bird	NOUN
fcis-2486	200	9	feathers	feather	NOUN
fcis-2486	200	10	better	well	ADV
fcis-2486	200	11	while	while	SCONJ
fcis-2486	200	12	the	the	DET
fcis-2486	200	13	edge	edge	NOUN
fcis-2486	200	14	is	be	AUX
fcis-2486	200	15	relatively	relatively	ADV
fcis-2486	200	16	clear	clear	ADJ
fcis-2486	200	17	.	.	PUNCT
fcis-2486	201	1	in	in	ADP
fcis-2486	201	2	contrast	contrast	NOUN
fcis-2486	201	3	,	,	PUNCT
fcis-2486	201	4	lmafn	lmafn	PROPN
fcis-2486	201	5	obtains	obtain	VERB
fcis-2486	201	6	sharper	sharp	ADJ
fcis-2486	201	7	results	result	NOUN
fcis-2486	201	8	and	and	CCONJ
fcis-2486	201	9	recovers	recover	VERB
fcis-2486	201	10	more	more	ADV
fcis-2486	201	11	high	high	ADJ
fcis-2486	201	12	contrast	contrast	NOUN
fcis-2486	201	13	and	and	CCONJ
fcis-2486	201	14	sharp	sharp	ADJ
fcis-2486	201	15	edges	edge	NOUN
fcis-2486	201	16	,	,	PUNCT
fcis-2486	201	17	and	and	CCONJ
fcis-2486	201	18	the	the	DET
fcis-2486	201	19	reconstruction	reconstruction	NOUN
fcis-2486	201	20	effect	effect	NOUN
fcis-2486	201	21	of	of	ADP
fcis-2486	201	22	the	the	DET
fcis-2486	201	23	algorithm	algorithm	NOUN
fcis-2486	201	24	is	be	AUX
fcis-2486	201	25	improved	improve	VERB
fcis-2486	201	26	to	to	ADP
fcis-2486	201	27	a	a	DET
fcis-2486	201	28	certain	certain	ADJ
fcis-2486	201	29	extent	extent	NOUN
fcis-2486	201	30	.	.	PUNCT
fcis-2486	202	1	to	to	PART
fcis-2486	202	2	illustrate	illustrate	VERB
fcis-2486	202	3	the	the	DET
fcis-2486	202	4	validity	validity	NOUN
fcis-2486	202	5	of	of	ADP
fcis-2486	202	6	our	our	PRON
fcis-2486	202	7	proposed	propose	VERB
fcis-2486	202	8	model	model	NOUN
fcis-2486	202	9	,	,	PUNCT
fcis-2486	202	10	we	we	PRON
fcis-2486	202	11	compared	compare	VERB
fcis-2486	202	12	the	the	DET
fcis-2486	202	13	super	super	NOUN
fcis-2486	202	14	-	-	NOUN
fcis-2486	202	15	resolution	resolution	NOUN
fcis-2486	202	16	(	(	PUNCT
fcis-2486	202	17	sr	sr	PROPN
fcis-2486	202	18	)	)	PUNCT
fcis-2486	202	19	reconstruction	reconstruction	NOUN
fcis-2486	202	20	results	result	NOUN
fcis-2486	202	21	of	of	ADP
fcis-2486	202	22	11	11	NUM
fcis-2486	202	23	advanced	advanced	ADJ
fcis-2486	202	24	sr	sr	PROPN
fcis-2486	202	25	models	model	NOUN
fcis-2486	202	26	based	base	VERB
fcis-2486	202	27	on	on	ADP
fcis-2486	202	28	deep	deep	ADJ
fcis-2486	202	29	learning	learning	NOUN
fcis-2486	202	30	,	,	PUNCT
fcis-2486	202	31	such	such	ADJ
fcis-2486	202	32	as	as	ADP
fcis-2486	202	33	srcnn	srcnn	NOUN
fcis-2486	202	34	[	[	X
fcis-2486	202	35	1	1	NUM
fcis-2486	202	36	]	]	PUNCT
fcis-2486	202	37	,	,	PUNCT
fcis-2486	202	38	drcn	drcn	ADJ
fcis-2486	202	39	[	[	X
fcis-2486	202	40	4	4	NUM
fcis-2486	202	41	]	]	PUNCT
fcis-2486	202	42	,	,	PUNCT
fcis-2486	202	43	lapsrn	lapsrn	VERB
fcis-2486	202	44	[	[	X
fcis-2486	202	45	7	7	NUM
fcis-2486	202	46	]	]	PUNCT
fcis-2486	202	47	,	,	PUNCT
fcis-2486	202	48	memnet	memnet	NOUN
fcis-2486	203	1	[	[	X
fcis-2486	203	2	27	27	NUM
fcis-2486	203	3	]	]	PUNCT
fcis-2486	203	4	,	,	PUNCT
fcis-2486	203	5	maffsrn	maffsrn	VERB
fcis-2486	204	1	[	[	X
fcis-2486	204	2	28	28	NUM
fcis-2486	204	3	]	]	X
fcis-2486	204	4	,	,	PUNCT
fcis-2486	204	5	in	in	ADP
fcis-2486	204	6	different	different	ADJ
fcis-2486	204	7	scales	scale	NOUN
fcis-2486	204	8	of	of	ADP
fcis-2486	204	9	five	five	NUM
fcis-2486	204	10	mainstream	mainstream	ADJ
fcis-2486	204	11	benchmark	benchmark	NOUN
fcis-2486	204	12	datasets	dataset	NOUN
fcis-2486	204	13	.	.	PUNCT
fcis-2486	205	1	the	the	DET
fcis-2486	205	2	experimental	experimental	ADJ
fcis-2486	205	3	results	result	NOUN
fcis-2486	205	4	are	be	AUX
fcis-2486	205	5	shown	show	VERB
fcis-2486	205	6	in	in	ADP
fcis-2486	205	7	table	table	NOUN
fcis-2486	205	8	3	3	NUM
fcis-2486	205	9	.	.	PUNCT
fcis-2486	206	1	the	the	DET
fcis-2486	206	2	best	good	ADJ
fcis-2486	206	3	results	result	NOUN
fcis-2486	206	4	have	have	AUX
fcis-2486	206	5	been	be	AUX
fcis-2486	206	6	bolded	bolde	VERB
fcis-2486	206	7	and	and	CCONJ
fcis-2486	206	8	underlined	underline	VERB
fcis-2486	206	9	.	.	PUNCT
fcis-2486	207	1	compared	compare	VERB
fcis-2486	207	2	with	with	ADP
fcis-2486	207	3	other	other	ADJ
fcis-2486	207	4	methods	method	NOUN
fcis-2486	207	5	,	,	PUNCT
fcis-2486	207	6	lmafn	lmafn	PROPN
fcis-2486	207	7	maintains	maintain	VERB
fcis-2486	207	8	better	well	ADJ
fcis-2486	207	9	accuracy	accuracy	NOUN
fcis-2486	207	10	while	while	SCONJ
fcis-2486	207	11	keeping	keep	VERB
fcis-2486	207	12	the	the	DET
fcis-2486	207	13	model	model	NOUN
fcis-2486	207	14	lightweight	lightweight	NOUN
fcis-2486	207	15	,	,	PUNCT
fcis-2486	207	16	and	and	CCONJ
fcis-2486	207	17	has	have	VERB
fcis-2486	207	18	the	the	DET
fcis-2486	207	19	best	good	ADJ
fcis-2486	207	20	comprehensive	comprehensive	ADJ
fcis-2486	207	21	performance	performance	NOUN
fcis-2486	207	22	.	.	PUNCT
fcis-2486	208	1	both	both	DET
fcis-2486	208	2	lmafn	lmafn	NOUN
fcis-2486	208	3	and	and	CCONJ
fcis-2486	208	4	maffsrn	maffsrn	NOUN
fcis-2486	208	5	[	[	X
fcis-2486	208	6	28	28	NUM
fcis-2486	208	7	]	]	PUNCT
fcis-2486	208	8	adopt	adopt	ADJ
fcis-2486	208	9	channel	channel	NOUN
fcis-2486	208	10	attention	attention	NOUN
fcis-2486	208	11	to	to	PART
fcis-2486	208	12	learn	learn	VERB
fcis-2486	208	13	the	the	DET
fcis-2486	208	14	interdependence	interdependence	NOUN
fcis-2486	208	15	among	among	ADP
fcis-2486	208	16	features	feature	NOUN
fcis-2486	208	17	,	,	PUNCT
fcis-2486	208	18	so	so	SCONJ
fcis-2486	208	19	that	that	SCONJ
fcis-2486	208	20	the	the	DET
fcis-2486	208	21	network	network	NOUN
fcis-2486	208	22	can	can	AUX
fcis-2486	208	23	focus	focus	VERB
fcis-2486	208	24	on	on	ADP
fcis-2486	208	25	more	more	ADV
fcis-2486	208	26	important	important	ADJ
fcis-2486	208	27	features	feature	NOUN
fcis-2486	208	28	,	,	PUNCT
fcis-2486	208	29	and	and	CCONJ
fcis-2486	208	30	thus	thus	ADV
fcis-2486	208	31	obtaining	obtain	VERB
fcis-2486	208	32	similar	similar	ADJ
fcis-2486	208	33	and	and	CCONJ
fcis-2486	208	34	better	well	ADJ
fcis-2486	208	35	results	result	NOUN
fcis-2486	208	36	than	than	ADP
fcis-2486	208	37	other	other	ADJ
fcis-2486	208	38	methods	method	NOUN
fcis-2486	208	39	.	.	PUNCT
fcis-2486	209	1	however	however	ADV
fcis-2486	209	2	,	,	PUNCT
fcis-2486	209	3	the	the	DET
fcis-2486	209	4	parameters	parameter	NOUN
fcis-2486	209	5	and	and	CCONJ
fcis-2486	209	6	multi	multi	NOUN
fcis-2486	209	7	-	-	ADJ
fcis-2486	209	8	adds	add	NOUN
fcis-2486	209	9	of	of	ADP
fcis-2486	209	10	our	our	PRON
fcis-2486	209	11	lmafn	lmafn	NOUN
fcis-2486	209	12	(	(	PUNCT
fcis-2486	209	13	×2	×2	NOUN
fcis-2486	209	14	)	)	PUNCT
fcis-2486	209	15	are	be	AUX
fcis-2486	209	16	reduced	reduce	VERB
fcis-2486	209	17	by	by	ADP
fcis-2486	209	18	about	about	ADV
fcis-2486	209	19	211.9k	211.9k	NUM
fcis-2486	209	20	and	and	CCONJ
fcis-2486	209	21	34.3	34.3	NUM
fcis-2486	209	22	g	g	NOUN
fcis-2486	209	23	respectively	respectively	ADV
fcis-2486	209	24	,	,	PUNCT
fcis-2486	209	25	which	which	PRON
fcis-2486	209	26	shows	show	VERB
fcis-2486	209	27	that	that	SCONJ
fcis-2486	209	28	our	our	PRON
fcis-2486	209	29	model	model	NOUN
fcis-2486	209	30	is	be	AUX
fcis-2486	209	31	more	more	ADV
fcis-2486	209	32	lightweight	lightweight	ADJ
fcis-2486	209	33	.	.	PUNCT
fcis-2486	210	1	compared	compare	VERB
fcis-2486	210	2	with	with	ADP
fcis-2486	210	3	another	another	DET
fcis-2486	210	4	lightweight	lightweight	ADJ
fcis-2486	210	5	model	model	NOUN
fcis-2486	210	6	,	,	PUNCT
fcis-2486	210	7	carn	carn	PROPN
fcis-2486	210	8	[	[	X
fcis-2486	210	9	21	21	NUM
fcis-2486	210	10	]	]	PUNCT
fcis-2486	210	11	,	,	PUNCT
fcis-2486	210	12	the	the	DET
fcis-2486	210	13	psnr	psnr	NOUN
fcis-2486	210	14	of	of	ADP
fcis-2486	210	15	our	our	PRON
fcis-2486	210	16	model	model	NOUN
fcis-2486	210	17	is	be	AUX
fcis-2486	210	18	0.08	0.08	NUM
fcis-2486	210	19	,	,	PUNCT
fcis-2486	210	20	0.05	0.05	NUM
fcis-2486	210	21	and	and	CCONJ
fcis-2486	210	22	0.16	0.16	NUM
fcis-2486	210	23	db	db	VERB
fcis-2486	210	24	higher	high	ADJ
fcis-2486	210	25	than	than	ADP
fcis-2486	210	26	that	that	PRON
fcis-2486	210	27	of	of	ADP
fcis-2486	210	28	carn	carn	NOUN
fcis-2486	210	29	on	on	ADP
fcis-2486	210	30	datasets	dataset	NOUN
fcis-2486	210	31	with	with	ADP
fcis-2486	210	32	more	more	ADJ
fcis-2486	210	33	texture	texture	ADJ
fcis-2486	210	34	information	information	NOUN
fcis-2486	210	35	(	(	PUNCT
fcis-2486	210	36	such	such	ADJ
fcis-2486	210	37	as	as	ADP
fcis-2486	210	38	set5	set5	PROPN
fcis-2486	210	39	,	,	PUNCT
fcis-2486	210	40	b100	b100	PROPN
fcis-2486	210	41	and	and	CCONJ
fcis-2486	210	42	urban100	urban100	PROPN
fcis-2486	210	43	)	)	PUNCT
fcis-2486	210	44	×	×	NOUN
fcis-2486	210	45	3	3	NUM
fcis-2486	210	46	scale	scale	NOUN
fcis-2486	210	47	respectively	respectively	ADV
fcis-2486	210	48	,	,	PUNCT
fcis-2486	210	49	though	though	ADV
fcis-2486	210	50	,	,	PUNCT
fcis-2486	210	51	the	the	DET
fcis-2486	210	52	results	result	NOUN
fcis-2486	210	53	on	on	ADP
fcis-2486	210	54	edge	edge	NOUN
fcis-2486	210	55	-	-	PUNCT
fcis-2486	210	56	informed	inform	VERB
fcis-2486	210	57	datasets	dataset	NOUN
fcis-2486	210	58	(	(	PUNCT
fcis-2486	210	59	such	such	ADJ
fcis-2486	210	60	as	as	ADP
fcis-2486	210	61	manga109	manga109	PROPN
fcis-2486	210	62	)	)	PUNCT
fcis-2486	210	63	are	be	AUX
fcis-2486	210	64	0.11db	0.11db	NUM
fcis-2486	210	65	lower	low	ADJ
fcis-2486	210	66	than	than	ADP
fcis-2486	210	67	carn	carn	PROPN
fcis-2486	210	68	,	,	PUNCT
fcis-2486	210	69	lmafn	lmafn	PROPN
fcis-2486	210	70	has	have	VERB
fcis-2486	210	71	about	about	ADV
fcis-2486	210	72	13	13	NUM
fcis-2486	210	73	%	%	NOUN
fcis-2486	210	74	of	of	ADP
fcis-2486	210	75	its	its	PRON
fcis-2486	210	76	parameters	parameter	NOUN
fcis-2486	210	77	and	and	CCONJ
fcis-2486	210	78	multi	multi	ADJ
fcis-2486	210	79	-	-	VERB
fcis-2486	210	80	add	add	VERB
fcis-2486	210	81	about	about	ADV
fcis-2486	210	82	20	20	NUM
fcis-2486	210	83	%	%	NOUN
fcis-2486	210	84	.	.	PUNCT
fcis-2486	211	1	texture	texture	ADJ
fcis-2486	211	2	information	information	NOUN
fcis-2486	211	3	is	be	AUX
fcis-2486	211	4	a	a	DET
fcis-2486	211	5	higher	high	ADJ
fcis-2486	211	6	-	-	PUNCT
fcis-2486	211	7	order	order	NOUN
fcis-2486	211	8	pattern	pattern	NOUN
fcis-2486	211	9	with	with	ADP
fcis-2486	211	10	more	more	ADV
fcis-2486	211	11	complex	complex	ADJ
fcis-2486	211	12	statistical	statistical	ADJ
fcis-2486	211	13	features	feature	NOUN
fcis-2486	211	14	,	,	PUNCT
fcis-2486	211	15	and	and	CCONJ
fcis-2486	211	16	edge	edge	NOUN
fcis-2486	211	17	information	information	NOUN
fcis-2486	211	18	is	be	AUX
fcis-2486	211	19	a	a	DET
fcis-2486	211	20	first	first	ADJ
fcis-2486	211	21	-	-	PUNCT
fcis-2486	211	22	order	order	NOUN
fcis-2486	211	23	pattern	pattern	NOUN
fcis-2486	211	24	that	that	PRON
fcis-2486	211	25	are	be	AUX
fcis-2486	211	26	able	able	ADJ
fcis-2486	211	27	to	to	PART
fcis-2486	211	28	be	be	AUX
fcis-2486	211	29	extracted	extract	VERB
fcis-2486	211	30	by	by	ADP
fcis-2486	211	31	a	a	DET
fcis-2486	211	32	first	first	ADJ
fcis-2486	211	33	-	-	PUNCT
fcis-2486	211	34	order	order	NOUN
fcis-2486	211	35	gradient	gradient	NOUN
fcis-2486	211	36	operator	operator	NOUN
fcis-2486	211	37	.	.	PUNCT
fcis-2486	212	1	therefore	therefore	ADV
fcis-2486	212	2	,	,	PUNCT
fcis-2486	212	3	lmafn	lmafn	PROPN
fcis-2486	212	4	has	have	VERB
fcis-2486	212	5	better	well	ADJ
fcis-2486	212	6	reconstruction	reconstruction	NOUN
fcis-2486	212	7	quality	quality	NOUN
fcis-2486	212	8	on	on	ADP
fcis-2486	212	9	images	image	NOUN
fcis-2486	212	10	with	with	ADP
fcis-2486	212	11	more	more	ADV
fcis-2486	212	12	high	high	ADJ
fcis-2486	212	13	-	-	PUNCT
fcis-2486	212	14	order	order	NOUN
fcis-2486	212	15	information	information	NOUN
fcis-2486	212	16	such	such	ADJ
fcis-2486	212	17	as	as	ADP
fcis-2486	212	18	textures	texture	NOUN
fcis-2486	212	19	.	.	PUNCT
fcis-2486	213	1	in	in	ADP
fcis-2486	213	2	summary	summary	NOUN
fcis-2486	213	3	,	,	PUNCT
fcis-2486	213	4	compared	compare	VERB
fcis-2486	213	5	with	with	ADP
fcis-2486	213	6	the	the	DET
fcis-2486	213	7	×3	×3	NOUN
fcis-2486	213	8	and	and	CCONJ
fcis-2486	213	9	×4	×4	NOUN
fcis-2486	213	10	scale	scale	NOUN
fcis-2486	213	11	,	,	PUNCT
fcis-2486	213	12	lmafn	lmafn	PROPN
fcis-2486	213	13	has	have	VERB
fcis-2486	213	14	more	more	ADJ
fcis-2486	213	15	obvious	obvious	ADJ
fcis-2486	213	16	advantages	advantage	NOUN
fcis-2486	213	17	on	on	ADP
fcis-2486	213	18	the	the	DET
fcis-2486	213	19	×2	×2	PROPN
fcis-2486	213	20	scale	scale	NOUN
fcis-2486	213	21	.	.	PUNCT
fcis-2486	214	1	fig	fig	NOUN
fcis-2486	215	1	.6	.6	NUM
fcis-2486	215	2	visual	visual	ADJ
fcis-2486	215	3	comparison	comparison	NOUN
fcis-2486	215	4	with	with	ADP
fcis-2486	215	5	other	other	ADJ
fcis-2486	215	6	image	image	NOUN
fcis-2486	215	7	super	super	ADJ
fcis-2486	215	8	-	-	ADJ
fcis-2486	215	9	resolution	resolution	ADJ
fcis-2486	215	10	models	model	NOUN
fcis-2486	215	11	on	on	ADP
fcis-2486	215	12	urban100	urban100	PROPN
fcis-2486	215	13	and	and	CCONJ
fcis-2486	215	14	b100	b100	PROPN
fcis-2486	215	15	datasets	dataset	VERB
fcis-2486	215	16	18	18	NUM
fcis-2486	215	17	table	table	NOUN
fcis-2486	215	18	3	3	NUM
fcis-2486	215	19	.	.	PUNCT
fcis-2486	215	20	comparison	comparison	NOUN
fcis-2486	215	21	of	of	ADP
fcis-2486	215	22	reconstruction	reconstruction	NOUN
fcis-2486	215	23	effects	effect	NOUN
fcis-2486	215	24	of	of	ADP
fcis-2486	215	25	different	different	ADJ
fcis-2486	215	26	image	image	NOUN
fcis-2486	215	27	super	super	ADJ
fcis-2486	215	28	-	-	ADJ
fcis-2486	215	29	resolution	resolution	ADJ
fcis-2486	215	30	models	model	NOUN
fcis-2486	215	31	models	model	NOUN
fcis-2486	215	32	scale	scale	NOUN
fcis-2486	215	33	params	param	NOUN
fcis-2486	215	34	multi	multi	PROPN
fcis-2486	215	35	adds	add	VERB
fcis-2486	215	36	set5	set5	PROPN
fcis-2486	215	37	set14	set14	PROPN
fcis-2486	215	38	b100	b100	PROPN
fcis-2486	215	39	urban100	urban100	PROPN
fcis-2486	215	40	manga109	manga109	PROPN
fcis-2486	215	41	psnr	psnr	NOUN
fcis-2486	215	42	/	/	SYM
fcis-2486	215	43	ssim	ssim	NOUN
fcis-2486	215	44	psnr	psnr	NOUN
fcis-2486	215	45	/	/	SYM
fcis-2486	215	46	ssim	ssim	NOUN
fcis-2486	215	47	psnr	psnr	NOUN
fcis-2486	215	48	/	/	SYM
fcis-2486	215	49	ssim	ssim	NOUN
fcis-2486	215	50	psnr	psnr	NOUN
fcis-2486	215	51	/	/	SYM
fcis-2486	215	52	ssim	ssim	NOUN
fcis-2486	215	53	psnr	psnr	NOUN
fcis-2486	215	54	/	/	SYM
fcis-2486	215	55	ssim	ssim	NOUN
fcis-2486	215	56	bicubic	bicubic	NOUN
fcis-2486	215	57	×2	×2	PROPN
fcis-2486	215	58	33.67/0.9291	33.67/0.9291	ADJ
fcis-2486	215	59	30.24/0.8677	30.24/0.8677	NUM
fcis-2486	215	60	29.56/0.8431	29.56/0.8431	NUM
fcis-2486	215	61	26.88/0.8423	26.88/0.8423	NOUN
fcis-2486	215	62	30.80/0.9337	30.80/0.9337	NUM
fcis-2486	215	63	srcnn	srcnn	NOUN
fcis-2486	215	64	57k	57k	PROPN
fcis-2486	215	65	52.7	52.7	NUM
fcis-2486	215	66	g	g	NOUN
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fcis-2486	215	68	32.38/0.9058	32.38/0.9058	NUM
fcis-2486	215	69	31.36/0.8876	31.36/0.8876	NUM
fcis-2486	215	70	29.51/0.8950	29.51/0.8950	NUM
fcis-2486	215	71	35.61/0.9663	35.61/0.9663	NUM
fcis-2486	215	72	fsrcnn	fsrcnn	NOUN
fcis-2486	215	73	12k	12k	PROPN
fcis-2486	216	1	6.0	6.0	NUM
fcis-2486	216	2	g	g	NOUN
fcis-2486	216	3	37.01/0.9556	37.01/0.9556	NUM
fcis-2486	216	4	32.68/0.9086	32.68/0.9086	NOUN
fcis-2486	216	5	31.53/0.8919	31.53/0.8919	NUM
fcis-2486	216	6	29.88/0.9020	29.88/0.9020	NUM
fcis-2486	216	7	36.65/0.9710	36.65/0.9710	NUM
fcis-2486	216	8	vdsr	vdsr	NOUN
fcis-2486	216	9	665k	665k	PROPN
fcis-2486	216	10	612.6	612.6	NUM
fcis-2486	216	11	g	g	PROPN
fcis-2486	216	12	37.53/0.9585	37.53/0.9585	NUM
fcis-2486	216	13	33.02/0.9122	33.02/0.9122	NUM
fcis-2486	216	14	31.90/0.8960	31.90/0.8960	NUM
fcis-2486	216	15	30.77/0.9143	30.77/0.9143	NOUN
fcis-2486	216	16	37.20/0/9746	37.20/0/9746	PROPN
fcis-2486	216	17	drcn	drcn	VERB
fcis-2486	216	18	1.7	1.7	NUM
fcis-2486	216	19	m	m	NOUN
fcis-2486	216	20	17.6	17.6	NUM
fcis-2486	216	21	t	t	NOUN
fcis-2486	216	22	37.63/0.9588	37.63/0.9588	NUM
fcis-2486	216	23	33.05/0.9117	33.05/0.9117	NUM
fcis-2486	216	24	31.85/0.8942	31.85/0.8942	NUM
fcis-2486	216	25	30.75/0.9136	30.75/0.9136	NUM
fcis-2486	216	26	37.56/0.9723	37.56/0.9723	NUM
fcis-2486	216	27	drrn	drrn	X
fcis-2486	216	28	297k	297k	NUM
fcis-2486	216	29	6.6	6.6	NUM
fcis-2486	216	30	t	t	NOUN
fcis-2486	216	31	37.74/0.9590	37.74/0.9590	NUM
fcis-2486	216	32	33.23/0.9136	33.23/0.9136	NUM
fcis-2486	217	1	32.05/0.8973	32.05/0.8973	NUM
fcis-2486	218	1	31.23/0.9188	31.23/0.9188	NUM
fcis-2486	218	2	37.88/0.9749	37.88/0.9749	NUM
fcis-2486	218	3	lapsrn	lapsrn	VERB
fcis-2486	218	4	251k	251k	NUM
fcis-2486	218	5	29.9	29.9	NUM
fcis-2486	218	6	g	g	NOUN
fcis-2486	218	7	37.52/0.9587	37.52/0.9587	ADJ
fcis-2486	218	8	33.05/0.9127	33.05/0.9127	NOUN
fcis-2486	218	9	31.87/0.8955	31.87/0.8955	NUM
fcis-2486	218	10	30.41/0.9103	30.41/0.9103	NUM
fcis-2486	218	11	37.27/0.9740	37.27/0.9740	NOUN
fcis-2486	218	12	carn	carn	PROPN
fcis-2486	218	13	1.6	1.6	NUM
fcis-2486	218	14	m	m	NOUN
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fcis-2486	218	18	33.52/0.9166	33.52/0.9166	NUM
fcis-2486	218	19	32.09/0.8978	32.09/0.8978	NUM
fcis-2486	218	20	31.92/0.9254	31.92/0.9254	NOUN
fcis-2486	218	21	38.40/0.9766	38.40/0.9766	PROPN
fcis-2486	218	22	memnet	memnet	NOUN
fcis-2486	218	23	678k	678k	PROPN
fcis-2486	218	24	2.6	2.6	NUM
fcis-2486	218	25	t	t	NOUN
fcis-2486	218	26	37.76/0.9589	37.76/0.9589	NUM
fcis-2486	218	27	33.28/0.9139	33.28/0.9139	ADJ
fcis-2486	218	28	32.04/0.8976	32.04/0.8976	NOUN
fcis-2486	218	29	31.31/0.9195	31.31/0.9195	NOUN
fcis-2486	218	30	37.69/0.9741	37.69/0.9741	NUM
fcis-2486	218	31	maffsrn	maffsrn	NOUN
fcis-2486	218	32	402k	402k	VERB
fcis-2486	218	33	77.2	77.2	NUM
fcis-2486	218	34	g	g	ADP
fcis-2486	218	35	37.89/0.9604	37.89/0.9604	NUM
fcis-2486	218	36	33.52/0.9170	33.52/0.9170	NUM
fcis-2486	218	37	32.14/0.8991	32.14/0.8991	NUM
fcis-2486	218	38	31.96/0.9268	31.96/0.9268	NUM
fcis-2486	218	39	awsrn	awsrn	NOUN
fcis-2486	218	40	-	-	PUNCT
fcis-2486	218	41	s	s	PART
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fcis-2486	218	44	g	g	NOUN
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fcis-2486	218	46	33.31/0.9151	33.31/0.9151	NUM
fcis-2486	218	47	32.00/0.8974	32.00/0.8974	NUM
fcis-2486	218	48	31.39/0.9207	31.39/0.9207	NOUN
fcis-2486	218	49	37.90/0.9755	37.90/0.9755	NUM
fcis-2486	218	50	lmafn	lmafn	VERB
fcis-2486	218	51	190.1k	190.1k	NUM
fcis-2486	218	52	42.9	42.9	NUM
fcis-2486	218	53	g	g	NOUN
fcis-2486	218	54	37.93/0.9607	37.93/0.9607	NUM
fcis-2486	218	55	33.52/0.9175	33.52/0.9175	NUM
fcis-2486	218	56	32.10/0.9010	32.10/0.9010	NOUN
fcis-2486	218	57	31.97/0.9267	31.97/0.9267	NUM
fcis-2486	218	58	38.45/0.9764	38.45/0.9764	NOUN
fcis-2486	218	59	bicubic	bicubic	NOUN
fcis-2486	218	60	×3	×3	NOUN
fcis-2486	218	61	30.41/0.8645	30.41/0.8645	NUM
fcis-2486	218	62	27.64/0.7722	27.64/0.7722	NUM
fcis-2486	218	63	27.21/0.7342	27.21/0.7342	NUM
fcis-2486	218	64	24.46/0.7401	24.46/0.7401	NUM
fcis-2486	218	65	26.96/0.8545	26.96/0.8545	NUM
fcis-2486	218	66	srcnn	srcnn	NOUN
fcis-2486	218	67	57k	57k	PROPN
fcis-2486	218	68	52.7	52.7	NUM
fcis-2486	218	69	g	g	ADP
fcis-2486	218	70	32.75/0.9090	32.75/0.9090	ADJ
fcis-2486	218	71	29.28/0.8210	29.28/0.8210	PROPN
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fcis-2486	218	73	26.25/0.7989	26.25/0.7989	ADJ
fcis-2486	218	74	30.54/0.9112	30.54/0.9112	NUM
fcis-2486	218	75	fsrcnn	fsrcnn	NOUN
fcis-2486	218	76	13k	13k	NOUN
fcis-2486	218	77	5.0	5.0	NUM
fcis-2486	218	78	g	g	ADP
fcis-2486	218	79	33.18/0.9101	33.18/0.9101	NUM
fcis-2486	218	80	29.42/0.821	29.42/0.821	NUM
fcis-2486	218	81	28.53/0.7910	28.53/0.7910	NUM
fcis-2486	218	82	26.30/0.8091	26.30/0.8091	NUM
fcis-2486	218	83	31.02/0.9210	31.02/0.9210	PROPN
fcis-2486	218	84	vdsr	vdsr	NOUN
fcis-2486	218	85	665k	665k	PROPN
fcis-2486	218	86	612.6	612.6	NUM
fcis-2486	218	87	g	g	NOUN
fcis-2486	218	88	33.68/0.9208	33.68/0.9208	NOUN
fcis-2486	218	89	29.80/0.8314	29.80/0.8314	NOUN
fcis-2486	218	90	28.80/0.7976	28.80/0.7976	NOUN
fcis-2486	218	91	27.14/0.8272	27.14/0.8272	NUM
fcis-2486	218	92	32.01/0.9340	32.01/0.9340	NUM
fcis-2486	218	93	drcn	drcn	ADJ
fcis-2486	218	94	1774k	1774k	PROPN
fcis-2486	218	95	17.6	17.6	NUM
fcis-2486	218	96	t	t	NOUN
fcis-2486	218	97	32.82/0.9224	32.82/0.9224	NUM
fcis-2486	218	98	29.76/0.8312	29.76/0.8312	NUM
fcis-2486	218	99	28.80/0.7962	28.80/0.7962	NUM
fcis-2486	218	100	27.15/0.8276	27.15/0.8276	NOUN
fcis-2486	218	101	32.27/0.9335	32.27/0.9335	NOUN
fcis-2486	218	102	drrn	drrn	ADJ
fcis-2486	218	103	298k	298k	PROPN
fcis-2486	218	104	6.64	6.64	NUM
fcis-2486	218	105	t	t	NOUN
fcis-2486	218	106	34.03/0.9243	34.03/0.9243	NUM
fcis-2486	218	107	29.96/0.8349	29.96/0.8349	NUM
fcis-2486	219	1	28.95/0.8005	28.95/0.8005	NUM
fcis-2486	219	2	27.53/0.8377	27.53/0.8377	NOUN
fcis-2486	219	3	32.72/0.9381	32.72/0.9381	NUM
fcis-2486	219	4	lapsrn	lapsrn	NOUN
fcis-2486	219	5	502k	502k	NOUN
fcis-2486	219	6	38.9	38.9	NUM
fcis-2486	219	7	g	g	ADP
fcis-2486	219	8	33.82/0.9218	33.82/0.9218	NOUN
fcis-2486	219	9	29.82/0.8316	29.82/0.8316	NUM
fcis-2486	219	10	28.82/0.7968	28.82/0.7968	NUM
fcis-2486	219	11	27.07/0.8281	27.07/0.8281	NUM
fcis-2486	219	12	32.21/0.9334	32.21/0.9334	NUM
fcis-2486	219	13	carn	carn	NOUN
fcis-2486	219	14	1.6	1.6	NUM
fcis-2486	219	15	m	m	NOUN
fcis-2486	219	16	118.8	118.8	NUM
fcis-2486	219	17	g	g	NOUN
fcis-2486	219	18	34.29/0.9254	34.29/0.9254	NUM
fcis-2486	219	19	30.29/0.8407	30.29/0.8407	NUM
fcis-2486	219	20	29.06/0.8034	29.06/0.8034	NUM
fcis-2486	219	21	28.05/0.8493	28.05/0.8493	NUM
fcis-2486	219	22	33.50/0.9440	33.50/0.9440	NUM
fcis-2486	219	23	memnet	memnet	NOUN
fcis-2486	219	24	678k	678k	PROPN
fcis-2486	219	25	2.6	2.6	NUM
fcis-2486	219	26	t	t	NOUN
fcis-2486	219	27	34.09/0.9247	34.09/0.9247	NUM
fcis-2486	219	28	30.02/0.8350	30.02/0.8350	NUM
fcis-2486	219	29	28.96/0.8003	28.96/0.8003	NUM
fcis-2486	220	1	27.56/0.8374	27.56/0.8374	NUM
fcis-2486	221	1	32.50/0.9366	32.50/0.9366	NUM
fcis-2486	221	2	maffsrn	maffsrn	NOUN
fcis-2486	221	3	418k	418k	NOUN
fcis-2486	221	4	34.2	34.2	NUM
fcis-2486	221	5	g	g	NOUN
fcis-2486	221	6	34.35/0.9269	34.35/0.9269	NUM
fcis-2486	221	7	30.35/0.8429	30.35/0.8429	NUM
fcis-2486	221	8	29.09/0.8052	29.09/0.8052	NUM
fcis-2486	221	9	28.13/0.8521	28.13/0.8521	NUM
fcis-2486	221	10	awsrn	awsrn	NOUN
fcis-2486	221	11	-	-	PUNCT
fcis-2486	221	12	s	s	PART
fcis-2486	221	13	477k	477k	NUM
fcis-2486	221	14	48.6	48.6	NUM
fcis-2486	221	15	g	g	ADP
fcis-2486	221	16	34.02/0.9240	34.02/0.9240	NOUN
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fcis-2486	221	18	28.92/0.8009	28.92/0.8009	NUM
fcis-2486	221	19	27.57/0.8391	27.57/0.8391	NUM
fcis-2486	221	20	32.82/0.9393	32.82/0.9393	NUM
fcis-2486	221	21	lmafn	lmafn	VERB
fcis-2486	221	22	262.3k	262.3k	NUM
fcis-2486	221	23	27.0	27.0	NUM
fcis-2486	221	24	g	g	PROPN
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fcis-2486	221	26	30.31/0.8437	30.31/0.8437	NUM
fcis-2486	221	27	29.11/0.8043	29.11/0.8043	NUM
fcis-2486	221	28	28.21/0.8520	28.21/0.8520	NUM
fcis-2486	221	29	33.39/0.9442	33.39/0.9442	NUM
fcis-2486	221	30	bicubic	bicubic	NOUN
fcis-2486	221	31	×4	×4	NOUN
fcis-2486	221	32	28.42/0.8052	28.42/0.8052	NUM
fcis-2486	221	33	26.10/0.6976	26.10/0.6976	NOUN
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fcis-2486	221	35	23.15/0.6589	23.15/0.6589	NUM
fcis-2486	221	36	24.89/0.7825	24.89/0.7825	NUM
fcis-2486	221	37	srcnn	srcnn	NOUN
fcis-2486	221	38	57k	57k	PROPN
fcis-2486	221	39	52.7	52.7	NUM
fcis-2486	221	40	g	g	NOUN
fcis-2486	221	41	30.49/0.8628	30.49/0.8628	NUM
fcis-2486	221	42	27.54/0.7514	27.54/0.7514	NUM
fcis-2486	221	43	26.90/0.7105	26.90/0.7105	NUM
fcis-2486	221	44	24.53/0.7226	24.53/0.7226	NUM
fcis-2486	221	45	27.62/0.8530	27.62/0.8530	NUM
fcis-2486	221	46	fsrcnn	fsrcnn	NOUN
fcis-2486	221	47	12k	12k	PROPN
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fcis-2486	221	49	g	g	NOUN
fcis-2486	221	50	30.72/0.8657	30.72/0.8657	NUM
fcis-2486	221	51	27.63/0.7532	27.63/0.7532	NUM
fcis-2486	221	52	26.98/0.7148	26.98/0.7148	NUM
fcis-2486	221	53	24.16/0.7277	24.16/0.7277	PROPN
fcis-2486	221	54	29.87/0.8563	29.87/0.8563	PROPN
fcis-2486	221	55	vdsr	vdsr	PROPN
fcis-2486	221	56	665k	665k	PROPN
fcis-2486	221	57	612.6	612.6	NUM
fcis-2486	221	58	g	g	NOUN
fcis-2486	221	59	31.35/0.8829	31.35/0.8829	NUM
fcis-2486	221	60	28.03/0.7678	28.03/0.7678	NUM
fcis-2486	221	61	27.29/0.7239	27.29/0.7239	NUM
fcis-2486	222	1	25.18/0.7530	25.18/0.7530	NUM
fcis-2486	222	2	28.76/0.8765	28.76/0.8765	NOUN
fcis-2486	222	3	drcn	drcn	ADJ
fcis-2486	222	4	1774k	1774k	NUM
fcis-2486	222	5	17.6	17.6	NUM
fcis-2486	222	6	t	t	PROPN
fcis-2486	222	7	31.54/0.8844	31.54/0.8844	NUM
fcis-2486	222	8	28.05/0.7663	28.05/0.7663	NUM
fcis-2486	222	9	27.23/0.7220	27.23/0.7220	NUM
fcis-2486	222	10	25.15/0.7510	25.15/0.7510	NUM
fcis-2486	222	11	28.96/0.8835	28.96/0.8835	NUM
fcis-2486	222	12	drrn	drrn	X
fcis-2486	222	13	297k	297k	NUM
fcis-2486	222	14	6.8	6.8	NUM
fcis-2486	222	15	t	t	NOUN
fcis-2486	222	16	31.69/0.8871	31.69/0.8871	NUM
fcis-2486	222	17	28.21/0.7720	28.21/0.7720	NUM
fcis-2486	223	1	27.38/0.7284	27.38/0.7284	NUM
fcis-2486	223	2	25.45/0.7638	25.45/0.7638	NOUN
fcis-2486	223	3	29.45/0.8953	29.45/0.8953	NOUN
fcis-2486	223	4	lapsrn	lapsrn	VERB
fcis-2486	223	5	813k	813k	NUM
fcis-2486	223	6	149.4	149.4	NUM
fcis-2486	223	7	g	g	NOUN
fcis-2486	223	8	31.54/0.8846	31.54/0.8846	NUM
fcis-2486	223	9	28.14/0.7745	28.14/0.7745	NUM
fcis-2486	223	10	27.32/0.7279	27.32/0.7279	NUM
fcis-2486	223	11	25.21/0.7560	25.21/0.7560	NUM
fcis-2486	223	12	29.02/0.8873	29.02/0.8873	ADJ
fcis-2486	223	13	carn	carn	PROPN
fcis-2486	223	14	1.6	1.6	NUM
fcis-2486	223	15	m	m	NOUN
fcis-2486	223	16	90.9	90.9	NUM
fcis-2486	223	17	g	g	NOUN
fcis-2486	223	18	32.13/0.8936	32.13/0.8936	NUM
fcis-2486	223	19	28.61/0.7806	28.61/0.7806	NUM
fcis-2486	223	20	27.57/0.7319	27.57/0.7319	NUM
fcis-2486	223	21	26.12/0.7837	26.12/0.7837	NUM
fcis-2486	223	22	30.38/0.9086	30.38/0.9086	NUM
fcis-2486	223	23	memnet	memnet	NOUN
fcis-2486	223	24	677k	677k	NUM
fcis-2486	223	25	2.6	2.6	NUM
fcis-2486	223	26	t	t	NOUN
fcis-2486	223	27	31.73/0.8901	31.73/0.8901	NUM
fcis-2486	223	28	28.26/0.7723	28.26/0.7723	SYM
fcis-2486	223	29	27.41/0.7289	27.41/0.7289	PROPN
fcis-2486	223	30	25.50/0.7627	25.50/0.7627	NUM
fcis-2486	223	31	29.02/0.8970	29.02/0.8970	NUM
fcis-2486	223	32	5	5	NUM
fcis-2486	223	33	.	.	PUNCT
fcis-2486	224	1	conclusion	conclusion	NOUN
fcis-2486	224	2	we	we	PRON
fcis-2486	224	3	propose	propose	VERB
fcis-2486	224	4	a	a	DET
fcis-2486	224	5	novel	novel	ADJ
fcis-2486	224	6	lightweight	lightweight	ADJ
fcis-2486	224	7	multi	multi	ADJ
fcis-2486	224	8	-	-	ADJ
fcis-2486	224	9	attention	attention	ADJ
fcis-2486	224	10	fusion	fusion	NOUN
fcis-2486	224	11	image	image	NOUN
fcis-2486	224	12	super	super	ADJ
fcis-2486	224	13	-	-	ADJ
fcis-2486	224	14	resolution	resolution	ADJ
fcis-2486	224	15	network	network	NOUN
fcis-2486	224	16	model	model	NOUN
fcis-2486	224	17	,	,	PUNCT
fcis-2486	224	18	lmafn	lmafn	PROPN
fcis-2486	224	19	.	.	PUNCT
fcis-2486	225	1	the	the	DET
fcis-2486	225	2	model	model	NOUN
fcis-2486	225	3	effectively	effectively	ADV
fcis-2486	225	4	obtains	obtain	VERB
fcis-2486	225	5	the	the	DET
fcis-2486	225	6	weight	weight	NOUN
fcis-2486	225	7	values	value	NOUN
fcis-2486	225	8	of	of	ADP
fcis-2486	225	9	different	different	ADJ
fcis-2486	225	10	features	feature	NOUN
fcis-2486	225	11	by	by	ADP
fcis-2486	225	12	integrating	integrate	VERB
fcis-2486	225	13	the	the	DET
fcis-2486	225	14	channel	channel	NOUN
fcis-2486	225	15	attention	attention	NOUN
fcis-2486	225	16	and	and	CCONJ
fcis-2486	225	17	the	the	DET
fcis-2486	225	18	spatial	spatial	ADJ
fcis-2486	225	19	attention	attention	NOUN
fcis-2486	225	20	while	while	SCONJ
fcis-2486	225	21	designing	design	VERB
fcis-2486	225	22	them	they	PRON
fcis-2486	225	23	in	in	ADP
fcis-2486	225	24	a	a	DET
fcis-2486	225	25	lightweight	lightweight	ADJ
fcis-2486	225	26	manner	manner	NOUN
fcis-2486	225	27	.	.	PUNCT
fcis-2486	226	1	for	for	ADP
fcis-2486	226	2	the	the	DET
fcis-2486	226	3	scab	scab	NOUN
fcis-2486	226	4	,	,	PUNCT
fcis-2486	226	5	two	two	NUM
fcis-2486	226	6	kinds	kind	NOUN
fcis-2486	226	7	of	of	ADP
fcis-2486	226	8	pooling	pooling	NOUN
fcis-2486	226	9	are	be	AUX
fcis-2486	226	10	introduced	introduce	VERB
fcis-2486	226	11	.	.	PUNCT
fcis-2486	227	1	compared	compare	VERB
fcis-2486	227	2	with	with	ADP
fcis-2486	227	3	single	single	ADJ
fcis-2486	227	4	pooling	pooling	NOUN
fcis-2486	227	5	,	,	PUNCT
fcis-2486	227	6	the	the	DET
fcis-2486	227	7	connection	connection	NOUN
fcis-2486	227	8	between	between	ADP
fcis-2486	227	9	each	each	DET
fcis-2486	227	10	channel	channel	NOUN
fcis-2486	227	11	is	be	AUX
fcis-2486	227	12	fully	fully	ADV
fcis-2486	227	13	considered	consider	VERB
fcis-2486	227	14	.	.	PUNCT
fcis-2486	228	1	it	it	PRON
fcis-2486	228	2	can	can	AUX
fcis-2486	228	3	extract	extract	VERB
fcis-2486	228	4	richer	rich	ADJ
fcis-2486	228	5	high	high	ADJ
fcis-2486	228	6	-	-	PUNCT
fcis-2486	228	7	level	level	NOUN
fcis-2486	228	8	features	feature	NOUN
fcis-2486	228	9	.	.	PUNCT
fcis-2486	229	1	the	the	DET
fcis-2486	229	2	experimental	experimental	ADJ
fcis-2486	229	3	results	result	NOUN
fcis-2486	229	4	imply	imply	VERB
fcis-2486	229	5	that	that	SCONJ
fcis-2486	229	6	lmafn	lmafn	PROPN
fcis-2486	229	7	improves	improve	VERB
fcis-2486	229	8	the	the	DET
fcis-2486	229	9	evaluation	evaluation	NOUN
fcis-2486	229	10	index	index	NOUN
fcis-2486	229	11	,	,	PUNCT
fcis-2486	229	12	while	while	SCONJ
fcis-2486	229	13	having	have	VERB
fcis-2486	229	14	better	well	ADJ
fcis-2486	229	15	results	result	NOUN
fcis-2486	229	16	in	in	ADP
fcis-2486	229	17	the	the	DET
fcis-2486	229	18	visual	visual	ADJ
fcis-2486	229	19	effect	effect	NOUN
fcis-2486	229	20	of	of	ADP
fcis-2486	229	21	the	the	DET
fcis-2486	229	22	reconstructed	reconstructed	ADJ
fcis-2486	229	23	image	image	NOUN
fcis-2486	229	24	.	.	PUNCT
fcis-2486	230	1	references	reference	NOUN
fcis-2486	230	2	[	[	X
fcis-2486	230	3	1	1	NUM
fcis-2486	230	4	]	]	X
fcis-2486	230	5	dong	dong	NOUN
fcis-2486	230	6	c	c	PROPN
fcis-2486	230	7	,	,	PUNCT
fcis-2486	230	8	loy	loy	PROPN
fcis-2486	230	9	c	c	PROPN
fcis-2486	230	10	c	c	X
fcis-2486	230	11	,	,	PUNCT
fcis-2486	230	12	he	he	PRON
fcis-2486	230	13	k	k	PROPN
fcis-2486	230	14	,	,	PUNCT
fcis-2486	230	15	et	et	PROPN
fcis-2486	230	16	al	al	PROPN
fcis-2486	230	17	.	.	PUNCT
fcis-2486	231	1	image	image	PROPN
fcis-2486	231	2	super	super	NOUN
fcis-2486	231	3	-	-	NOUN
fcis-2486	231	4	resolution	resolution	NOUN
fcis-2486	231	5	using	use	VERB
fcis-2486	231	6	deep	deep	ADJ
fcis-2486	231	7	convolutional	convolutional	ADJ
fcis-2486	231	8	networks[j	networks[j	NOUN
fcis-2486	231	9	]	]	X
fcis-2486	231	10	.	.	PUNCT
fcis-2486	232	1	ieee	ieee	NOUN
fcis-2486	232	2	transactions	transaction	NOUN
fcis-2486	232	3	on	on	ADP
fcis-2486	232	4	pattern	pattern	NOUN
fcis-2486	232	5	analysis	analysis	NOUN
fcis-2486	232	6	and	and	CCONJ
fcis-2486	232	7	machine	machine	NOUN
fcis-2486	232	8	intelligence	intelligence	NOUN
fcis-2486	232	9	,	,	PUNCT
fcis-2486	232	10	2015	2015	NUM
fcis-2486	232	11	,	,	PUNCT
fcis-2486	232	12	38(2	38(2	NUM
fcis-2486	232	13	):	):	PUNCT
fcis-2486	232	14	295	295	NUM
fcis-2486	232	15	-	-	SYM
fcis-2486	232	16	307	307	NUM
fcis-2486	232	17	.	.	PUNCT
fcis-2486	233	1	[	[	X
fcis-2486	233	2	2	2	NUM
fcis-2486	233	3	]	]	X
fcis-2486	233	4	dong	dong	NOUN
fcis-2486	233	5	c	c	PROPN
fcis-2486	233	6	,	,	PUNCT
fcis-2486	233	7	loy	loy	PROPN
fcis-2486	233	8	c	c	PROPN
fcis-2486	233	9	c	c	X
fcis-2486	233	10	,	,	PUNCT
fcis-2486	233	11	he	he	PRON
fcis-2486	233	12	k	k	PROPN
fcis-2486	233	13	,	,	PUNCT
fcis-2486	233	14	et	et	PROPN
fcis-2486	233	15	al	al	PROPN
fcis-2486	233	16	.	.	PUNCT
fcis-2486	234	1	learning	learn	VERB
fcis-2486	234	2	a	a	DET
fcis-2486	234	3	deep	deep	ADJ
fcis-2486	234	4	convolutional	convolutional	ADJ
fcis-2486	234	5	network	network	NOUN
fcis-2486	234	6	for	for	ADP
fcis-2486	234	7	image	image	NOUN
fcis-2486	234	8	super	super	ADJ
fcis-2486	234	9	-	-	ADJ
fcis-2486	234	10	resolution[c]//european	resolution[c]//european	ADJ
fcis-2486	234	11	conference	conference	NOUN
fcis-2486	234	12	on	on	ADP
fcis-2486	234	13	computer	computer	NOUN
fcis-2486	234	14	vision	vision	NOUN
fcis-2486	234	15	.	.	PUNCT
fcis-2486	235	1	springer	springer	NOUN
fcis-2486	235	2	,	,	PUNCT
fcis-2486	235	3	cham	cham	PROPN
fcis-2486	235	4	,	,	PUNCT
fcis-2486	235	5	2014	2014	NUM
fcis-2486	235	6	:	:	PUNCT
fcis-2486	235	7	184	184	NUM
fcis-2486	235	8	-	-	SYM
fcis-2486	235	9	199	199	NUM
fcis-2486	235	10	.	.	PUNCT
fcis-2486	236	1	[	[	X
fcis-2486	236	2	3	3	X
fcis-2486	236	3	]	]	X
fcis-2486	236	4	dong	dong	NOUN
fcis-2486	236	5	c	c	PROPN
fcis-2486	236	6	,	,	PUNCT
fcis-2486	236	7	loy	loy	PROPN
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fcis-2486	236	9	c	c	PROPN
fcis-2486	236	10	,	,	PUNCT
fcis-2486	236	11	tang	tang	X
fcis-2486	236	12	x.	x.	NOUN
fcis-2486	236	13	accelerating	accelerate	VERB
fcis-2486	236	14	the	the	DET
fcis-2486	236	15	super	super	ADJ
fcis-2486	236	16	-	-	ADJ
fcis-2486	236	17	resolution	resolution	ADJ
fcis-2486	236	18	convolutional	convolutional	ADJ
fcis-2486	236	19	neural	neural	ADJ
fcis-2486	236	20	network[c]//european	network[c]//european	PROPN
fcis-2486	236	21	conference	conference	NOUN
fcis-2486	236	22	on	on	ADP
fcis-2486	236	23	computer	computer	NOUN
fcis-2486	236	24	vision	vision	NOUN
fcis-2486	236	25	.	.	PUNCT
fcis-2486	237	1	springer	springer	NOUN
fcis-2486	237	2	,	,	PUNCT
fcis-2486	237	3	cham	cham	PROPN
fcis-2486	237	4	,	,	PUNCT
fcis-2486	237	5	2016	2016	NUM
fcis-2486	237	6	:	:	PUNCT
fcis-2486	237	7	391	391	NUM
fcis-2486	237	8	-	-	SYM
fcis-2486	237	9	407	407	NUM
fcis-2486	237	10	.	.	PUNCT
fcis-2486	238	1	[	[	X
fcis-2486	238	2	4	4	X
fcis-2486	238	3	]	]	X
fcis-2486	238	4	kim	kim	PROPN
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fcis-2486	238	6	,	,	PUNCT
fcis-2486	238	7	lee	lee	PROPN
fcis-2486	238	8	j	j	PROPN
fcis-2486	238	9	k	k	PROPN
fcis-2486	238	10	,	,	PUNCT
fcis-2486	238	11	lee	lee	PROPN
fcis-2486	238	12	k	k	PROPN
fcis-2486	238	13	m.	m.	PROPN
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fcis-2486	238	15	-	-	PUNCT
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fcis-2486	238	17	convolutional	convolutional	ADJ
fcis-2486	238	18	network	network	NOUN
fcis-2486	238	19	for	for	ADP
fcis-2486	238	20	image	image	NOUN
fcis-2486	238	21	super	super	NOUN
fcis-2486	238	22	-	-	NOUN
fcis-2486	238	23	resolution[c]//proceedings	resolution[c]//proceeding	NOUN
fcis-2486	238	24	of	of	ADP
fcis-2486	238	25	the	the	DET
fcis-2486	238	26	ieee	ieee	NOUN
fcis-2486	238	27	conference	conference	NOUN
fcis-2486	238	28	on	on	ADP
fcis-2486	238	29	computer	computer	NOUN
fcis-2486	238	30	vision	vision	NOUN
fcis-2486	238	31	and	and	CCONJ
fcis-2486	238	32	pattern	pattern	NOUN
fcis-2486	238	33	recognition	recognition	NOUN
fcis-2486	238	34	.	.	PUNCT
fcis-2486	239	1	2016	2016	NUM
fcis-2486	239	2	:	:	PUNCT
fcis-2486	239	3	1637	1637	NUM
fcis-2486	239	4	-	-	SYM
fcis-2486	239	5	1645	1645	NUM
fcis-2486	239	6	.	.	PUNCT
fcis-2486	240	1	[	[	X
fcis-2486	240	2	5	5	NUM
fcis-2486	240	3	]	]	PUNCT
fcis-2486	240	4	tai	tai	PROPN
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fcis-2486	240	6	,	,	PUNCT
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fcis-2486	250	2	:	:	PUNCT
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fcis-2486	253	2	:	:	PUNCT
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fcis-2486	265	29	and	and	CCONJ
fcis-2486	265	30	pattern	pattern	NOUN
fcis-2486	265	31	recognition	recognition	NOUN
fcis-2486	265	32	workshops	workshop	NOUN
fcis-2486	265	33	.	.	PUNCT
fcis-2486	266	1	2017	2017	NUM
fcis-2486	266	2	:	:	PUNCT
fcis-2486	266	3	126	126	NUM
fcis-2486	266	4	-	-	SYM
fcis-2486	266	5	135	135	NUM
fcis-2486	266	6	.	.	PUNCT
fcis-2486	267	1	[	[	X
fcis-2486	267	2	16	16	NUM
fcis-2486	267	3	]	]	PUNCT
fcis-2486	267	4	bevilacqua	bevilacqua	NOUN
fcis-2486	267	5	m	m	PROPN
fcis-2486	267	6	,	,	PUNCT
fcis-2486	267	7	roumy	roumy	VERB
fcis-2486	267	8	a	a	DET
fcis-2486	267	9	,	,	PUNCT
fcis-2486	267	10	guillemot	guillemot	NOUN
fcis-2486	267	11	c	c	NOUN
fcis-2486	267	12	,	,	PUNCT
fcis-2486	267	13	et	et	PROPN
fcis-2486	267	14	al	al	PROPN
fcis-2486	267	15	.	.	PUNCT
fcis-2486	267	16	low	low	ADJ
fcis-2486	267	17	-	-	PUNCT
fcis-2486	267	18	complexity	complexity	NOUN
fcis-2486	267	19	single	single	ADJ
fcis-2486	267	20	-	-	PUNCT
fcis-2486	267	21	image	image	NOUN
fcis-2486	267	22	super	super	NOUN
fcis-2486	267	23	-	-	NOUN
fcis-2486	267	24	resolution	resolution	NOUN
fcis-2486	267	25	based	base	VERB
fcis-2486	267	26	on	on	ADP
fcis-2486	267	27	nonnegative	nonnegative	ADJ
fcis-2486	267	28	neighbor	neighbor	NOUN
fcis-2486	267	29	embedding[j	embedding[j	PROPN
fcis-2486	267	30	]	]	PUNCT
fcis-2486	267	31	.	.	PUNCT
fcis-2486	268	1	2012	2012	NUM
fcis-2486	268	2	.	.	PUNCT
fcis-2486	269	1	[	[	X
fcis-2486	269	2	17	17	NUM
fcis-2486	269	3	]	]	X
fcis-2486	269	4	fan	fan	PROPN
fcis-2486	269	5	y	y	PROPN
fcis-2486	269	6	,	,	PUNCT
fcis-2486	269	7	shi	shi	PROPN
fcis-2486	269	8	h	h	PROPN
fcis-2486	269	9	,	,	PUNCT
fcis-2486	269	10	yu	yu	PROPN
fcis-2486	269	11	j	j	PROPN
fcis-2486	269	12	,	,	PUNCT
fcis-2486	269	13	et	et	PROPN
fcis-2486	269	14	al	al	PROPN
fcis-2486	269	15	.	.	PROPN
fcis-2486	269	16	balanced	balance	VERB
fcis-2486	269	17	two	two	NUM
fcis-2486	269	18	-	-	PUNCT
fcis-2486	269	19	stage	stage	NOUN
fcis-2486	269	20	residual	residual	ADJ
fcis-2486	269	21	networks	network	NOUN
fcis-2486	269	22	for	for	ADP
fcis-2486	269	23	image	image	NOUN
fcis-2486	269	24	super	super	NOUN
fcis-2486	269	25	-	-	NOUN
fcis-2486	269	26	resolution[c]//proceedings	resolution[c]//proceeding	NOUN
fcis-2486	269	27	of	of	ADP
fcis-2486	269	28	the	the	DET
fcis-2486	269	29	ieee	ieee	NOUN
fcis-2486	269	30	conference	conference	NOUN
fcis-2486	269	31	on	on	ADP
fcis-2486	269	32	computer	computer	NOUN
fcis-2486	269	33	vision	vision	NOUN
fcis-2486	269	34	and	and	CCONJ
fcis-2486	269	35	pattern	pattern	NOUN
fcis-2486	269	36	recognition	recognition	NOUN
fcis-2486	269	37	workshops	workshop	NOUN
fcis-2486	269	38	.	.	PUNCT
fcis-2486	270	1	2017	2017	NUM
fcis-2486	270	2	:	:	PUNCT
fcis-2486	270	3	161	161	NUM
fcis-2486	270	4	-	-	SYM
fcis-2486	270	5	168	168	NUM
fcis-2486	270	6	.	.	PUNCT
fcis-2486	271	1	[	[	X
fcis-2486	271	2	18	18	NUM
fcis-2486	271	3	]	]	PUNCT
fcis-2486	271	4	matsui	matsui	PROPN
fcis-2486	271	5	y	y	PROPN
fcis-2486	271	6	,	,	PUNCT
fcis-2486	271	7	ito	ito	PROPN
fcis-2486	271	8	k	k	PROPN
fcis-2486	271	9	,	,	PUNCT
fcis-2486	271	10	aramaki	aramaki	PROPN
fcis-2486	271	11	y	y	PROPN
fcis-2486	271	12	,	,	PUNCT
fcis-2486	271	13	et	et	PROPN
fcis-2486	271	14	al	al	PROPN
fcis-2486	271	15	.	.	PROPN
fcis-2486	271	16	sketch	sketch	NOUN
fcis-2486	271	17	-	-	PUNCT
fcis-2486	271	18	based	base	VERB
fcis-2486	271	19	manga	manga	NOUN
fcis-2486	271	20	retrieval	retrieval	NOUN
fcis-2486	271	21	using	use	VERB
fcis-2486	271	22	manga109	manga109	PROPN
fcis-2486	271	23	dataset[j	dataset[j	PROPN
fcis-2486	271	24	]	]	PUNCT
fcis-2486	271	25	.	.	PUNCT
fcis-2486	272	1	multimedia	multimedia	NOUN
fcis-2486	272	2	tools	tool	NOUN
fcis-2486	272	3	and	and	CCONJ
fcis-2486	272	4	applications	application	NOUN
fcis-2486	272	5	,	,	PUNCT
fcis-2486	272	6	2017	2017	NUM
fcis-2486	272	7	,	,	PUNCT
fcis-2486	272	8	76(20	76(20	NUM
fcis-2486	272	9	):	):	PUNCT
fcis-2486	272	10	21811	21811	NUM
fcis-2486	272	11	-	-	SYM
fcis-2486	272	12	21838	21838	NUM
fcis-2486	272	13	.	.	PUNCT
fcis-2486	273	1	[	[	X
fcis-2486	273	2	19	19	NUM
fcis-2486	273	3	]	]	X
fcis-2486	273	4	wang	wang	PROPN
fcis-2486	273	5	z	z	PROPN
fcis-2486	273	6	,	,	PUNCT
fcis-2486	273	7	bovik	bovik	ADV
fcis-2486	273	8	a	a	DET
fcis-2486	273	9	c	c	NOUN
fcis-2486	273	10	,	,	PUNCT
fcis-2486	273	11	sheikh	sheikh	NOUN
fcis-2486	273	12	h	h	PROPN
fcis-2486	273	13	r	r	PROPN
fcis-2486	273	14	,	,	PUNCT
fcis-2486	273	15	et	et	PROPN
fcis-2486	273	16	al	al	PROPN
fcis-2486	273	17	.	.	PUNCT
fcis-2486	273	18	image	image	NOUN
fcis-2486	273	19	quality	quality	NOUN
fcis-2486	273	20	assessment	assessment	NOUN
fcis-2486	273	21	:	:	PUNCT
fcis-2486	273	22	from	from	ADP
fcis-2486	273	23	error	error	NOUN
fcis-2486	273	24	visibility	visibility	NOUN
fcis-2486	273	25	to	to	ADP
fcis-2486	273	26	structural	structural	ADJ
fcis-2486	273	27	similarity[j	similarity[j	PROPN
fcis-2486	273	28	]	]	PUNCT
fcis-2486	273	29	.	.	PUNCT
fcis-2486	274	1	ieee	ieee	NOUN
fcis-2486	274	2	transactions	transaction	NOUN
fcis-2486	274	3	on	on	ADP
fcis-2486	274	4	image	image	NOUN
fcis-2486	274	5	processing	processing	NOUN
fcis-2486	274	6	,	,	PUNCT
fcis-2486	274	7	2004	2004	NUM
fcis-2486	274	8	,	,	PUNCT
fcis-2486	274	9	13(4	13(4	NUM
fcis-2486	274	10	):	):	PUNCT
fcis-2486	274	11	600	600	NUM
fcis-2486	274	12	-	-	NUM
fcis-2486	274	13	612	612	NUM
fcis-2486	274	14	.	.	PUNCT
fcis-2486	275	1	[	[	X
fcis-2486	275	2	20	20	NUM
fcis-2486	275	3	]	]	SYM
fcis-2486	275	4	li	li	PROPN
fcis-2486	275	5	j	j	PROPN
fcis-2486	275	6	,	,	PUNCT
fcis-2486	275	7	fang	fang	PROPN
fcis-2486	275	8	f	f	PROPN
fcis-2486	275	9	,	,	PUNCT
fcis-2486	275	10	mei	mei	PROPN
fcis-2486	275	11	k	k	PROPN
fcis-2486	275	12	,	,	PUNCT
fcis-2486	275	13	et	et	PROPN
fcis-2486	275	14	al	al	PROPN
fcis-2486	275	15	.	.	PUNCT
fcis-2486	275	16	multi	multi	ADJ
fcis-2486	275	17	-	-	ADJ
fcis-2486	275	18	scale	scale	ADJ
fcis-2486	275	19	residual	residual	ADJ
fcis-2486	275	20	network	network	NOUN
fcis-2486	275	21	for	for	ADP
fcis-2486	275	22	image	image	NOUN
fcis-2486	275	23	super	super	NOUN
fcis-2486	275	24	-	-	NOUN
fcis-2486	275	25	resolution[c]//proceedings	resolution[c]//proceeding	NOUN
fcis-2486	275	26	of	of	ADP
fcis-2486	275	27	the	the	DET
fcis-2486	275	28	european	european	PROPN
fcis-2486	275	29	conference	conference	PROPN
fcis-2486	275	30	on	on	ADP
fcis-2486	275	31	computer	computer	NOUN
fcis-2486	275	32	vision	vision	NOUN
fcis-2486	275	33	(	(	PUNCT
fcis-2486	275	34	eccv	eccv	ADV
fcis-2486	275	35	)	)	PUNCT
fcis-2486	275	36	.	.	PUNCT
fcis-2486	276	1	2018	2018	NUM
fcis-2486	276	2	:	:	PUNCT
fcis-2486	276	3	517	517	NUM
fcis-2486	276	4	-	-	SYM
fcis-2486	276	5	532	532	NUM
fcis-2486	276	6	.	.	PUNCT
fcis-2486	277	1	[	[	X
fcis-2486	277	2	21	21	NUM
fcis-2486	277	3	]	]	X
fcis-2486	277	4	ahn	ahn	PROPN
fcis-2486	277	5	n	n	PROPN
fcis-2486	277	6	,	,	PUNCT
fcis-2486	277	7	kang	kang	PROPN
fcis-2486	277	8	b	b	PROPN
fcis-2486	277	9	,	,	PUNCT
fcis-2486	277	10	sohn	sohn	PROPN
fcis-2486	277	11	k	k	PROPN
fcis-2486	277	12	a.	a.	PROPN
fcis-2486	277	13	fast	fast	ADV
fcis-2486	277	14	,	,	PUNCT
fcis-2486	277	15	accurate	accurate	ADJ
fcis-2486	277	16	,	,	PUNCT
fcis-2486	277	17	and	and	CCONJ
fcis-2486	277	18	lightweight	lightweight	ADJ
fcis-2486	277	19	super	super	NOUN
fcis-2486	277	20	-	-	NOUN
fcis-2486	277	21	resolution	resolution	NOUN
fcis-2486	277	22	with	with	ADP
fcis-2486	277	23	cascading	cascade	VERB
fcis-2486	277	24	residual	residual	ADJ
fcis-2486	277	25	network[c]//proceedings	network[c]//proceeding	NOUN
fcis-2486	277	26	of	of	ADP
fcis-2486	277	27	the	the	DET
fcis-2486	277	28	european	european	ADJ
fcis-2486	277	29	conference	conference	PROPN
fcis-2486	277	30	on	on	ADP
fcis-2486	277	31	computer	computer	NOUN
fcis-2486	277	32	vision	vision	NOUN
fcis-2486	277	33	(	(	PUNCT
fcis-2486	277	34	eccv	eccv	ADV
fcis-2486	277	35	)	)	PUNCT
fcis-2486	277	36	.	.	PUNCT
fcis-2486	278	1	2018	2018	NUM
fcis-2486	278	2	:	:	PUNCT
fcis-2486	278	3	252	252	NUM
fcis-2486	278	4	-	-	SYM
fcis-2486	278	5	268	268	NUM
fcis-2486	278	6	.	.	PUNCT
fcis-2486	279	1	[	[	X
fcis-2486	279	2	22	22	NUM
fcis-2486	279	3	]	]	X
fcis-2486	279	4	lu	lu	PROPN
fcis-2486	279	5	y	y	PROPN
fcis-2486	279	6	,	,	PUNCT
fcis-2486	279	7	zhou	zhou	PROPN
fcis-2486	279	8	y	y	PROPN
fcis-2486	279	9	,	,	PUNCT
fcis-2486	279	10	jiang	jiang	PROPN
fcis-2486	280	1	z	z	PROPN
fcis-2486	280	2	,	,	PUNCT
fcis-2486	280	3	et	et	PROPN
fcis-2486	280	4	al	al	PROPN
fcis-2486	280	5	.	.	PROPN
fcis-2486	280	6	channel	channel	PROPN
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fcis-2486	280	8	and	and	CCONJ
fcis-2486	280	9	multi	multi	ADJ
fcis-2486	280	10	-	-	ADJ
fcis-2486	280	11	level	level	ADJ
fcis-2486	280	12	features	feature	NOUN
fcis-2486	280	13	fusion	fusion	NOUN
fcis-2486	280	14	for	for	ADP
fcis-2486	280	15	single	single	ADJ
fcis-2486	280	16	image	image	NOUN
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fcis-2486	280	19	resolution[c]//2018	resolution[c]//2018	PRON
fcis-2486	280	20	ieee	ieee	NOUN
fcis-2486	280	21	visual	visual	ADJ
fcis-2486	280	22	communications	communication	NOUN
fcis-2486	280	23	and	and	CCONJ
fcis-2486	280	24	image	image	NOUN
fcis-2486	280	25	processing	processing	NOUN
fcis-2486	280	26	(	(	PUNCT
fcis-2486	280	27	vcip	vcip	PROPN
fcis-2486	280	28	)	)	PUNCT
fcis-2486	280	29	.	.	PUNCT
fcis-2486	281	1	ieee	ieee	NOUN
fcis-2486	281	2	,	,	PUNCT
fcis-2486	281	3	2018	2018	NUM
fcis-2486	281	4	:	:	PUNCT
fcis-2486	281	5	1	1	NUM
fcis-2486	281	6	-	-	SYM
fcis-2486	281	7	4	4	NUM
fcis-2486	281	8	.	.	PUNCT
fcis-2486	282	1	[	[	X
fcis-2486	282	2	23	23	NUM
fcis-2486	282	3	]	]	X
fcis-2486	282	4	hui	hui	PROPN
fcis-2486	282	5	z	z	PROPN
fcis-2486	282	6	,	,	PUNCT
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fcis-2486	282	8	x	x	PROPN
fcis-2486	282	9	,	,	PUNCT
fcis-2486	282	10	yang	yang	PROPN
fcis-2486	282	11	y	y	PROPN
fcis-2486	282	12	,	,	PUNCT
fcis-2486	282	13	et	et	PROPN
fcis-2486	282	14	al	al	PROPN
fcis-2486	282	15	.	.	PUNCT
fcis-2486	283	1	lightweight	lightweight	ADJ
fcis-2486	283	2	image	image	NOUN
fcis-2486	283	3	superresolution	superresolution	NOUN
fcis-2486	283	4	with	with	ADP
fcis-2486	283	5	information	information	NOUN
fcis-2486	283	6	multi	multi	ADJ
fcis-2486	283	7	-	-	ADJ
fcis-2486	283	8	distillation	distillation	ADJ
fcis-2486	283	9	network[c]//proceedings	network[c]//proceeding	NOUN
fcis-2486	283	10	of	of	ADP
fcis-2486	283	11	the	the	DET
fcis-2486	283	12	27th	27th	ADJ
fcis-2486	283	13	acm	acm	PROPN
fcis-2486	283	14	international	international	ADJ
fcis-2486	283	15	conference	conference	NOUN
fcis-2486	283	16	on	on	ADP
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fcis-2486	283	18	.	.	PUNCT
fcis-2486	284	1	2019	2019	NUM
fcis-2486	284	2	:	:	PUNCT
fcis-2486	284	3	2024	2024	NUM
fcis-2486	284	4	-	-	SYM
fcis-2486	284	5	2032	2032	NUM
fcis-2486	284	6	.	.	PUNCT
fcis-2486	285	1	national	national	ADJ
fcis-2486	285	2	conference	conference	NOUN
fcis-2486	285	3	on	on	ADP
fcis-2486	285	4	multimedia	multimedia	NOUN
fcis-2486	285	5	.	.	PUNCT
fcis-2486	286	1	2019	2019	NUM
fcis-2486	286	2	:	:	PUNCT
fcis-2486	286	3	2024	2024	NUM
fcis-2486	286	4	-	-	SYM
fcis-2486	286	5	2032	2032	NUM
fcis-2486	286	6	.	.	PUNCT
fcis-2486	287	1	[	[	X
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fcis-2486	287	3	]	]	X
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fcis-2486	287	5	z	z	PROPN
fcis-2486	287	6	,	,	PUNCT
fcis-2486	287	7	wang	wang	PROPN
fcis-2486	287	8	x	x	PROPN
fcis-2486	287	9	,	,	PUNCT
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fcis-2486	287	16	image	image	NOUN
fcis-2486	287	17	superresolution	superresolution	NOUN
fcis-2486	287	18	via	via	ADP
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fcis-2486	287	21	network[c]//proceedings	network[c]//proceeding	NOUN
fcis-2486	287	22	of	of	ADP
fcis-2486	287	23	the	the	DET
fcis-2486	287	24	ieee	ieee	NOUN
fcis-2486	287	25	conference	conference	NOUN
fcis-2486	287	26	on	on	ADP
fcis-2486	287	27	computer	computer	NOUN
fcis-2486	287	28	vision	vision	NOUN
fcis-2486	287	29	and	and	CCONJ
fcis-2486	287	30	pattern	pattern	NOUN
fcis-2486	287	31	recognition	recognition	NOUN
fcis-2486	287	32	.	.	PUNCT
fcis-2486	288	1	2018	2018	NUM
fcis-2486	288	2	:	:	PUNCT
fcis-2486	289	1	723	723	NUM
fcis-2486	289	2	-	-	SYM
fcis-2486	289	3	731	731	NUM
fcis-2486	289	4	.	.	PUNCT
fcis-2486	290	1	[	[	X
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fcis-2486	290	3	]	]	X
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fcis-2486	290	6	,	,	PUNCT
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fcis-2486	290	8	z	z	PROPN
fcis-2486	290	9	,	,	PUNCT
fcis-2486	290	10	shi	shi	PROPN
fcis-2486	290	11	j.	j.	PROPN
fcis-2486	290	12	lightweight	lightweight	PROPN
fcis-2486	290	13	image	image	NOUN
fcis-2486	290	14	super	super	NOUN
fcis-2486	290	15	-	-	NOUN
fcis-2486	290	16	resolution	resolution	NOUN
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fcis-2486	290	18	adaptive	adaptive	ADJ
fcis-2486	290	19	weighted	weight	VERB
fcis-2486	290	20	learning	learn	VERB
fcis-2486	290	21	network[j	network[j	PROPN
fcis-2486	290	22	]	]	PUNCT
fcis-2486	290	23	.	.	PUNCT
fcis-2486	291	1	arxiv	arxiv	PROPN
fcis-2486	291	2	preprint	preprint	VERB
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fcis-2486	291	4	,	,	PUNCT
fcis-2486	291	5	2019	2019	NUM
fcis-2486	291	6	.	.	PUNCT
fcis-2486	292	1	[	[	X
fcis-2486	292	2	26	26	NUM
fcis-2486	292	3	]	]	X
fcis-2486	292	4	dai	dai	PROPN
fcis-2486	292	5	t	t	PROPN
fcis-2486	292	6	,	,	PUNCT
fcis-2486	292	7	cai	cai	PROPN
fcis-2486	292	8	j	j	PROPN
fcis-2486	292	9	,	,	PUNCT
fcis-2486	292	10	zhang	zhang	PROPN
fcis-2486	292	11	y	y	PROPN
fcis-2486	292	12	,	,	PUNCT
fcis-2486	292	13	et	et	PROPN
fcis-2486	292	14	al	al	PROPN
fcis-2486	292	15	.	.	PUNCT
fcis-2486	292	16	second	second	ADJ
fcis-2486	292	17	-	-	PUNCT
fcis-2486	292	18	order	order	NOUN
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fcis-2486	292	22	single	single	ADJ
fcis-2486	292	23	image	image	NOUN
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fcis-2486	292	27	of	of	ADP
fcis-2486	292	28	the	the	DET
fcis-2486	292	29	ieee	ieee	NOUN
fcis-2486	292	30	/	/	SYM
fcis-2486	292	31	cvf	cvf	NOUN
fcis-2486	292	32	conference	conference	NOUN
fcis-2486	292	33	on	on	ADP
fcis-2486	292	34	computer	computer	NOUN
fcis-2486	292	35	vision	vision	NOUN
fcis-2486	292	36	and	and	CCONJ
fcis-2486	292	37	pattern	pattern	NOUN
fcis-2486	292	38	recognition	recognition	NOUN
fcis-2486	292	39	.	.	PUNCT
fcis-2486	293	1	2019	2019	NUM
fcis-2486	293	2	:	:	PUNCT
fcis-2486	293	3	11065	11065	NUM
fcis-2486	293	4	-	-	SYM
fcis-2486	293	5	11074	11074	NUM
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fcis-2486	294	1	[	[	X
fcis-2486	294	2	27	27	NUM
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fcis-2486	294	15	.	.	PROPN
fcis-2486	294	16	memnet	memnet	PROPN
fcis-2486	294	17	:	:	PUNCT
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fcis-2486	294	19	persistent	persistent	ADJ
fcis-2486	294	20	memory	memory	NOUN
fcis-2486	294	21	network	network	NOUN
fcis-2486	294	22	for	for	ADP
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fcis-2486	294	25	of	of	ADP
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fcis-2486	294	30	on	on	ADP
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fcis-2486	294	33	.	.	PUNCT
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fcis-2486	295	2	:	:	PUNCT
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fcis-2486	295	4	-	-	SYM
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fcis-2486	297	6	lightweight	lightweight	ADJ
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fcis-2486	297	9	-	-	ADJ
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fcis-2486	297	12	on	on	ADP
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fcis-2486	297	14	vision	vision	NOUN
fcis-2486	297	15	.	.	PUNCT
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fcis-2486	298	2	,	,	PUNCT
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fcis-2486	298	6	:	:	PUNCT
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fcis-2486	299	3	]	]	PUNCT
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fcis-2486	299	18	of	of	ADP
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fcis-2486	299	20	neural	neural	ADJ
fcis-2486	299	21	networks	network	NOUN
fcis-2486	299	22	via	via	ADP
fcis-2486	299	23	attention	attention	NOUN
fcis-2486	299	24	transfer[j	transfer[j	NOUN
fcis-2486	299	25	]	]	PUNCT
fcis-2486	299	26	.	.	PUNCT
fcis-2486	300	1	arxiv	arxiv	PROPN
fcis-2486	300	2	preprint	preprint	VERB
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fcis-2486	300	4	,	,	PUNCT
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fcis-2486	301	1	[	[	X
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fcis-2486	301	3	]	]	X
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fcis-2486	301	8	j	j	PROPN
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fcis-2486	301	12	k	k	PROPN
fcis-2486	301	13	m.	m.	PROPN
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fcis-2486	301	17	-	-	NOUN
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fcis-2486	301	25	the	the	DET
fcis-2486	301	26	ieee	ieee	NOUN
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fcis-2486	302	2	:	:	PUNCT
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fcis-2486	302	4	-	-	SYM
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