id	sid	tid	token	lemma	pos
fcis-29234	1	1	frontiers	frontier	NOUN
fcis-29234	1	2	in	in	ADP
fcis-29234	1	3	computing	computing	NOUN
fcis-29234	1	4	and	and	CCONJ
fcis-29234	1	5	intelligent	intelligent	ADJ
fcis-29234	1	6	systems	system	NOUN
fcis-29234	1	7	issn	issn	VERB
fcis-29234	1	8	:	:	PUNCT
fcis-29234	1	9	2832	2832	NUM
fcis-29234	1	10	-	-	SYM
fcis-29234	1	11	6024	6024	NUM
fcis-29234	1	12	|	|	NOUN
fcis-29234	1	13	vol	vol	NOUN
fcis-29234	1	14	.	.	PROPN
fcis-29234	2	1	11	11	NUM
fcis-29234	2	2	,	,	PUNCT
fcis-29234	2	3	no	no	INTJ
fcis-29234	2	4	.	.	NOUN
fcis-29234	2	5	1	1	NUM
fcis-29234	2	6	,	,	PUNCT
fcis-29234	2	7	2025	2025	NUM
fcis-29234	2	8	53	53	NUM
fcis-29234	2	9	a	a	DET
fcis-29234	2	10	vehicle	vehicle	NOUN
fcis-29234	2	11	re‐identification	re‐identification	NOUN
fcis-29234	2	12	algorithm	algorithm	NOUN
fcis-29234	2	13	for	for	ADP
fcis-29234	2	14	long‐distance	long‐distance	NOUN
fcis-29234	2	15	small	small	ADJ
fcis-29234	2	16	targets	target	NOUN
fcis-29234	2	17	combining	combine	VERB
fcis-29234	2	18	yolov8	yolov8	NOUN
fcis-29234	2	19	object	object	NOUN
fcis-29234	2	20	detection	detection	NOUN
fcis-29234	2	21	algorithm	algorithm	NOUN
fcis-29234	2	22	chenyu	chenyu	VERB
fcis-29234	2	23	gu	gu	PROPN
fcis-29234	2	24	*	*	PROPN
fcis-29234	2	25	,	,	PUNCT
fcis-29234	2	26	hong	hong	PROPN
fcis-29234	2	27	du	du	PROPN
fcis-29234	2	28	,	,	PUNCT
fcis-29234	2	29	xiaozheng	xiaozheng	PROPN
fcis-29234	2	30	zhang	zhang	PROPN
fcis-29234	2	31	,	,	PUNCT
fcis-29234	2	32	ying	ying	PROPN
fcis-29234	2	33	wang	wang	PROPN
fcis-29234	2	34	,	,	PUNCT
fcis-29234	2	35	zhonglin	zhonglin	PROPN
fcis-29234	2	36	yang	yang	PROPN
fcis-29234	2	37	,	,	PUNCT
fcis-29234	2	38	gaotian	gaotian	PROPN
fcis-29234	2	39	liu	liu	PROPN
fcis-29234	2	40	,	,	PUNCT
fcis-29234	2	41	chuan	chuan	PROPN
fcis-29234	2	42	zhang	zhang	PROPN
fcis-29234	2	43	,	,	PUNCT
fcis-29234	2	44	lei	lei	PROPN
fcis-29234	2	45	sun	sun	PROPN
fcis-29234	2	46	china	china	PROPN
fcis-29234	2	47	northern	northern	PROPN
fcis-29234	2	48	vehicle	vehicle	PROPN
fcis-29234	2	49	research	research	PROPN
fcis-29234	2	50	institute	institute	PROPN
fcis-29234	2	51	,	,	PUNCT
fcis-29234	2	52	beijing	beijing	PROPN
fcis-29234	2	53	,	,	PUNCT
fcis-29234	2	54	china	china	PROPN
fcis-29234	2	55	*	*	PUNCT
fcis-29234	2	56	corresponding	correspond	VERB
fcis-29234	2	57	author	author	NOUN
fcis-29234	2	58	:	:	PUNCT
fcis-29234	2	59	chenyu	chenyu	PROPN
fcis-29234	2	60	gu	gu	NOUN
fcis-29234	2	61	abstract	abstract	PROPN
fcis-29234	2	62	:	:	PUNCT
fcis-29234	2	63	with	with	ADP
fcis-29234	2	64	the	the	DET
fcis-29234	2	65	rapid	rapid	ADJ
fcis-29234	2	66	development	development	NOUN
fcis-29234	2	67	of	of	ADP
fcis-29234	2	68	intelligent	intelligent	ADJ
fcis-29234	2	69	technologies	technology	NOUN
fcis-29234	2	70	,	,	PUNCT
fcis-29234	2	71	deep	deep	ADJ
fcis-29234	2	72	learning	learning	NOUN
fcis-29234	2	73	-	-	PUNCT
fcis-29234	2	74	based	base	VERB
fcis-29234	2	75	object	object	NOUN
fcis-29234	2	76	detection	detection	NOUN
fcis-29234	2	77	has	have	AUX
fcis-29234	2	78	been	be	AUX
fcis-29234	2	79	widely	widely	ADV
fcis-29234	2	80	applied	apply	VERB
fcis-29234	2	81	in	in	ADP
fcis-29234	2	82	dynamic	dynamic	ADJ
fcis-29234	2	83	fields	field	NOUN
fcis-29234	2	84	,	,	PUNCT
fcis-29234	2	85	especially	especially	ADV
fcis-29234	2	86	in	in	ADP
fcis-29234	2	87	vehicle	vehicle	NOUN
fcis-29234	2	88	management	management	NOUN
fcis-29234	2	89	.	.	PUNCT
fcis-29234	3	1	however	however	ADV
fcis-29234	3	2	,	,	PUNCT
fcis-29234	3	3	challenges	challenge	NOUN
fcis-29234	3	4	in	in	ADP
fcis-29234	3	5	accurate	accurate	ADJ
fcis-29234	3	6	recognition	recognition	NOUN
fcis-29234	3	7	and	and	CCONJ
fcis-29234	3	8	tracking	tracking	NOUN
fcis-29234	3	9	remain	remain	VERB
fcis-29234	3	10	,	,	PUNCT
fcis-29234	3	11	particularly	particularly	ADV
fcis-29234	3	12	due	due	ADJ
fcis-29234	3	13	to	to	ADP
fcis-29234	3	14	issues	issue	NOUN
fcis-29234	3	15	like	like	ADP
fcis-29234	3	16	intra	intra	ADJ
fcis-29234	3	17	-	-	ADJ
fcis-29234	3	18	class	class	ADJ
fcis-29234	3	19	variations	variation	NOUN
fcis-29234	3	20	and	and	CCONJ
fcis-29234	3	21	inter	inter	ADJ
fcis-29234	3	22	-	-	ADJ
fcis-29234	3	23	class	class	ADJ
fcis-29234	3	24	similarities	similarity	NOUN
fcis-29234	3	25	in	in	ADP
fcis-29234	3	26	vehicle	vehicle	NOUN
fcis-29234	3	27	re	re	NOUN
fcis-29234	3	28	-	-	NOUN
fcis-29234	3	29	identification	identification	NOUN
fcis-29234	3	30	across	across	ADP
fcis-29234	3	31	camera	camera	NOUN
fcis-29234	3	32	viewpoints	viewpoint	NOUN
fcis-29234	3	33	.	.	PUNCT
fcis-29234	4	1	this	this	DET
fcis-29234	4	2	paper	paper	NOUN
fcis-29234	4	3	proposes	propose	VERB
fcis-29234	4	4	a	a	DET
fcis-29234	4	5	method	method	NOUN
fcis-29234	4	6	for	for	ADP
fcis-29234	4	7	long	long	ADJ
fcis-29234	4	8	-	-	PUNCT
fcis-29234	4	9	range	range	NOUN
fcis-29234	4	10	small	small	ADJ
fcis-29234	4	11	vehicle	vehicle	NOUN
fcis-29234	4	12	target	target	NOUN
fcis-29234	4	13	re	re	NOUN
fcis-29234	4	14	-	-	NOUN
fcis-29234	4	15	identification	identification	NOUN
fcis-29234	4	16	based	base	VERB
fcis-29234	4	17	on	on	ADP
fcis-29234	4	18	the	the	DET
fcis-29234	4	19	yolov8	yolov8	NOUN
fcis-29234	4	20	object	object	NOUN
fcis-29234	4	21	detection	detection	NOUN
fcis-29234	4	22	algorithm	algorithm	NOUN
fcis-29234	4	23	,	,	PUNCT
fcis-29234	4	24	aiming	aim	VERB
fcis-29234	4	25	to	to	PART
fcis-29234	4	26	improve	improve	VERB
fcis-29234	4	27	the	the	DET
fcis-29234	4	28	detection	detection	NOUN
fcis-29234	4	29	accuracy	accuracy	NOUN
fcis-29234	4	30	and	and	CCONJ
fcis-29234	4	31	re	re	NOUN
fcis-29234	4	32	-	-	NOUN
fcis-29234	4	33	identification	identification	NOUN
fcis-29234	4	34	performance	performance	NOUN
fcis-29234	4	35	of	of	ADP
fcis-29234	4	36	small	small	ADJ
fcis-29234	4	37	targets	target	NOUN
fcis-29234	4	38	in	in	ADP
fcis-29234	4	39	complex	complex	ADJ
fcis-29234	4	40	environments	environment	NOUN
fcis-29234	4	41	.	.	PUNCT
fcis-29234	5	1	first	first	ADV
fcis-29234	5	2	,	,	PUNCT
fcis-29234	5	3	a	a	DET
fcis-29234	5	4	detection	detection	NOUN
fcis-29234	5	5	and	and	CCONJ
fcis-29234	5	6	re	re	NOUN
fcis-29234	5	7	-	-	NOUN
fcis-29234	5	8	identification	identification	NOUN
fcis-29234	5	9	dataset	dataset	NOUN
fcis-29234	5	10	containing	contain	VERB
fcis-29234	5	11	long	long	ADJ
fcis-29234	5	12	-	-	PUNCT
fcis-29234	5	13	range	range	NOUN
fcis-29234	5	14	vehicle	vehicle	NOUN
fcis-29234	5	15	images	image	NOUN
fcis-29234	5	16	is	be	AUX
fcis-29234	5	17	constructed	construct	VERB
fcis-29234	5	18	based	base	VERB
fcis-29234	5	19	on	on	ADP
fcis-29234	5	20	a	a	DET
fcis-29234	5	21	complex	complex	ADJ
fcis-29234	5	22	background	background	NOUN
fcis-29234	5	23	and	and	CCONJ
fcis-29234	5	24	small	small	ADJ
fcis-29234	5	25	target	target	NOUN
fcis-29234	5	26	dataset	dataset	NOUN
fcis-29234	5	27	.	.	PUNCT
fcis-29234	6	1	secondly	secondly	ADV
fcis-29234	6	2	,	,	PUNCT
fcis-29234	6	3	the	the	DET
fcis-29234	6	4	yolov8	yolov8	PROPN
fcis-29234	6	5	-	-	PUNCT
fcis-29234	6	6	ema	ema	PROPN
fcis-29234	6	7	-	-	PUNCT
fcis-29234	6	8	rfb	rfb	PROPN
fcis-29234	6	9	optimization	optimization	NOUN
fcis-29234	6	10	algorithm	algorithm	NOUN
fcis-29234	6	11	is	be	AUX
fcis-29234	6	12	introduced	introduce	VERB
fcis-29234	6	13	,	,	PUNCT
fcis-29234	6	14	which	which	PRON
fcis-29234	6	15	combines	combine	VERB
fcis-29234	6	16	the	the	DET
fcis-29234	6	17	ema	ema	NOUN
fcis-29234	6	18	(	(	PUNCT
fcis-29234	6	19	exponential	exponential	NOUN
fcis-29234	6	20	moving	move	VERB
fcis-29234	6	21	average	average	ADJ
fcis-29234	6	22	)	)	PUNCT
fcis-29234	6	23	attention	attention	NOUN
fcis-29234	6	24	mechanism	mechanism	NOUN
fcis-29234	6	25	and	and	CCONJ
fcis-29234	6	26	rfb	rfb	PROPN
fcis-29234	6	27	(	(	PUNCT
fcis-29234	6	28	receptive	receptive	ADJ
fcis-29234	6	29	field	field	NOUN
fcis-29234	6	30	block	block	NOUN
fcis-29234	6	31	)	)	PUNCT
fcis-29234	6	32	structure	structure	NOUN
fcis-29234	6	33	.	.	PUNCT
fcis-29234	7	1	the	the	DET
fcis-29234	7	2	ema	ema	PROPN
fcis-29234	7	3	module	module	NOUN
fcis-29234	7	4	enhances	enhance	VERB
fcis-29234	7	5	feature	feature	NOUN
fcis-29234	7	6	extraction	extraction	NOUN
fcis-29234	7	7	for	for	ADP
fcis-29234	7	8	small	small	ADJ
fcis-29234	7	9	targets	target	NOUN
fcis-29234	7	10	and	and	CCONJ
fcis-29234	7	11	reduces	reduce	VERB
fcis-29234	7	12	noise	noise	NOUN
fcis-29234	7	13	interference	interference	NOUN
fcis-29234	7	14	,	,	PUNCT
fcis-29234	7	15	while	while	SCONJ
fcis-29234	7	16	the	the	DET
fcis-29234	7	17	rfb	rfb	PROPN
fcis-29234	7	18	module	module	NOUN
fcis-29234	7	19	increases	increase	VERB
fcis-29234	7	20	the	the	DET
fcis-29234	7	21	receptive	receptive	ADJ
fcis-29234	7	22	field	field	NOUN
fcis-29234	7	23	,	,	PUNCT
fcis-29234	7	24	improving	improve	VERB
fcis-29234	7	25	the	the	DET
fcis-29234	7	26	detection	detection	NOUN
fcis-29234	7	27	capability	capability	NOUN
fcis-29234	7	28	for	for	ADP
fcis-29234	7	29	small	small	ADJ
fcis-29234	7	30	targets	target	NOUN
fcis-29234	7	31	.	.	PUNCT
fcis-29234	8	1	through	through	ADP
fcis-29234	8	2	these	these	DET
fcis-29234	8	3	optimizations	optimization	NOUN
fcis-29234	8	4	,	,	PUNCT
fcis-29234	8	5	the	the	DET
fcis-29234	8	6	proposed	propose	VERB
fcis-29234	8	7	method	method	NOUN
fcis-29234	8	8	effectively	effectively	ADV
fcis-29234	8	9	improves	improve	VERB
fcis-29234	8	10	the	the	DET
fcis-29234	8	11	detection	detection	NOUN
fcis-29234	8	12	accuracy	accuracy	NOUN
fcis-29234	8	13	of	of	ADP
fcis-29234	8	14	long	long	ADJ
fcis-29234	8	15	-	-	PUNCT
fcis-29234	8	16	range	range	NOUN
fcis-29234	8	17	small	small	ADJ
fcis-29234	8	18	targets	target	NOUN
fcis-29234	8	19	in	in	ADP
fcis-29234	8	20	the	the	DET
fcis-29234	8	21	yolov8	yolov8	NOUN
fcis-29234	8	22	model	model	NOUN
fcis-29234	8	23	.	.	PUNCT
fcis-29234	9	1	additionally	additionally	ADV
fcis-29234	9	2	,	,	PUNCT
fcis-29234	9	3	by	by	ADP
fcis-29234	9	4	incorporating	incorporate	VERB
fcis-29234	9	5	the	the	DET
fcis-29234	9	6	alignedreid	alignedreid	NOUN
fcis-29234	9	7	method	method	NOUN
fcis-29234	9	8	,	,	PUNCT
fcis-29234	9	9	feature	feature	NOUN
fcis-29234	9	10	matching	match	VERB
fcis-29234	9	11	in	in	ADP
fcis-29234	9	12	object	object	NOUN
fcis-29234	9	13	re	re	NOUN
fcis-29234	9	14	-	-	NOUN
fcis-29234	9	15	identification	identification	NOUN
fcis-29234	9	16	is	be	AUX
fcis-29234	9	17	improved	improve	VERB
fcis-29234	9	18	,	,	PUNCT
fcis-29234	9	19	further	far	ADV
fcis-29234	9	20	enhancing	enhance	VERB
fcis-29234	9	21	the	the	DET
fcis-29234	9	22	accuracy	accuracy	NOUN
fcis-29234	9	23	and	and	CCONJ
fcis-29234	9	24	robustness	robustness	NOUN
fcis-29234	9	25	of	of	ADP
fcis-29234	9	26	multi	multi	ADJ
fcis-29234	9	27	-	-	ADJ
fcis-29234	9	28	object	object	ADJ
fcis-29234	9	29	tracking	tracking	NOUN
fcis-29234	9	30	.	.	PUNCT
fcis-29234	10	1	experimental	experimental	ADJ
fcis-29234	10	2	results	result	NOUN
fcis-29234	10	3	demonstrate	demonstrate	VERB
fcis-29234	10	4	that	that	SCONJ
fcis-29234	10	5	the	the	DET
fcis-29234	10	6	proposed	propose	VERB
fcis-29234	10	7	method	method	NOUN
fcis-29234	10	8	achieves	achieve	VERB
fcis-29234	10	9	high	high	ADJ
fcis-29234	10	10	precision	precision	NOUN
fcis-29234	10	11	and	and	CCONJ
fcis-29234	10	12	real	real	ADJ
fcis-29234	10	13	-	-	PUNCT
fcis-29234	10	14	time	time	NOUN
fcis-29234	10	15	performance	performance	NOUN
fcis-29234	10	16	in	in	ADP
fcis-29234	10	17	multi	multi	ADJ
fcis-29234	10	18	-	-	ADJ
fcis-29234	10	19	scene	scene	ADJ
fcis-29234	10	20	vehicle	vehicle	NOUN
fcis-29234	10	21	detection	detection	NOUN
fcis-29234	10	22	and	and	CCONJ
fcis-29234	10	23	re	re	NOUN
fcis-29234	10	24	-	-	NOUN
fcis-29234	10	25	identification	identification	NOUN
fcis-29234	10	26	,	,	PUNCT
fcis-29234	10	27	offering	offer	VERB
fcis-29234	10	28	a	a	DET
fcis-29234	10	29	novel	novel	ADJ
fcis-29234	10	30	solution	solution	NOUN
fcis-29234	10	31	for	for	ADP
fcis-29234	10	32	intelligent	intelligent	ADJ
fcis-29234	10	33	transportation	transportation	NOUN
fcis-29234	10	34	and	and	CCONJ
fcis-29234	10	35	urban	urban	ADJ
fcis-29234	10	36	security	security	NOUN
fcis-29234	10	37	surveillance	surveillance	NOUN
fcis-29234	10	38	.	.	PUNCT
fcis-29234	11	1	keywords	keyword	NOUN
fcis-29234	11	2	:	:	PUNCT
fcis-29234	11	3	vehicle	vehicle	NOUN
fcis-29234	11	4	re	re	NOUN
fcis-29234	11	5	-	-	NOUN
fcis-29234	11	6	identification	identification	NOUN
fcis-29234	11	7	;	;	PUNCT
fcis-29234	11	8	object	object	NOUN
fcis-29234	11	9	detection	detection	NOUN
fcis-29234	11	10	;	;	PUNCT
fcis-29234	11	11	attention	attention	NOUN
fcis-29234	11	12	mechanism	mechanism	NOUN
fcis-29234	11	13	.	.	PUNCT
fcis-29234	12	1	1	1	X
fcis-29234	12	2	.	.	X
fcis-29234	12	3	introduction	introduction	NOUN
fcis-29234	12	4	with	with	ADP
fcis-29234	12	5	the	the	DET
fcis-29234	12	6	rapid	rapid	ADJ
fcis-29234	12	7	development	development	NOUN
fcis-29234	12	8	of	of	ADP
fcis-29234	12	9	intelligence	intelligence	NOUN
fcis-29234	12	10	,	,	PUNCT
fcis-29234	12	11	deep	deep	ADV
fcis-29234	12	12	learningbased	learningbased	ADJ
fcis-29234	12	13	object	object	NOUN
fcis-29234	12	14	detection	detection	NOUN
fcis-29234	12	15	has	have	AUX
fcis-29234	12	16	been	be	AUX
fcis-29234	12	17	applied	apply	VERB
fcis-29234	12	18	in	in	ADP
fcis-29234	12	19	dynamic	dynamic	ADJ
fcis-29234	12	20	fields	field	NOUN
fcis-29234	12	21	of	of	ADP
fcis-29234	12	22	view	view	NOUN
fcis-29234	12	23	.	.	PUNCT
fcis-29234	13	1	its	its	PRON
fcis-29234	13	2	object	object	NOUN
fcis-29234	13	3	detection	detection	NOUN
fcis-29234	13	4	capabilities	capability	NOUN
fcis-29234	13	5	can	can	AUX
fcis-29234	13	6	be	be	AUX
fcis-29234	13	7	used	use	VERB
fcis-29234	13	8	to	to	PART
fcis-29234	13	9	generate	generate	VERB
fcis-29234	13	10	target	target	NOUN
fcis-29234	13	11	trajectories	trajectory	NOUN
fcis-29234	13	12	,	,	PUNCT
fcis-29234	13	13	aiding	aid	VERB
fcis-29234	13	14	target	target	NOUN
fcis-29234	13	15	management	management	NOUN
fcis-29234	13	16	tasks	task	NOUN
fcis-29234	13	17	,	,	PUNCT
fcis-29234	13	18	thereby	thereby	ADV
fcis-29234	13	19	addressing	address	VERB
fcis-29234	13	20	vehicle	vehicle	NOUN
fcis-29234	13	21	control	control	NOUN
fcis-29234	13	22	issues	issue	NOUN
fcis-29234	13	23	in	in	ADP
fcis-29234	13	24	different	different	ADJ
fcis-29234	13	25	scenarios	scenario	NOUN
fcis-29234	13	26	.	.	PUNCT
fcis-29234	14	1	with	with	ADP
fcis-29234	14	2	the	the	DET
fcis-29234	14	3	emergence	emergence	NOUN
fcis-29234	14	4	of	of	ADP
fcis-29234	14	5	large	large	ADJ
fcis-29234	14	6	-	-	PUNCT
fcis-29234	14	7	scale	scale	NOUN
fcis-29234	14	8	vehicle	vehicle	NOUN
fcis-29234	14	9	datasets	dataset	NOUN
fcis-29234	14	10	and	and	CCONJ
fcis-29234	14	11	deep	deep	ADJ
fcis-29234	14	12	learning	learning	NOUN
fcis-29234	14	13	-	-	PUNCT
fcis-29234	14	14	based	base	VERB
fcis-29234	14	15	methods	method	NOUN
fcis-29234	14	16	,	,	PUNCT
fcis-29234	14	17	significant	significant	ADJ
fcis-29234	14	18	progress	progress	NOUN
fcis-29234	14	19	has	have	AUX
fcis-29234	14	20	been	be	AUX
fcis-29234	14	21	made	make	VERB
fcis-29234	14	22	in	in	ADP
fcis-29234	14	23	vehicle	vehicle	NOUN
fcis-29234	14	24	re	re	NOUN
fcis-29234	14	25	-	-	NOUN
fcis-29234	14	26	identification	identification	NOUN
fcis-29234	14	27	tasks	task	NOUN
fcis-29234	14	28	.	.	PUNCT
fcis-29234	15	1	however	however	ADV
fcis-29234	15	2	,	,	PUNCT
fcis-29234	15	3	due	due	ADP
fcis-29234	15	4	to	to	ADP
fcis-29234	15	5	the	the	DET
fcis-29234	15	6	complex	complex	ADJ
fcis-29234	15	7	viewpoint	viewpoint	NOUN
fcis-29234	15	8	transformations	transformation	NOUN
fcis-29234	15	9	across	across	ADP
fcis-29234	15	10	cameras	camera	NOUN
fcis-29234	15	11	and	and	CCONJ
fcis-29234	15	12	two	two	NUM
fcis-29234	15	13	main	main	ADJ
fcis-29234	15	14	challenges	challenge	NOUN
fcis-29234	15	15	in	in	ADP
fcis-29234	15	16	vehicle	vehicle	NOUN
fcis-29234	15	17	re	re	NOUN
fcis-29234	15	18	-	-	NOUN
fcis-29234	15	19	identification	identification	NOUN
fcis-29234	15	20	—	—	PUNCT
fcis-29234	15	21	intra	intra	ADJ
fcis-29234	15	22	-	-	ADJ
fcis-29234	15	23	class	class	ADJ
fcis-29234	15	24	variation	variation	NOUN
fcis-29234	15	25	and	and	CCONJ
fcis-29234	15	26	inter	inter	ADJ
fcis-29234	15	27	-	-	ADJ
fcis-29234	15	28	class	class	ADJ
fcis-29234	15	29	similarity	similarity	NOUN
fcis-29234	15	30	—	—	PUNCT
fcis-29234	15	31	vehicle	vehicle	NOUN
fcis-29234	15	32	re	re	NOUN
fcis-29234	15	33	-	-	NOUN
fcis-29234	15	34	identification	identification	NOUN
fcis-29234	15	35	tasks	task	NOUN
fcis-29234	15	36	remain	remain	VERB
fcis-29234	15	37	challenging	challenge	VERB
fcis-29234	15	38	[	[	X
fcis-29234	15	39	1	1	NUM
fcis-29234	15	40	]	]	PUNCT
fcis-29234	15	41	.	.	PUNCT
fcis-29234	16	1	generally	generally	ADV
fcis-29234	16	2	,	,	PUNCT
fcis-29234	16	3	vehicle	vehicle	NOUN
fcis-29234	16	4	detection	detection	NOUN
fcis-29234	16	5	algorithms	algorithm	NOUN
fcis-29234	16	6	perform	perform	VERB
fcis-29234	16	7	well	well	ADV
fcis-29234	16	8	in	in	ADP
fcis-29234	16	9	static	static	ADJ
fcis-29234	16	10	scenes	scene	NOUN
fcis-29234	16	11	,	,	PUNCT
fcis-29234	16	12	but	but	CCONJ
fcis-29234	16	13	maintaining	maintain	VERB
fcis-29234	16	14	unique	unique	ADJ
fcis-29234	16	15	target	target	NOUN
fcis-29234	16	16	identification	identification	NOUN
fcis-29234	16	17	numbers	number	NOUN
fcis-29234	16	18	for	for	ADP
fcis-29234	16	19	the	the	DET
fcis-29234	16	20	same	same	ADJ
fcis-29234	16	21	target	target	NOUN
fcis-29234	16	22	becomes	become	VERB
fcis-29234	16	23	difficult	difficult	ADJ
fcis-29234	16	24	under	under	ADP
fcis-29234	16	25	crosscamera	crosscamera	NOUN
fcis-29234	16	26	conditions	condition	NOUN
fcis-29234	16	27	or	or	CCONJ
fcis-29234	16	28	when	when	SCONJ
fcis-29234	16	29	affected	affect	VERB
fcis-29234	16	30	by	by	ADP
fcis-29234	16	31	viewpoint	viewpoint	NOUN
fcis-29234	16	32	changes	change	NOUN
fcis-29234	16	33	,	,	PUNCT
fcis-29234	16	34	occlusion	occlusion	NOUN
fcis-29234	16	35	,	,	PUNCT
fcis-29234	16	36	and	and	CCONJ
fcis-29234	16	37	other	other	ADJ
fcis-29234	16	38	factors	factor	NOUN
fcis-29234	16	39	.	.	PUNCT
fcis-29234	17	1	the	the	DET
fcis-29234	17	2	main	main	ADJ
fcis-29234	17	3	challenges	challenge	NOUN
fcis-29234	17	4	faced	face	VERB
fcis-29234	17	5	by	by	ADP
fcis-29234	17	6	vehicle	vehicle	NOUN
fcis-29234	17	7	detection	detection	NOUN
fcis-29234	17	8	and	and	CCONJ
fcis-29234	17	9	reidentification	reidentification	NOUN
fcis-29234	17	10	algorithms	algorithm	NOUN
fcis-29234	17	11	under	under	ADP
fcis-29234	17	12	different	different	ADJ
fcis-29234	17	13	fields	field	NOUN
fcis-29234	17	14	of	of	ADP
fcis-29234	17	15	view	view	NOUN
fcis-29234	17	16	include	include	VERB
fcis-29234	17	17	the	the	DET
fcis-29234	17	18	short	short	ADJ
fcis-29234	17	19	-	-	PUNCT
fcis-29234	17	20	term	term	NOUN
fcis-29234	17	21	disappearance	disappearance	NOUN
fcis-29234	17	22	of	of	ADP
fcis-29234	17	23	targets	target	NOUN
fcis-29234	17	24	due	due	ADJ
fcis-29234	17	25	to	to	ADP
fcis-29234	17	26	fieldof	fieldof	ADJ
fcis-29234	17	27	-	-	PUNCT
fcis-29234	17	28	view	view	NOUN
fcis-29234	17	29	changes	change	NOUN
fcis-29234	17	30	,	,	PUNCT
fcis-29234	17	31	the	the	DET
fcis-29234	17	32	impact	impact	NOUN
fcis-29234	17	33	of	of	ADP
fcis-29234	17	34	viewpoint	viewpoint	NOUN
fcis-29234	17	35	changes	change	NOUN
fcis-29234	17	36	on	on	ADP
fcis-29234	17	37	feature	feature	NOUN
fcis-29234	17	38	matching	matching	NOUN
fcis-29234	17	39	,	,	PUNCT
fcis-29234	17	40	and	and	CCONJ
fcis-29234	17	41	the	the	DET
fcis-29234	17	42	difficulty	difficulty	NOUN
fcis-29234	17	43	of	of	ADP
fcis-29234	17	44	extracting	extract	VERB
fcis-29234	17	45	features	feature	NOUN
fcis-29234	17	46	from	from	ADP
fcis-29234	17	47	small	small	ADJ
fcis-29234	17	48	targets	target	NOUN
fcis-29234	17	49	.	.	PUNCT
fcis-29234	18	1	these	these	DET
fcis-29234	18	2	challenges	challenge	NOUN
fcis-29234	18	3	collectively	collectively	ADV
fcis-29234	18	4	constrain	constrain	VERB
fcis-29234	18	5	the	the	DET
fcis-29234	18	6	performance	performance	NOUN
fcis-29234	18	7	of	of	ADP
fcis-29234	18	8	vehicle	vehicle	NOUN
fcis-29234	18	9	detection	detection	NOUN
fcis-29234	18	10	and	and	CCONJ
fcis-29234	18	11	re	re	NOUN
fcis-29234	18	12	-	-	NOUN
fcis-29234	18	13	identification	identification	NOUN
fcis-29234	18	14	.	.	PUNCT
fcis-29234	19	1	in	in	ADP
fcis-29234	19	2	the	the	DET
fcis-29234	19	3	early	early	ADJ
fcis-29234	19	4	stages	stage	NOUN
fcis-29234	19	5	,	,	PUNCT
fcis-29234	19	6	researchers	researcher	NOUN
fcis-29234	19	7	focused	focus	VERB
fcis-29234	19	8	on	on	ADP
fcis-29234	19	9	extracting	extract	VERB
fcis-29234	19	10	global	global	ADJ
fcis-29234	19	11	or	or	CCONJ
fcis-29234	19	12	appearance	appearance	NOUN
fcis-29234	19	13	features	feature	NOUN
fcis-29234	19	14	,	,	PUNCT
fcis-29234	19	15	such	such	ADJ
fcis-29234	19	16	as	as	ADP
fcis-29234	19	17	vehicle	vehicle	NOUN
fcis-29234	19	18	windows	window	NOUN
fcis-29234	19	19	,	,	PUNCT
fcis-29234	19	20	headlights	headlight	NOUN
fcis-29234	19	21	,	,	PUNCT
fcis-29234	19	22	interior	interior	NOUN
fcis-29234	19	23	,	,	PUNCT
fcis-29234	19	24	brand	brand	NOUN
fcis-29234	19	25	logos	logo	NOUN
fcis-29234	19	26	,	,	PUNCT
fcis-29234	19	27	and	and	CCONJ
fcis-29234	19	28	other	other	ADJ
fcis-29234	19	29	distinguishable	distinguishable	ADJ
fcis-29234	19	30	features	feature	NOUN
fcis-29234	19	31	.	.	PUNCT
fcis-29234	20	1	however	however	ADV
fcis-29234	20	2	,	,	PUNCT
fcis-29234	20	3	due	due	ADP
fcis-29234	20	4	to	to	ADP
fcis-29234	20	5	the	the	DET
fcis-29234	20	6	issue	issue	NOUN
fcis-29234	20	7	of	of	ADP
fcis-29234	20	8	inter	inter	ADJ
fcis-29234	20	9	-	-	ADJ
fcis-29234	20	10	class	class	ADJ
fcis-29234	20	11	similarity	similarity	NOUN
fcis-29234	20	12	,	,	PUNCT
fcis-29234	20	13	this	this	DET
fcis-29234	20	14	approach	approach	NOUN
fcis-29234	20	15	has	have	VERB
fcis-29234	20	16	certain	certain	ADJ
fcis-29234	20	17	limitations	limitation	NOUN
fcis-29234	20	18	.	.	PUNCT
fcis-29234	21	1	subsequently	subsequently	ADV
fcis-29234	21	2	,	,	PUNCT
fcis-29234	21	3	methods	method	NOUN
fcis-29234	21	4	utilizing	utilize	VERB
fcis-29234	21	5	local	local	ADJ
fcis-29234	21	6	features	feature	NOUN
fcis-29234	21	7	to	to	PART
fcis-29234	21	8	improve	improve	VERB
fcis-29234	21	9	the	the	DET
fcis-29234	21	10	accuracy	accuracy	NOUN
fcis-29234	21	11	of	of	ADP
fcis-29234	21	12	vehicle	vehicle	NOUN
fcis-29234	21	13	reidentification	reidentification	NOUN
fcis-29234	21	14	tasks	task	NOUN
fcis-29234	21	15	have	have	AUX
fcis-29234	21	16	been	be	AUX
fcis-29234	21	17	widely	widely	ADV
fcis-29234	21	18	proposed	propose	VERB
fcis-29234	21	19	.	.	PUNCT
fcis-29234	22	1	for	for	ADP
fcis-29234	22	2	example	example	NOUN
fcis-29234	22	3	,	,	PUNCT
fcis-29234	22	4	reference	reference	NOUN
fcis-29234	22	5	[	[	X
fcis-29234	22	6	2	2	NUM
fcis-29234	22	7	]	]	PUNCT
fcis-29234	22	8	uses	use	VERB
fcis-29234	22	9	object	object	NOUN
fcis-29234	22	10	detection	detection	NOUN
fcis-29234	22	11	methods	method	NOUN
fcis-29234	22	12	to	to	PART
fcis-29234	22	13	extract	extract	VERB
fcis-29234	22	14	local	local	ADJ
fcis-29234	22	15	regions	region	NOUN
fcis-29234	22	16	such	such	ADJ
fcis-29234	22	17	as	as	ADP
fcis-29234	22	18	headlights	headlight	NOUN
fcis-29234	22	19	,	,	PUNCT
fcis-29234	22	20	inspection	inspection	NOUN
fcis-29234	22	21	stickers	sticker	NOUN
fcis-29234	22	22	,	,	PUNCT
fcis-29234	22	23	and	and	CCONJ
fcis-29234	22	24	decorations	decoration	NOUN
fcis-29234	22	25	from	from	ADP
fcis-29234	22	26	vehicle	vehicle	NOUN
fcis-29234	22	27	images	image	NOUN
fcis-29234	22	28	,	,	PUNCT
fcis-29234	22	29	and	and	CCONJ
fcis-29234	22	30	uses	use	VERB
fcis-29234	22	31	these	these	DET
fcis-29234	22	32	local	local	ADJ
fcis-29234	22	33	parts	part	NOUN
fcis-29234	22	34	for	for	ADP
fcis-29234	22	35	vehicle	vehicle	NOUN
fcis-29234	22	36	re	re	NOUN
fcis-29234	22	37	-	-	NOUN
fcis-29234	22	38	identification	identification	NOUN
fcis-29234	22	39	.	.	PUNCT
fcis-29234	23	1	to	to	PART
fcis-29234	23	2	better	well	ADV
fcis-29234	23	3	focus	focus	VERB
fcis-29234	23	4	on	on	ADP
fcis-29234	23	5	local	local	ADJ
fcis-29234	23	6	regions	region	NOUN
fcis-29234	23	7	,	,	PUNCT
fcis-29234	23	8	references	reference	NOUN
fcis-29234	23	9	[	[	X
fcis-29234	23	10	3	3	NUM
fcis-29234	23	11	-	-	SYM
fcis-29234	23	12	5	5	NUM
fcis-29234	23	13	]	]	PUNCT
fcis-29234	23	14	designed	design	VERB
fcis-29234	23	15	methods	method	NOUN
fcis-29234	23	16	to	to	PART
fcis-29234	23	17	divide	divide	VERB
fcis-29234	23	18	feature	feature	NOUN
fcis-29234	23	19	maps	map	NOUN
fcis-29234	23	20	along	along	ADP
fcis-29234	23	21	the	the	DET
fcis-29234	23	22	horizontal	horizontal	ADJ
fcis-29234	23	23	and	and	CCONJ
fcis-29234	23	24	vertical	vertical	ADJ
fcis-29234	23	25	dimensions	dimension	NOUN
fcis-29234	23	26	to	to	PART
fcis-29234	23	27	extract	extract	VERB
fcis-29234	23	28	finegrained	finegraine	VERB
fcis-29234	23	29	local	local	ADJ
fcis-29234	23	30	features	feature	NOUN
fcis-29234	23	31	.	.	PUNCT
fcis-29234	24	1	however	however	ADV
fcis-29234	24	2	,	,	PUNCT
fcis-29234	24	3	in	in	ADP
fcis-29234	24	4	complex	complex	ADJ
fcis-29234	24	5	traffic	traffic	NOUN
fcis-29234	24	6	environments	environment	NOUN
fcis-29234	24	7	,	,	PUNCT
fcis-29234	24	8	due	due	ADP
fcis-29234	24	9	to	to	ADP
fcis-29234	24	10	factors	factor	NOUN
fcis-29234	24	11	such	such	ADJ
fcis-29234	24	12	as	as	ADP
fcis-29234	24	13	camera	camera	NOUN
fcis-29234	24	14	viewpoint	viewpoint	NOUN
fcis-29234	24	15	changes	change	NOUN
fcis-29234	24	16	and	and	CCONJ
fcis-29234	24	17	lighting	lighting	NOUN
fcis-29234	24	18	,	,	PUNCT
fcis-29234	24	19	the	the	DET
fcis-29234	24	20	features	feature	NOUN
fcis-29234	24	21	extracted	extract	VERB
fcis-29234	24	22	by	by	ADP
fcis-29234	24	23	the	the	DET
fcis-29234	24	24	above	above	ADJ
fcis-29234	24	25	methods	method	NOUN
fcis-29234	24	26	are	be	AUX
fcis-29234	24	27	often	often	ADV
fcis-29234	24	28	ineffective	ineffective	ADJ
fcis-29234	24	29	for	for	ADP
fcis-29234	24	30	completing	complete	VERB
fcis-29234	24	31	vehicle	vehicle	NOUN
fcis-29234	24	32	re	re	NOUN
fcis-29234	24	33	-	-	NOUN
fcis-29234	24	34	identification	identification	NOUN
fcis-29234	24	35	tasks	task	NOUN
fcis-29234	24	36	.	.	PUNCT
fcis-29234	25	1	this	this	DET
fcis-29234	25	2	paper	paper	NOUN
fcis-29234	25	3	aims	aim	VERB
fcis-29234	25	4	to	to	PART
fcis-29234	25	5	optimize	optimize	VERB
fcis-29234	25	6	vehicle	vehicle	NOUN
fcis-29234	25	7	detection	detection	NOUN
fcis-29234	25	8	and	and	CCONJ
fcis-29234	25	9	re	re	NOUN
fcis-29234	25	10	-	-	NOUN
fcis-29234	25	11	identification	identification	NOUN
fcis-29234	25	12	algorithms	algorithm	NOUN
fcis-29234	25	13	in	in	ADP
fcis-29234	25	14	such	such	ADJ
fcis-29234	25	15	scenarios	scenario	NOUN
fcis-29234	25	16	.	.	PUNCT
fcis-29234	26	1	2	2	X
fcis-29234	26	2	.	.	X
fcis-29234	26	3	optimization	optimization	NOUN
fcis-29234	26	4	of	of	ADP
fcis-29234	26	5	object	object	NOUN
fcis-29234	26	6	detectors	detector	NOUN
fcis-29234	26	7	for	for	ADP
fcis-29234	26	8	long	long	ADJ
fcis-29234	26	9	-	-	PUNCT
fcis-29234	26	10	distance	distance	NOUN
fcis-29234	26	11	small	small	ADJ
fcis-29234	26	12	vehicle	vehicle	NOUN
fcis-29234	26	13	targets	target	VERB
fcis-29234	26	14	2.1	2.1	NUM
fcis-29234	26	15	.	.	PUNCT
fcis-29234	27	1	design	design	NOUN
fcis-29234	27	2	of	of	ADP
fcis-29234	27	3	vehicle	vehicle	NOUN
fcis-29234	27	4	target	target	NOUN
fcis-29234	27	5	detection	detection	NOUN
fcis-29234	27	6	algorithm	algorithm	NOUN
fcis-29234	27	7	based	base	VERB
fcis-29234	27	8	on	on	ADP
fcis-29234	27	9	yolov8	yolov8	PROPN
fcis-29234	27	10	yolov8	yolov8	PROPN
fcis-29234	27	11	model	model	NOUN
fcis-29234	27	12	is	be	AUX
fcis-29234	27	13	one	one	NUM
fcis-29234	27	14	of	of	ADP
fcis-29234	27	15	the	the	DET
fcis-29234	27	16	latest	late	ADJ
fcis-29234	27	17	generation	generation	NOUN
fcis-29234	27	18	of	of	ADP
fcis-29234	27	19	yolo	yolo	ADJ
fcis-29234	27	20	series	series	PROPN
fcis-29234	27	21	algorithms	algorithm	NOUN
fcis-29234	27	22	introduced	introduce	VERB
fcis-29234	27	23	by	by	ADP
fcis-29234	27	24	ultralytics	ultralytic	NOUN
fcis-29234	27	25	,	,	PUNCT
fcis-29234	27	26	which	which	PRON
fcis-29234	27	27	contains	contain	VERB
fcis-29234	27	28	five	five	NUM
fcis-29234	27	29	versions	version	NOUN
fcis-29234	27	30	from	from	ADP
fcis-29234	27	31	small	small	ADJ
fcis-29234	27	32	to	to	PART
fcis-29234	27	33	large	large	ADJ
fcis-29234	27	34	:	:	PUNCT
fcis-29234	27	35	yolov8n	yolov8n	NOUN
fcis-29234	27	36	,	,	PUNCT
fcis-29234	27	37	yolov8s	yolov8	NOUN
fcis-29234	27	38	,	,	PUNCT
fcis-29234	27	39	yolov8	yolov8	PROPN
fcis-29234	27	40	m	m	PROPN
fcis-29234	27	41	,	,	PUNCT
fcis-29234	27	42	yolov8l	yolov8l	NOUN
fcis-29234	27	43	,	,	PUNCT
fcis-29234	27	44	and	and	CCONJ
fcis-29234	27	45	yolov8x	yolov8x	PROPN
fcis-29234	27	46	.	.	PUNCT
fcis-29234	28	1	the	the	DET
fcis-29234	28	2	detection	detection	NOUN
fcis-29234	28	3	accuracy	accuracy	NOUN
fcis-29234	28	4	of	of	ADP
fcis-29234	28	5	the	the	DET
fcis-29234	28	6	model	model	NOUN
fcis-29234	28	7	is	be	AUX
fcis-29234	28	8	progressively	progressively	ADV
fcis-29234	28	9	improved	improve	VERB
fcis-29234	28	10	with	with	ADP
fcis-29234	28	11	the	the	DET
fcis-29234	28	12	increase	increase	NOUN
fcis-29234	28	13	of	of	ADP
fcis-29234	28	14	the	the	DET
fcis-29234	28	15	size	size	NOUN
fcis-29234	28	16	of	of	ADP
fcis-29234	28	17	the	the	DET
fcis-29234	28	18	model	model	NOUN
fcis-29234	28	19	,	,	PUNCT
fcis-29234	28	20	so	so	SCONJ
fcis-29234	28	21	that	that	SCONJ
fcis-29234	28	22	a	a	DET
fcis-29234	28	23	suitable	suitable	ADJ
fcis-29234	28	24	yolov8	yolov8	NOUN
fcis-29234	28	25	54	54	NUM
fcis-29234	28	26	model	model	NOUN
fcis-29234	28	27	version	version	NOUN
fcis-29234	28	28	can	can	AUX
fcis-29234	28	29	be	be	AUX
fcis-29234	28	30	selected	select	VERB
fcis-29234	28	31	according	accord	VERB
fcis-29234	28	32	to	to	ADP
fcis-29234	28	33	the	the	DET
fcis-29234	28	34	specific	specific	ADJ
fcis-29234	28	35	task	task	NOUN
fcis-29234	28	36	requirements	requirement	NOUN
fcis-29234	28	37	.	.	PUNCT
fcis-29234	29	1	version	version	NOUN
fcis-29234	29	2	of	of	ADP
fcis-29234	29	3	the	the	DET
fcis-29234	29	4	yolov8	yolov8	NOUN
fcis-29234	29	5	model.yolov8	model.yolov8	NOUN
fcis-29234	29	6	performs	perform	VERB
fcis-29234	29	7	well	well	ADV
fcis-29234	29	8	in	in	ADP
fcis-29234	29	9	tasks	task	NOUN
fcis-29234	29	10	such	such	ADJ
fcis-29234	29	11	as	as	ADP
fcis-29234	29	12	target	target	NOUN
fcis-29234	29	13	detection	detection	NOUN
fcis-29234	29	14	,	,	PUNCT
fcis-29234	29	15	instance	instance	NOUN
fcis-29234	29	16	segmentation	segmentation	NOUN
fcis-29234	29	17	and	and	CCONJ
fcis-29234	29	18	target	target	VERB
fcis-29234	29	19	classification	classification	NOUN
fcis-29234	29	20	[	[	X
fcis-29234	29	21	17].the	17].the	NUM
fcis-29234	29	22	structure	structure	NOUN
fcis-29234	29	23	of	of	ADP
fcis-29234	29	24	the	the	DET
fcis-29234	29	25	yolov8	yolov8	NOUN
fcis-29234	29	26	model	model	NOUN
fcis-29234	29	27	is	be	AUX
fcis-29234	29	28	shown	show	VERB
fcis-29234	29	29	in	in	ADP
fcis-29234	29	30	fig.1	fig.1	PROPN
fcis-29234	29	31	.	.	PUNCT
fcis-29234	30	1	the	the	DET
fcis-29234	30	2	yolov8	yolov8	PROPN
fcis-29234	30	3	model	model	NOUN
fcis-29234	30	4	consists	consist	VERB
fcis-29234	30	5	of	of	ADP
fcis-29234	30	6	three	three	NUM
fcis-29234	30	7	main	main	ADJ
fcis-29234	30	8	parts	part	NOUN
fcis-29234	30	9	:	:	PUNCT
fcis-29234	30	10	the	the	DET
fcis-29234	30	11	backbone	backbone	NOUN
fcis-29234	30	12	network	network	NOUN
fcis-29234	30	13	(	(	PUNCT
fcis-29234	30	14	backbone	backbone	PROPN
fcis-29234	30	15	)	)	PUNCT
fcis-29234	30	16	,	,	PUNCT
fcis-29234	30	17	the	the	DET
fcis-29234	30	18	neck	neck	NOUN
fcis-29234	30	19	network	network	NOUN
fcis-29234	30	20	(	(	PUNCT
fcis-29234	30	21	neck	neck	NOUN
fcis-29234	30	22	)	)	PUNCT
fcis-29234	30	23	,	,	PUNCT
fcis-29234	30	24	and	and	CCONJ
fcis-29234	30	25	the	the	DET
fcis-29234	30	26	detection	detection	NOUN
fcis-29234	30	27	head	head	NOUN
fcis-29234	30	28	(	(	PUNCT
fcis-29234	30	29	head	head	NOUN
fcis-29234	30	30	)	)	PUNCT
fcis-29234	30	31	.	.	PUNCT
fcis-29234	31	1	the	the	DET
fcis-29234	31	2	backbone	backbone	NOUN
fcis-29234	31	3	network	network	NOUN
fcis-29234	31	4	of	of	ADP
fcis-29234	31	5	yolov8	yolov8	NOUN
fcis-29234	31	6	is	be	AUX
fcis-29234	31	7	responsible	responsible	ADJ
fcis-29234	31	8	for	for	ADP
fcis-29234	31	9	extracting	extract	VERB
fcis-29234	31	10	features	feature	NOUN
fcis-29234	31	11	from	from	ADP
fcis-29234	31	12	the	the	DET
fcis-29234	31	13	input	input	NOUN
fcis-29234	31	14	image	image	NOUN
fcis-29234	31	15	and	and	CCONJ
fcis-29234	31	16	transforming	transform	VERB
fcis-29234	31	17	the	the	DET
fcis-29234	31	18	image	image	NOUN
fcis-29234	31	19	into	into	ADP
fcis-29234	31	20	a	a	DET
fcis-29234	31	21	feature	feature	NOUN
fcis-29234	31	22	representation	representation	NOUN
fcis-29234	31	23	with	with	ADP
fcis-29234	31	24	rich	rich	ADJ
fcis-29234	31	25	semantic	semantic	ADJ
fcis-29234	31	26	information.yolov8n	information.yolov8n	NOUN
fcis-29234	31	27	adopts	adopt	VERB
fcis-29234	31	28	darknet-53	darknet-53	PROPN
fcis-29234	31	29	as	as	ADP
fcis-29234	31	30	the	the	DET
fcis-29234	31	31	backbone	backbone	NOUN
fcis-29234	31	32	network	network	NOUN
fcis-29234	31	33	and	and	CCONJ
fcis-29234	31	34	introduces	introduce	VERB
fcis-29234	31	35	the	the	DET
fcis-29234	31	36	c2f	c2f	NOUN
fcis-29234	31	37	module	module	NOUN
fcis-29234	31	38	for	for	ADP
fcis-29234	31	39	residual	residual	ADJ
fcis-29234	31	40	learning	learning	NOUN
fcis-29234	31	41	.	.	PUNCT
fcis-29234	32	1	compared	compare	VERB
fcis-29234	32	2	with	with	ADP
fcis-29234	32	3	the	the	DET
fcis-29234	32	4	c3	c3	PROPN
fcis-29234	32	5	module	module	NOUN
fcis-29234	32	6	in	in	ADP
fcis-29234	32	7	yolov5	yolov5	PROPN
fcis-29234	32	8	,	,	PUNCT
fcis-29234	32	9	the	the	DET
fcis-29234	32	10	c2f	c2f	NOUN
fcis-29234	32	11	module	module	NOUN
fcis-29234	32	12	has	have	VERB
fcis-29234	32	13	better	well	ADJ
fcis-29234	32	14	feature	feature	NOUN
fcis-29234	32	15	extraction	extraction	NOUN
fcis-29234	32	16	capability	capability	NOUN
fcis-29234	32	17	while	while	SCONJ
fcis-29234	32	18	maintaining	maintain	VERB
fcis-29234	32	19	a	a	DET
fcis-29234	32	20	smaller	small	ADJ
fcis-29234	32	21	number	number	NOUN
fcis-29234	32	22	of	of	ADP
fcis-29234	32	23	parameters	parameter	NOUN
fcis-29234	32	24	.	.	PUNCT
fcis-29234	33	1	specifically	specifically	ADV
fcis-29234	33	2	,	,	PUNCT
fcis-29234	33	3	the	the	DET
fcis-29234	33	4	c2f	c2f	NOUN
fcis-29234	33	5	module	module	NOUN
fcis-29234	33	6	adopts	adopt	VERB
fcis-29234	33	7	a	a	DET
fcis-29234	33	8	cross	cross	ADJ
fcis-29234	33	9	-	-	ADJ
fcis-29234	33	10	convolutional	convolutional	ADJ
fcis-29234	33	11	structure	structure	NOUN
fcis-29234	33	12	combined	combine	VERB
fcis-29234	33	13	with	with	ADP
fcis-29234	33	14	bottleneckblock	bottleneckblock	NOUN
fcis-29234	33	15	and	and	CCONJ
fcis-29234	33	16	sppf	sppf	ADJ
fcis-29234	33	17	(	(	PUNCT
fcis-29234	33	18	spatialpyramidpoolingfast	spatialpyramidpoolingfast	NOUN
fcis-29234	33	19	)	)	PUNCT
fcis-29234	33	20	modules	module	NOUN
fcis-29234	33	21	to	to	PART
fcis-29234	33	22	enhance	enhance	VERB
fcis-29234	33	23	the	the	DET
fcis-29234	33	24	feature	feature	NOUN
fcis-29234	33	25	extraction	extraction	NOUN
fcis-29234	33	26	capability	capability	NOUN
fcis-29234	33	27	.	.	PUNCT
fcis-29234	34	1	this	this	DET
fcis-29234	34	2	structural	structural	ADJ
fcis-29234	34	3	design	design	NOUN
fcis-29234	34	4	not	not	PART
fcis-29234	34	5	only	only	ADV
fcis-29234	34	6	reduces	reduce	VERB
fcis-29234	34	7	redundant	redundant	ADJ
fcis-29234	34	8	parameters	parameter	NOUN
fcis-29234	34	9	,	,	PUNCT
fcis-29234	34	10	but	but	CCONJ
fcis-29234	34	11	also	also	ADV
fcis-29234	34	12	enhances	enhance	VERB
fcis-29234	34	13	the	the	DET
fcis-29234	34	14	computational	computational	ADJ
fcis-29234	34	15	efficiency	efficiency	NOUN
fcis-29234	34	16	.	.	PUNCT
fcis-29234	35	1	in	in	ADP
fcis-29234	35	2	the	the	DET
fcis-29234	35	3	backbone	backbone	NOUN
fcis-29234	35	4	network	network	NOUN
fcis-29234	35	5	,	,	PUNCT
fcis-29234	35	6	the	the	DET
fcis-29234	35	7	conv	conv	ADJ
fcis-29234	35	8	convolutional	convolutional	ADJ
fcis-29234	35	9	module	module	NOUN
fcis-29234	35	10	and	and	CCONJ
fcis-29234	35	11	the	the	DET
fcis-29234	35	12	c2f	c2f	NOUN
fcis-29234	35	13	module	module	NOUN
fcis-29234	35	14	are	be	AUX
fcis-29234	35	15	stacked	stack	VERB
fcis-29234	35	16	in	in	ADP
fcis-29234	35	17	tandem	tandem	PROPN
fcis-29234	35	18	four	four	NUM
fcis-29234	35	19	times	time	NOUN
fcis-29234	35	20	,	,	PUNCT
fcis-29234	35	21	and	and	CCONJ
fcis-29234	35	22	each	each	DET
fcis-29234	35	23	stack	stack	NOUN
fcis-29234	35	24	is	be	AUX
fcis-29234	35	25	called	call	VERB
fcis-29234	35	26	a	a	DET
fcis-29234	35	27	stage	stage	NOUN
fcis-29234	35	28	.	.	PUNCT
fcis-29234	36	1	this	this	DET
fcis-29234	36	2	design	design	NOUN
fcis-29234	36	3	allows	allow	VERB
fcis-29234	36	4	yolov8n	yolov8n	NOUN
fcis-29234	36	5	to	to	PART
fcis-29234	36	6	incorporate	incorporate	VERB
fcis-29234	36	7	richer	rich	ADJ
fcis-29234	36	8	global	global	ADJ
fcis-29234	36	9	features	feature	NOUN
fcis-29234	36	10	while	while	SCONJ
fcis-29234	36	11	remaining	remain	VERB
fcis-29234	36	12	lightweight	lightweight	ADJ
fcis-29234	36	13	.	.	PUNCT
fcis-29234	37	1	finally	finally	ADV
fcis-29234	37	2	,	,	PUNCT
fcis-29234	37	3	the	the	DET
fcis-29234	37	4	different	different	ADJ
fcis-29234	37	5	feature	feature	NOUN
fcis-29234	37	6	layers	layer	NOUN
fcis-29234	37	7	are	be	AUX
fcis-29234	37	8	encoded	encode	VERB
fcis-29234	37	9	using	use	VERB
fcis-29234	37	10	the	the	DET
fcis-29234	37	11	sppf	sppf	ADJ
fcis-29234	37	12	module	module	NOUN
fcis-29234	37	13	,	,	PUNCT
fcis-29234	37	14	and	and	CCONJ
fcis-29234	37	15	the	the	DET
fcis-29234	37	16	processed	process	VERB
fcis-29234	37	17	feature	feature	NOUN
fcis-29234	37	18	maps	map	NOUN
fcis-29234	37	19	are	be	AUX
fcis-29234	37	20	fed	feed	VERB
fcis-29234	37	21	into	into	ADP
fcis-29234	37	22	the	the	DET
fcis-29234	37	23	neck	neck	NOUN
fcis-29234	37	24	network	network	NOUN
fcis-29234	37	25	for	for	ADP
fcis-29234	37	26	further	further	ADJ
fcis-29234	37	27	fusion	fusion	NOUN
fcis-29234	37	28	and	and	CCONJ
fcis-29234	37	29	processing	processing	NOUN
fcis-29234	37	30	.	.	PUNCT
fcis-29234	38	1	the	the	DET
fcis-29234	38	2	neck	neck	NOUN
fcis-29234	38	3	network	network	NOUN
fcis-29234	38	4	is	be	AUX
fcis-29234	38	5	simplified	simplify	VERB
fcis-29234	38	6	compared	compare	VERB
fcis-29234	38	7	to	to	ADP
fcis-29234	38	8	yolov5	yolov5	NOUN
fcis-29234	38	9	by	by	ADP
fcis-29234	38	10	removing	remove	VERB
fcis-29234	38	11	the	the	DET
fcis-29234	38	12	two	two	NUM
fcis-29234	38	13	convolutional	convolutional	ADJ
fcis-29234	38	14	fully	fully	ADV
fcis-29234	38	15	-	-	PUNCT
fcis-29234	38	16	connected	connect	VERB
fcis-29234	38	17	layers	layer	NOUN
fcis-29234	38	18	and	and	CCONJ
fcis-29234	38	19	adopting	adopt	VERB
fcis-29234	38	20	a	a	DET
fcis-29234	38	21	pan	pan	ADJ
fcis-29234	38	22	-	-	ADJ
fcis-29234	38	23	fpn	fpn	ADJ
fcis-29234	38	24	structure	structure	NOUN
fcis-29234	38	25	,	,	PUNCT
fcis-29234	38	26	which	which	PRON
fcis-29234	38	27	is	be	AUX
fcis-29234	38	28	responsible	responsible	ADJ
fcis-29234	38	29	for	for	ADP
fcis-29234	38	30	multi	multi	ADJ
fcis-29234	38	31	-	-	ADJ
fcis-29234	38	32	scale	scale	ADJ
fcis-29234	38	33	feature	feature	NOUN
fcis-29234	38	34	fusion	fusion	NOUN
fcis-29234	38	35	.	.	PUNCT
fcis-29234	39	1	this	this	DET
fcis-29234	39	2	structure	structure	NOUN
fcis-29234	39	3	enhances	enhance	VERB
fcis-29234	39	4	the	the	DET
fcis-29234	39	5	feature	feature	NOUN
fcis-29234	39	6	representation	representation	NOUN
fcis-29234	39	7	capability	capability	NOUN
fcis-29234	39	8	by	by	ADP
fcis-29234	39	9	fusing	fuse	VERB
fcis-29234	39	10	feature	feature	NOUN
fcis-29234	39	11	maps	map	NOUN
fcis-29234	39	12	from	from	ADP
fcis-29234	39	13	different	different	ADJ
fcis-29234	39	14	stages	stage	NOUN
fcis-29234	39	15	of	of	ADP
fcis-29234	39	16	the	the	DET
fcis-29234	39	17	backbone	backbone	NOUN
fcis-29234	39	18	network	network	NOUN
fcis-29234	39	19	.	.	PUNCT
fcis-29234	40	1	specifically	specifically	ADV
fcis-29234	40	2	,	,	PUNCT
fcis-29234	40	3	the	the	DET
fcis-29234	40	4	neck	neck	NOUN
fcis-29234	40	5	network	network	NOUN
fcis-29234	40	6	contains	contain	VERB
fcis-29234	40	7	the	the	DET
fcis-29234	40	8	sppf	sppf	ADJ
fcis-29234	40	9	module	module	NOUN
fcis-29234	40	10	,	,	PUNCT
fcis-29234	40	11	the	the	DET
fcis-29234	40	12	paa	paa	NOUN
fcis-29234	40	13	module	module	NOUN
fcis-29234	40	14	,	,	PUNCT
fcis-29234	40	15	and	and	CCONJ
fcis-29234	40	16	the	the	DET
fcis-29234	40	17	pan	pan	NOUN
fcis-29234	40	18	module	module	NOUN
fcis-29234	40	19	,	,	PUNCT
fcis-29234	40	20	which	which	PRON
fcis-29234	40	21	work	work	VERB
fcis-29234	40	22	together	together	ADV
fcis-29234	40	23	to	to	PART
fcis-29234	40	24	enable	enable	VERB
fcis-29234	40	25	the	the	DET
fcis-29234	40	26	yolov8n	yolov8n	NOUN
fcis-29234	40	27	to	to	PART
fcis-29234	40	28	effectively	effectively	ADV
fcis-29234	40	29	utilize	utilize	VERB
fcis-29234	40	30	the	the	DET
fcis-29234	40	31	features	feature	NOUN
fcis-29234	40	32	extracted	extract	VERB
fcis-29234	40	33	from	from	ADP
fcis-29234	40	34	the	the	DET
fcis-29234	40	35	backbone	backbone	NOUN
fcis-29234	40	36	network	network	NOUN
fcis-29234	40	37	to	to	PART
fcis-29234	40	38	enhance	enhance	VERB
fcis-29234	40	39	the	the	DET
fcis-29234	40	40	model	model	NOUN
fcis-29234	40	41	's	's	PART
fcis-29234	40	42	performance	performance	NOUN
fcis-29234	40	43	in	in	ADP
fcis-29234	40	44	the	the	DET
fcis-29234	40	45	target	target	NOUN
fcis-29234	40	46	detection	detection	NOUN
fcis-29234	40	47	task	task	NOUN
fcis-29234	40	48	.	.	PUNCT
fcis-29234	41	1	with	with	ADP
fcis-29234	41	2	the	the	DET
fcis-29234	41	3	pan	pan	NOUN
fcis-29234	41	4	-	-	ADJ
fcis-29234	41	5	fpn	fpn	ADJ
fcis-29234	41	6	structure	structure	NOUN
fcis-29234	41	7	,	,	PUNCT
fcis-29234	41	8	the	the	DET
fcis-29234	41	9	neck	neck	NOUN
fcis-29234	41	10	network	network	NOUN
fcis-29234	41	11	achieves	achieve	VERB
fcis-29234	41	12	efficient	efficient	ADJ
fcis-29234	41	13	multi	multi	ADJ
fcis-29234	41	14	-	-	ADJ
fcis-29234	41	15	level	level	ADJ
fcis-29234	41	16	feature	feature	NOUN
fcis-29234	41	17	fusion	fusion	NOUN
fcis-29234	41	18	and	and	CCONJ
fcis-29234	41	19	enhances	enhance	VERB
fcis-29234	41	20	the	the	DET
fcis-29234	41	21	detection	detection	NOUN
fcis-29234	41	22	of	of	ADP
fcis-29234	41	23	multi	multi	ADJ
fcis-29234	41	24	-	-	ADJ
fcis-29234	41	25	scale	scale	ADJ
fcis-29234	41	26	targets	target	NOUN
fcis-29234	41	27	,	,	PUNCT
fcis-29234	41	28	thus	thus	ADV
fcis-29234	41	29	improving	improve	VERB
fcis-29234	41	30	the	the	DET
fcis-29234	41	31	overall	overall	ADJ
fcis-29234	41	32	model	model	NOUN
fcis-29234	41	33	accuracy	accuracy	NOUN
fcis-29234	41	34	and	and	CCONJ
fcis-29234	41	35	robustness	robustness	NOUN
fcis-29234	41	36	.	.	PUNCT
fcis-29234	42	1	this	this	DET
fcis-29234	42	2	design	design	NOUN
fcis-29234	42	3	implements	implement	VERB
fcis-29234	42	4	top	top	ADJ
fcis-29234	42	5	-	-	PUNCT
fcis-29234	42	6	down	down	NOUN
fcis-29234	42	7	and	and	CCONJ
fcis-29234	42	8	bottom	bottom	ADJ
fcis-29234	42	9	-	-	PUNCT
fcis-29234	42	10	up	up	ADP
fcis-29234	42	11	feature	feature	NOUN
fcis-29234	42	12	pyramids	pyramid	NOUN
fcis-29234	42	13	to	to	PART
fcis-29234	42	14	fuse	fuse	VERB
fcis-29234	42	15	different	different	ADJ
fcis-29234	42	16	features	feature	NOUN
fcis-29234	42	17	processed	process	VERB
fcis-29234	42	18	by	by	ADP
fcis-29234	42	19	the	the	DET
fcis-29234	42	20	backbone	backbone	NOUN
fcis-29234	42	21	network	network	NOUN
fcis-29234	42	22	.	.	PUNCT
fcis-29234	43	1	in	in	ADP
fcis-29234	43	2	the	the	DET
fcis-29234	43	3	detection	detection	NOUN
fcis-29234	43	4	head	head	NOUN
fcis-29234	43	5	part	part	NOUN
fcis-29234	43	6	,	,	PUNCT
fcis-29234	43	7	yolov8	yolov8	PROPN
fcis-29234	43	8	adopts	adopt	VERB
fcis-29234	43	9	the	the	DET
fcis-29234	43	10	current	current	ADJ
fcis-29234	43	11	mainstream	mainstream	NOUN
fcis-29234	43	12	decoupled	decouple	VERB
fcis-29234	43	13	-	-	PUNCT
fcis-29234	43	14	head	head	NOUN
fcis-29234	43	15	structure	structure	NOUN
fcis-29234	43	16	instead	instead	ADV
fcis-29234	43	17	of	of	ADP
fcis-29234	43	18	the	the	DET
fcis-29234	43	19	coupled	couple	VERB
fcis-29234	43	20	-	-	PUNCT
fcis-29234	43	21	head	head	NOUN
fcis-29234	43	22	structure	structure	NOUN
fcis-29234	43	23	of	of	ADP
fcis-29234	43	24	yolov5	yolov5	NOUN
fcis-29234	43	25	.	.	PUNCT
fcis-29234	44	1	the	the	DET
fcis-29234	44	2	decoupled	decouple	VERB
fcis-29234	44	3	-	-	PUNCT
fcis-29234	44	4	head	head	NOUN
fcis-29234	44	5	handles	handle	VERB
fcis-29234	44	6	the	the	DET
fcis-29234	44	7	classification	classification	NOUN
fcis-29234	44	8	and	and	CCONJ
fcis-29234	44	9	detection	detection	NOUN
fcis-29234	44	10	tasks	task	NOUN
fcis-29234	44	11	separately	separately	ADV
fcis-29234	44	12	,	,	PUNCT
fcis-29234	44	13	and	and	CCONJ
fcis-29234	44	14	at	at	ADP
fcis-29234	44	15	the	the	DET
fcis-29234	44	16	same	same	ADJ
fcis-29234	44	17	time	time	NOUN
fcis-29234	44	18	converts	convert	NOUN
fcis-29234	44	19	from	from	ADP
fcis-29234	44	20	an	an	DET
fcis-29234	44	21	anchor	anchor	NOUN
fcis-29234	44	22	-	-	PUNCT
fcis-29234	44	23	frame	frame	NOUN
fcis-29234	44	24	mechanism	mechanism	NOUN
fcis-29234	44	25	to	to	ADP
fcis-29234	44	26	an	an	DET
fcis-29234	44	27	anchor	anchor	NOUN
fcis-29234	44	28	-	-	PUNCT
fcis-29234	44	29	free	free	ADJ
fcis-29234	44	30	mechanism	mechanism	NOUN
fcis-29234	44	31	(	(	PUNCT
fcis-29234	44	32	anchor	anchor	NOUN
fcis-29234	44	33	-	-	PUNCT
fcis-29234	44	34	free	free	ADJ
fcis-29234	44	35	)	)	PUNCT
fcis-29234	44	36	,	,	PUNCT
fcis-29234	44	37	which	which	PRON
fcis-29234	44	38	improves	improve	VERB
fcis-29234	44	39	the	the	DET
fcis-29234	44	40	detection	detection	NOUN
fcis-29234	44	41	efficiency	efficiency	NOUN
fcis-29234	44	42	and	and	CCONJ
fcis-29234	44	43	accuracy	accuracy	NOUN
fcis-29234	44	44	.	.	PUNCT
fcis-29234	45	1	the	the	DET
fcis-29234	45	2	detection	detection	NOUN
fcis-29234	45	3	head	head	NOUN
fcis-29234	45	4	part	part	NOUN
fcis-29234	45	5	is	be	AUX
fcis-29234	45	6	responsible	responsible	ADJ
fcis-29234	45	7	for	for	ADP
fcis-29234	45	8	the	the	DET
fcis-29234	45	9	final	final	ADJ
fcis-29234	45	10	prediction	prediction	NOUN
fcis-29234	45	11	tasks	task	NOUN
fcis-29234	45	12	,	,	PUNCT
fcis-29234	45	13	including	include	VERB
fcis-29234	45	14	bounding	bound	VERB
fcis-29234	45	15	box	box	NOUN
fcis-29234	45	16	regression	regression	NOUN
fcis-29234	45	17	,	,	PUNCT
fcis-29234	45	18	target	target	VERB
fcis-29234	45	19	classification	classification	NOUN
fcis-29234	45	20	,	,	PUNCT
fcis-29234	45	21	and	and	CCONJ
fcis-29234	45	22	confidence	confidence	NOUN
fcis-29234	45	23	prediction.the	prediction.the	DET
fcis-29234	45	24	detection	detection	NOUN
fcis-29234	45	25	head	head	NOUN
fcis-29234	45	26	of	of	ADP
fcis-29234	45	27	yolov8	yolov8	NOUN
fcis-29234	45	28	consists	consist	VERB
fcis-29234	45	29	of	of	ADP
fcis-29234	45	30	a	a	DET
fcis-29234	45	31	convolutional	convolutional	ADJ
fcis-29234	45	32	layer	layer	NOUN
fcis-29234	45	33	,	,	PUNCT
fcis-29234	45	34	a	a	DET
fcis-29234	45	35	global	global	ADJ
fcis-29234	45	36	average	average	ADJ
fcis-29234	45	37	pooling	pool	VERB
fcis-29234	45	38	layer	layer	NOUN
fcis-29234	45	39	,	,	PUNCT
fcis-29234	45	40	and	and	CCONJ
fcis-29234	45	41	a	a	DET
fcis-29234	45	42	loss	loss	NOUN
fcis-29234	45	43	function	function	NOUN
fcis-29234	45	44	.	.	PUNCT
fcis-29234	46	1	the	the	DET
fcis-29234	46	2	convolutional	convolutional	ADJ
fcis-29234	46	3	layer	layer	NOUN
fcis-29234	46	4	generates	generate	VERB
fcis-29234	46	5	the	the	DET
fcis-29234	46	6	detection	detection	NOUN
fcis-29234	46	7	results	result	VERB
fcis-29234	46	8	through	through	ADP
fcis-29234	46	9	a	a	DET
fcis-29234	46	10	series	series	NOUN
fcis-29234	46	11	of	of	ADP
fcis-29234	46	12	convolutional	convolutional	ADJ
fcis-29234	46	13	and	and	CCONJ
fcis-29234	46	14	deconvolutional	deconvolutional	ADJ
fcis-29234	46	15	operations	operation	NOUN
fcis-29234	46	16	,	,	PUNCT
fcis-29234	46	17	and	and	CCONJ
fcis-29234	46	18	is	be	AUX
fcis-29234	46	19	responsible	responsible	ADJ
fcis-29234	46	20	for	for	ADP
fcis-29234	46	21	predicting	predict	VERB
fcis-29234	46	22	the	the	DET
fcis-29234	46	23	bounding	bounding	NOUN
fcis-29234	46	24	box	box	NOUN
fcis-29234	46	25	regression	regression	NOUN
fcis-29234	46	26	value	value	NOUN
fcis-29234	46	27	and	and	CCONJ
fcis-29234	46	28	the	the	DET
fcis-29234	46	29	confidence	confidence	NOUN
fcis-29234	46	30	of	of	ADP
fcis-29234	46	31	target	target	NOUN
fcis-29234	46	32	presence	presence	NOUN
fcis-29234	46	33	for	for	ADP
fcis-29234	46	34	each	each	DET
fcis-29234	46	35	anchor	anchor	NOUN
fcis-29234	46	36	box	box	PROPN
fcis-29234	46	37	.	.	PUNCT
fcis-29234	47	1	the	the	DET
fcis-29234	47	2	global	global	ADJ
fcis-29234	47	3	average	average	ADJ
fcis-29234	47	4	pooling	pooling	NOUN
fcis-29234	47	5	layer	layer	NOUN
fcis-29234	47	6	enhances	enhance	VERB
fcis-29234	47	7	the	the	DET
fcis-29234	47	8	model	model	NOUN
fcis-29234	47	9	's	's	PART
fcis-29234	47	10	ability	ability	NOUN
fcis-29234	47	11	to	to	PART
fcis-29234	47	12	handle	handle	VERB
fcis-29234	47	13	multicategory	multicategory	ADJ
fcis-29234	47	14	classification	classification	NOUN
fcis-29234	47	15	tasks	task	NOUN
fcis-29234	47	16	by	by	ADP
fcis-29234	47	17	reducing	reduce	VERB
fcis-29234	47	18	the	the	DET
fcis-29234	47	19	dimensionality	dimensionality	NOUN
fcis-29234	47	20	of	of	ADP
fcis-29234	47	21	the	the	DET
fcis-29234	47	22	feature	feature	NOUN
fcis-29234	47	23	map	map	NOUN
fcis-29234	47	24	and	and	CCONJ
fcis-29234	47	25	outputting	output	VERB
fcis-29234	47	26	a	a	DET
fcis-29234	47	27	probability	probability	NOUN
fcis-29234	47	28	distribution	distribution	NOUN
fcis-29234	47	29	for	for	ADP
fcis-29234	47	30	each	each	DET
fcis-29234	47	31	category	category	NOUN
fcis-29234	47	32	.	.	PUNCT
fcis-29234	48	1	for	for	ADP
fcis-29234	48	2	the	the	DET
fcis-29234	48	3	loss	loss	NOUN
fcis-29234	48	4	function	function	NOUN
fcis-29234	48	5	part	part	NOUN
fcis-29234	48	6	,	,	PUNCT
fcis-29234	48	7	yolov8	yolov8	PROPN
fcis-29234	48	8	employs	employ	VERB
fcis-29234	48	9	task	task	NOUN
fcis-29234	48	10	-	-	PUNCT
fcis-29234	48	11	alignedassigner	alignedassigner	NOUN
fcis-29234	48	12	positive	positive	ADJ
fcis-29234	48	13	and	and	CCONJ
fcis-29234	48	14	negative	negative	ADJ
fcis-29234	48	15	sample	sample	NOUN
fcis-29234	48	16	matching	matching	NOUN
fcis-29234	48	17	in	in	ADP
fcis-29234	48	18	the	the	DET
fcis-29234	48	19	detection	detection	NOUN
fcis-29234	48	20	header	header	NOUN
fcis-29234	48	21	and	and	CCONJ
fcis-29234	48	22	distributionfocalloss	distributionfocalloss	NOUN
fcis-29234	48	23	(	(	PUNCT
fcis-29234	48	24	dfl	dfl	PROPN
fcis-29234	48	25	)	)	PUNCT
fcis-29234	48	26	to	to	PART
fcis-29234	48	27	further	far	ADV
fcis-29234	48	28	enhance	enhance	VERB
fcis-29234	48	29	the	the	DET
fcis-29234	48	30	performance	performance	NOUN
fcis-29234	48	31	of	of	ADP
fcis-29234	48	32	the	the	DET
fcis-29234	48	33	model	model	NOUN
fcis-29234	48	34	.	.	PUNCT
fcis-29234	49	1	through	through	ADP
fcis-29234	49	2	these	these	DET
fcis-29234	49	3	optimizations	optimization	NOUN
fcis-29234	49	4	and	and	CCONJ
fcis-29234	49	5	improvements	improvement	NOUN
fcis-29234	49	6	,	,	PUNCT
fcis-29234	49	7	yolov8	yolov8	NOUN
fcis-29234	49	8	finally	finally	ADV
fcis-29234	49	9	and	and	CCONJ
fcis-29234	49	10	effectively	effectively	ADV
fcis-29234	49	11	improves	improve	VERB
fcis-29234	49	12	the	the	DET
fcis-29234	49	13	accuracy	accuracy	NOUN
fcis-29234	49	14	and	and	CCONJ
fcis-29234	49	15	robustness	robustness	NOUN
fcis-29234	49	16	of	of	ADP
fcis-29234	49	17	target	target	NOUN
fcis-29234	49	18	detection	detection	NOUN
fcis-29234	49	19	.	.	PUNCT
fcis-29234	50	1	the	the	DET
fcis-29234	50	2	application	application	NOUN
fcis-29234	50	3	of	of	ADP
fcis-29234	50	4	the	the	DET
fcis-29234	50	5	decoupled	decouple	VERB
fcis-29234	50	6	head	head	NOUN
fcis-29234	50	7	structure	structure	NOUN
fcis-29234	50	8	and	and	CCONJ
fcis-29234	50	9	the	the	DET
fcis-29234	50	10	anchorless	anchorless	ADJ
fcis-29234	50	11	mechanism	mechanism	NOUN
fcis-29234	50	12	makes	make	VERB
fcis-29234	50	13	the	the	DET
fcis-29234	50	14	model	model	NOUN
fcis-29234	50	15	more	more	ADV
fcis-29234	50	16	flexible	flexible	ADJ
fcis-29234	50	17	and	and	CCONJ
fcis-29234	50	18	efficient	efficient	ADJ
fcis-29234	50	19	in	in	ADP
fcis-29234	50	20	dealing	deal	VERB
fcis-29234	50	21	with	with	ADP
fcis-29234	50	22	targets	target	NOUN
fcis-29234	50	23	of	of	ADP
fcis-29234	50	24	different	different	ADJ
fcis-29234	50	25	sizes	size	NOUN
fcis-29234	50	26	and	and	CCONJ
fcis-29234	50	27	classes	class	NOUN
fcis-29234	50	28	.	.	PUNCT
fcis-29234	51	1	fig	fig	NOUN
fcis-29234	51	2	1	1	NUM
fcis-29234	51	3	.	.	PUNCT
fcis-29234	52	1	network	network	NOUN
fcis-29234	52	2	chart	chart	NOUN
fcis-29234	52	3	of	of	ADP
fcis-29234	52	4	yolov8	yolov8	PROPN
fcis-29234	52	5	55	55	NUM
fcis-29234	52	6	2.2	2.2	NUM
fcis-29234	52	7	.	.	PUNCT
fcis-29234	53	1	s	s	VERB
fcis-29234	53	2	optimization	optimization	NOUN
fcis-29234	53	3	of	of	ADP
fcis-29234	53	4	object	object	NOUN
fcis-29234	53	5	detectors	detector	NOUN
fcis-29234	53	6	for	for	ADP
fcis-29234	53	7	long	long	ADJ
fcis-29234	53	8	-	-	PUNCT
fcis-29234	53	9	distance	distance	NOUN
fcis-29234	53	10	small	small	ADJ
fcis-29234	53	11	vehicle	vehicle	NOUN
fcis-29234	53	12	targets	target	NOUN
fcis-29234	53	13	in	in	ADP
fcis-29234	53	14	vehicle	vehicle	NOUN
fcis-29234	53	15	re	re	NOUN
fcis-29234	53	16	-	-	NOUN
fcis-29234	53	17	identification	identification	NOUN
fcis-29234	53	18	tasks	task	NOUN
fcis-29234	53	19	,	,	PUNCT
fcis-29234	53	20	the	the	DET
fcis-29234	53	21	accuracy	accuracy	NOUN
fcis-29234	53	22	of	of	ADP
fcis-29234	53	23	object	object	NOUN
fcis-29234	53	24	detectors	detector	NOUN
fcis-29234	53	25	significantly	significantly	ADV
fcis-29234	53	26	affects	affect	VERB
fcis-29234	53	27	the	the	DET
fcis-29234	53	28	performance	performance	NOUN
fcis-29234	53	29	of	of	ADP
fcis-29234	53	30	reidentification	reidentification	NOUN
fcis-29234	53	31	.	.	PUNCT
fcis-29234	54	1	addressing	address	VERB
fcis-29234	54	2	the	the	DET
fcis-29234	54	3	challenge	challenge	NOUN
fcis-29234	54	4	of	of	ADP
fcis-29234	54	5	feature	feature	NOUN
fcis-29234	54	6	extraction	extraction	NOUN
fcis-29234	54	7	for	for	ADP
fcis-29234	54	8	long	long	ADJ
fcis-29234	54	9	-	-	PUNCT
fcis-29234	54	10	distance	distance	NOUN
fcis-29234	54	11	small	small	ADJ
fcis-29234	54	12	targets	target	NOUN
fcis-29234	54	13	,	,	PUNCT
fcis-29234	54	14	this	this	DET
fcis-29234	54	15	study	study	NOUN
fcis-29234	54	16	focuses	focus	VERB
fcis-29234	54	17	on	on	ADP
fcis-29234	54	18	optimizing	optimize	VERB
fcis-29234	54	19	the	the	DET
fcis-29234	54	20	yolov8	yolov8	NOUN
fcis-29234	54	21	object	object	NOUN
fcis-29234	54	22	detector	detector	NOUN
fcis-29234	54	23	.	.	PUNCT
fcis-29234	55	1	the	the	DET
fcis-29234	55	2	proposed	propose	VERB
fcis-29234	55	3	enhancements	enhancement	NOUN
fcis-29234	55	4	aim	aim	VERB
fcis-29234	55	5	to	to	PART
fcis-29234	55	6	improve	improve	VERB
fcis-29234	55	7	the	the	DET
fcis-29234	55	8	detection	detection	NOUN
fcis-29234	55	9	accuracy	accuracy	NOUN
fcis-29234	55	10	of	of	ADP
fcis-29234	55	11	small	small	ADJ
fcis-29234	55	12	targets	target	NOUN
fcis-29234	55	13	,	,	PUNCT
fcis-29234	55	14	thereby	thereby	ADV
fcis-29234	55	15	optimizing	optimize	VERB
fcis-29234	55	16	multi	multi	ADJ
fcis-29234	55	17	-	-	ADJ
fcis-29234	55	18	object	object	ADJ
fcis-29234	55	19	tracking	tracking	NOUN
fcis-29234	55	20	algorithms	algorithm	NOUN
fcis-29234	55	21	.	.	PUNCT
fcis-29234	56	1	the	the	DET
fcis-29234	56	2	architecture	architecture	NOUN
fcis-29234	56	3	of	of	ADP
fcis-29234	56	4	the	the	DET
fcis-29234	56	5	improved	improved	ADJ
fcis-29234	56	6	yolov8	yolov8	NOUN
fcis-29234	56	7	-	-	PUNCT
fcis-29234	56	8	ema	ema	PROPN
fcis-29234	56	9	-	-	PUNCT
fcis-29234	56	10	rfb	rfb	PROPN
fcis-29234	56	11	object	object	NOUN
fcis-29234	56	12	detection	detection	NOUN
fcis-29234	56	13	algorithm	algorithm	NOUN
fcis-29234	56	14	is	be	AUX
fcis-29234	56	15	illustrated	illustrate	VERB
fcis-29234	56	16	in	in	ADP
fcis-29234	56	17	fig	fig	NOUN
fcis-29234	56	18	.	.	PUNCT
fcis-29234	57	1	2	2	X
fcis-29234	57	2	.	.	X
fcis-29234	57	3	fig	fig	NOUN
fcis-29234	57	4	2	2	NUM
fcis-29234	57	5	.	.	PUNCT
fcis-29234	58	1	the	the	DET
fcis-29234	58	2	architecture	architecture	NOUN
fcis-29234	58	3	diagram	diagram	NOUN
fcis-29234	58	4	of	of	ADP
fcis-29234	58	5	the	the	DET
fcis-29234	58	6	yolov8	yolov8	PROPN
fcis-29234	58	7	-	-	PUNCT
fcis-29234	58	8	ema	ema	PROPN
fcis-29234	58	9	-	-	PUNCT
fcis-29234	58	10	rfb	rfb	PROPN
fcis-29234	58	11	object	object	NOUN
fcis-29234	58	12	detection	detection	NOUN
fcis-29234	58	13	algorithm	algorithm	NOUN
fcis-29234	58	14	2.2.1	2.2.1	NUM
fcis-29234	58	15	.	.	PUNCT
fcis-29234	59	1	integration	integration	NOUN
fcis-29234	59	2	of	of	ADP
fcis-29234	59	3	the	the	DET
fcis-29234	59	4	attention	attention	NOUN
fcis-29234	59	5	module	module	NOUN
fcis-29234	59	6	fig	fig	NOUN
fcis-29234	59	7	3	3	NUM
fcis-29234	59	8	.	.	PUNCT
fcis-29234	60	1	the	the	DET
fcis-29234	60	2	overall	overall	ADJ
fcis-29234	60	3	structure	structure	NOUN
fcis-29234	60	4	of	of	ADP
fcis-29234	60	5	the	the	DET
fcis-29234	60	6	ema	ema	PROPN
fcis-29234	60	7	module	module	NOUN
fcis-29234	60	8	the	the	DET
fcis-29234	60	9	ema	ema	PROPN
fcis-29234	60	10	(	(	PUNCT
fcis-29234	60	11	exponential	exponential	NOUN
fcis-29234	60	12	moving	move	VERB
fcis-29234	60	13	average	average	ADJ
fcis-29234	60	14	)	)	PUNCT
fcis-29234	60	15	attention	attention	NOUN
fcis-29234	60	16	module	module	NOUN
fcis-29234	60	17	is	be	AUX
fcis-29234	60	18	added	add	VERB
fcis-29234	60	19	to	to	ADP
fcis-29234	60	20	the	the	DET
fcis-29234	60	21	backbone	backbone	NOUN
fcis-29234	60	22	of	of	ADP
fcis-29234	60	23	yolov8	yolov8	NOUN
fcis-29234	60	24	to	to	PART
fcis-29234	60	25	enhance	enhance	VERB
fcis-29234	60	26	feature	feature	NOUN
fcis-29234	60	27	representation	representation	NOUN
fcis-29234	60	28	and	and	CCONJ
fcis-29234	60	29	improve	improve	VERB
fcis-29234	60	30	overall	overall	ADJ
fcis-29234	60	31	performance	performance	NOUN
fcis-29234	60	32	.	.	PUNCT
fcis-29234	61	1	ema	ema	PROPN
fcis-29234	61	2	utilizes	utilize	VERB
fcis-29234	61	3	exponential	exponential	PROPN
fcis-29234	61	4	weighted	weight	VERB
fcis-29234	61	5	moving	move	VERB
fcis-29234	61	6	averages	average	NOUN
fcis-29234	61	7	to	to	PART
fcis-29234	61	8	effectively	effectively	ADV
fcis-29234	61	9	focus	focus	VERB
fcis-29234	61	10	on	on	ADP
fcis-29234	61	11	important	important	ADJ
fcis-29234	61	12	features	feature	NOUN
fcis-29234	61	13	while	while	SCONJ
fcis-29234	61	14	smoothing	smooth	VERB
fcis-29234	61	15	noise	noise	NOUN
fcis-29234	61	16	,	,	PUNCT
fcis-29234	61	17	which	which	PRON
fcis-29234	61	18	enhances	enhance	VERB
fcis-29234	61	19	the	the	DET
fcis-29234	61	20	ability	ability	NOUN
fcis-29234	61	21	to	to	PART
fcis-29234	61	22	extract	extract	VERB
fcis-29234	61	23	key	key	ADJ
fcis-29234	61	24	features	feature	NOUN
fcis-29234	61	25	.	.	PUNCT
fcis-29234	62	1	compared	compare	VERB
fcis-29234	62	2	to	to	ADP
fcis-29234	62	3	other	other	ADJ
fcis-29234	62	4	complex	complex	ADJ
fcis-29234	62	5	attention	attention	NOUN
fcis-29234	62	6	mechanisms	mechanism	NOUN
fcis-29234	62	7	,	,	PUNCT
fcis-29234	62	8	ema	ema	PROPN
fcis-29234	62	9	has	have	VERB
fcis-29234	62	10	lower	low	ADJ
fcis-29234	62	11	computational	computational	ADJ
fcis-29234	62	12	complexity	complexity	NOUN
fcis-29234	62	13	,	,	PUNCT
fcis-29234	62	14	maintaining	maintain	VERB
fcis-29234	62	15	high	high	ADJ
fcis-29234	62	16	computational	computational	ADJ
fcis-29234	62	17	efficiency	efficiency	NOUN
fcis-29234	62	18	while	while	SCONJ
fcis-29234	62	19	improving	improve	VERB
fcis-29234	62	20	performance	performance	NOUN
fcis-29234	62	21	.	.	PUNCT
fcis-29234	63	1	ema	ema	PROPN
fcis-29234	63	2	is	be	AUX
fcis-29234	63	3	adaptable	adaptable	ADJ
fcis-29234	63	4	to	to	ADP
fcis-29234	63	5	various	various	ADJ
fcis-29234	63	6	visual	visual	ADJ
fcis-29234	63	7	tasks	task	NOUN
fcis-29234	63	8	and	and	CCONJ
fcis-29234	63	9	enhances	enhance	VERB
fcis-29234	63	10	the	the	DET
fcis-29234	63	11	model	model	NOUN
fcis-29234	63	12	's	's	PART
fcis-29234	63	13	ability	ability	NOUN
fcis-29234	63	14	to	to	PART
fcis-29234	63	15	handle	handle	VERB
fcis-29234	63	16	different	different	ADJ
fcis-29234	63	17	tasks	task	NOUN
fcis-29234	63	18	effectively	effectively	ADV
fcis-29234	63	19	.	.	PUNCT
fcis-29234	64	1	the	the	DET
fcis-29234	64	2	overall	overall	ADJ
fcis-29234	64	3	structure	structure	NOUN
fcis-29234	64	4	of	of	ADP
fcis-29234	64	5	the	the	DET
fcis-29234	64	6	ema	ema	PROPN
fcis-29234	64	7	module	module	NOUN
fcis-29234	64	8	is	be	AUX
fcis-29234	64	9	shown	show	VERB
fcis-29234	64	10	in	in	ADP
fcis-29234	64	11	fig.3	fig.3	PROPN
fcis-29234	64	12	.	.	PUNCT
fcis-29234	65	1	in	in	ADP
fcis-29234	65	2	this	this	DET
fcis-29234	65	3	section	section	NOUN
fcis-29234	65	4	,	,	PUNCT
fcis-29234	65	5	we	we	PRON
fcis-29234	65	6	will	will	AUX
fcis-29234	65	7	discuss	discuss	VERB
fcis-29234	65	8	how	how	SCONJ
fcis-29234	65	9	ema	ema	PROPN
fcis-29234	65	10	learns	learn	VERB
fcis-29234	65	11	effective	effective	ADJ
fcis-29234	65	12	channel	channel	NOUN
fcis-29234	65	13	descriptions	description	NOUN
fcis-29234	65	14	in	in	ADP
fcis-29234	65	15	convolutional	convolutional	ADJ
fcis-29234	65	16	operations	operation	NOUN
fcis-29234	65	17	without	without	ADP
fcis-29234	65	18	compressing	compress	VERB
fcis-29234	65	19	the	the	DET
fcis-29234	65	20	channel	channel	NOUN
fcis-29234	65	21	dimension	dimension	NOUN
fcis-29234	65	22	,	,	PUNCT
fcis-29234	65	23	thus	thus	ADV
fcis-29234	65	24	generating	generate	VERB
fcis-29234	65	25	superior	superior	ADJ
fcis-29234	65	26	pixel	pixel	ADJ
fcis-29234	65	27	-	-	PUNCT
fcis-29234	65	28	level	level	NOUN
fcis-29234	65	29	attention	attention	NOUN
fcis-29234	65	30	for	for	ADP
fcis-29234	65	31	high	high	ADJ
fcis-29234	65	32	-	-	PUNCT
fcis-29234	65	33	level	level	NOUN
fcis-29234	65	34	feature	feature	NOUN
fcis-29234	65	35	maps	map	NOUN
fcis-29234	65	36	.	.	PUNCT
fcis-29234	66	1	specifically	specifically	ADV
fcis-29234	66	2	,	,	PUNCT
fcis-29234	66	3	we	we	PRON
fcis-29234	66	4	extract	extract	VERB
fcis-29234	66	5	the	the	DET
fcis-29234	66	6	shared	share	VERB
fcis-29234	66	7	1x1	1x1	NUM
fcis-29234	66	8	convolution	convolution	NOUN
fcis-29234	66	9	component	component	NOUN
fcis-29234	66	10	from	from	ADP
fcis-29234	66	11	the	the	DET
fcis-29234	66	12	ca	ca	NOUN
fcis-29234	66	13	(	(	PUNCT
fcis-29234	66	14	coordinate	coordinate	VERB
fcis-29234	66	15	attention	attention	NOUN
fcis-29234	66	16	)	)	PUNCT
fcis-29234	66	17	module	module	NOUN
fcis-29234	66	18	and	and	CCONJ
fcis-29234	66	19	refer	refer	VERB
fcis-29234	66	20	to	to	ADP
fcis-29234	66	21	it	it	PRON
fcis-29234	66	22	as	as	ADP
fcis-29234	66	23	the	the	DET
fcis-29234	66	24	1x1	1x1	NUM
fcis-29234	66	25	branch	branch	NOUN
fcis-29234	66	26	in	in	ADP
fcis-29234	66	27	ema	ema	PROPN
fcis-29234	66	28	.	.	PUNCT
fcis-29234	67	1	to	to	PART
fcis-29234	67	2	aggregate	aggregate	VERB
fcis-29234	67	3	multi	multi	ADJ
fcis-29234	67	4	-	-	ADJ
fcis-29234	67	5	scale	scale	ADJ
fcis-29234	67	6	spatial	spatial	ADJ
fcis-29234	67	7	structure	structure	NOUN
fcis-29234	67	8	information	information	NOUN
fcis-29234	67	9	,	,	PUNCT
fcis-29234	67	10	a	a	DET
fcis-29234	67	11	3x3	3x3	NUM
fcis-29234	67	12	convolution	convolution	NOUN
fcis-29234	67	13	kernel	kernel	NOUN
fcis-29234	67	14	is	be	AUX
fcis-29234	67	15	introduced	introduce	VERB
fcis-29234	67	16	in	in	ADP
fcis-29234	67	17	the	the	DET
fcis-29234	67	18	parallel	parallel	ADJ
fcis-29234	67	19	path	path	NOUN
fcis-29234	67	20	of	of	ADP
fcis-29234	67	21	the	the	DET
fcis-29234	67	22	1x1	1x1	NUM
fcis-29234	67	23	branch	branch	NOUN
fcis-29234	67	24	for	for	ADP
fcis-29234	67	25	fast	fast	ADJ
fcis-29234	67	26	responses	response	NOUN
fcis-29234	67	27	,	,	PUNCT
fcis-29234	67	28	referred	refer	VERB
fcis-29234	67	29	to	to	ADP
fcis-29234	67	30	as	as	ADP
fcis-29234	67	31	the	the	DET
fcis-29234	67	32	3x3	3x3	NUM
fcis-29234	67	33	branch	branch	NOUN
fcis-29234	67	34	.	.	PUNCT
fcis-29234	68	1	considering	consider	VERB
fcis-29234	68	2	feature	feature	NOUN
fcis-29234	68	3	grouping	grouping	NOUN
fcis-29234	68	4	and	and	CCONJ
fcis-29234	68	5	multi	multi	ADJ
fcis-29234	68	6	-	-	ADJ
fcis-29234	68	7	scale	scale	ADJ
fcis-29234	68	8	structure	structure	NOUN
fcis-29234	68	9	,	,	PUNCT
fcis-29234	68	10	this	this	DET
fcis-29234	68	11	design	design	NOUN
fcis-29234	68	12	helps	helps	AUX
fcis-29234	68	13	efficiently	efficiently	ADV
fcis-29234	68	14	establish	establish	VERB
fcis-29234	68	15	both	both	DET
fcis-29234	68	16	short	short	ADJ
fcis-29234	68	17	-	-	PUNCT
fcis-29234	68	18	range	range	NOUN
fcis-29234	68	19	and	and	CCONJ
fcis-29234	68	20	long	long	ADJ
fcis-29234	68	21	-	-	PUNCT
fcis-29234	68	22	range	range	NOUN
fcis-29234	68	23	dependencies	dependency	NOUN
fcis-29234	68	24	,	,	PUNCT
fcis-29234	68	25	thereby	thereby	ADV
fcis-29234	68	26	improving	improve	VERB
fcis-29234	68	27	performance	performance	NOUN
fcis-29234	68	28	.	.	PUNCT
fcis-29234	69	1	for	for	ADP
fcis-29234	69	2	a	a	DET
fcis-29234	69	3	given	give	VERB
fcis-29234	69	4	input	input	NOUN
fcis-29234	69	5	feature	feature	NOUN
fcis-29234	69	6	map	map	NOUN
fcis-29234	69	7	x∈rc×h×w	x∈rc×h×w	PROPN
fcis-29234	69	8	,	,	PUNCT
fcis-29234	69	9	ema	ema	PROPN
fcis-29234	69	10	divides	divide	VERB
fcis-29234	69	11	x	x	PUNCT
fcis-29234	69	12	along	along	ADP
fcis-29234	69	13	the	the	DET
fcis-29234	69	14	channel	channel	NOUN
fcis-29234	69	15	dimension	dimension	NOUN
fcis-29234	69	16	into	into	ADP
fcis-29234	69	17	g	g	PROPN
fcis-29234	69	18	sub	sub	NOUN
fcis-29234	69	19	-	-	NOUN
fcis-29234	69	20	features	feature	NOUN
fcis-29234	69	21	to	to	PART
fcis-29234	69	22	learn	learn	VERB
fcis-29234	69	23	different	different	ADJ
fcis-29234	69	24	semantics	semantic	NOUN
fcis-29234	69	25	.	.	PUNCT
fcis-29234	70	1	this	this	DET
fcis-29234	70	2	grouping	grouping	NOUN
fcis-29234	70	3	can	can	AUX
fcis-29234	70	4	be	be	AUX
fcis-29234	70	5	expressed	express	VERB
fcis-29234	70	6	as	as	ADP
fcis-29234	70	7	x=[x0,x1,	x=[x0,x1,	NOUN
fcis-29234	70	8	…	…	PUNCT
fcis-29234	70	9	,xg−1	,xg−1	PUNCT
fcis-29234	70	10	]	]	X
fcis-29234	70	11	,	,	PUNCT
fcis-29234	70	12	where	where	SCONJ
fcis-29234	70	13	xi∈rc	xi∈rc	X
fcis-29234	70	14	/	/	SYM
fcis-29234	70	15	g×h×w	g×h×w	PROPN
fcis-29234	70	16	.	.	PUNCT
fcis-29234	71	1	without	without	ADP
fcis-29234	71	2	loss	loss	NOUN
fcis-29234	71	3	56	56	NUM
fcis-29234	71	4	of	of	ADP
fcis-29234	71	5	generality	generality	NOUN
fcis-29234	71	6	,	,	PUNCT
fcis-29234	71	7	we	we	PRON
fcis-29234	71	8	assume	assume	VERB
fcis-29234	71	9	that	that	SCONJ
fcis-29234	71	10	g≪c	g≪c	NOUN
fcis-29234	71	11	,	,	PUNCT
fcis-29234	71	12	and	and	CCONJ
fcis-29234	71	13	the	the	DET
fcis-29234	71	14	learned	learn	VERB
fcis-29234	71	15	attention	attention	NOUN
fcis-29234	71	16	weight	weight	NOUN
fcis-29234	71	17	descriptors	descriptor	NOUN
fcis-29234	71	18	are	be	AUX
fcis-29234	71	19	used	use	VERB
fcis-29234	71	20	to	to	PART
fcis-29234	71	21	enhance	enhance	VERB
fcis-29234	71	22	the	the	DET
fcis-29234	71	23	feature	feature	NOUN
fcis-29234	71	24	representation	representation	NOUN
fcis-29234	71	25	of	of	ADP
fcis-29234	71	26	the	the	DET
fcis-29234	71	27	regions	region	NOUN
fcis-29234	71	28	of	of	ADP
fcis-29234	71	29	interest	interest	NOUN
fcis-29234	71	30	in	in	ADP
fcis-29234	71	31	each	each	DET
fcis-29234	71	32	sub	sub	NOUN
fcis-29234	71	33	-	-	NOUN
fcis-29234	71	34	feature	feature	NOUN
fcis-29234	71	35	.	.	PUNCT
fcis-29234	72	1	the	the	DET
fcis-29234	72	2	larger	large	ADJ
fcis-29234	72	3	local	local	ADJ
fcis-29234	72	4	receptive	receptive	ADJ
fcis-29234	72	5	field	field	NOUN
fcis-29234	72	6	of	of	ADP
fcis-29234	72	7	neurons	neuron	NOUN
fcis-29234	72	8	allows	allow	VERB
fcis-29234	72	9	them	they	PRON
fcis-29234	72	10	to	to	PART
fcis-29234	72	11	collect	collect	VERB
fcis-29234	72	12	multi	multi	ADJ
fcis-29234	72	13	-	-	ADJ
fcis-29234	72	14	scale	scale	ADJ
fcis-29234	72	15	spatial	spatial	ADJ
fcis-29234	72	16	information	information	NOUN
fcis-29234	72	17	.	.	PUNCT
fcis-29234	73	1	based	base	VERB
fcis-29234	73	2	on	on	ADP
fcis-29234	73	3	this	this	PRON
fcis-29234	73	4	,	,	PUNCT
fcis-29234	73	5	ema	ema	PROPN
fcis-29234	73	6	designs	design	VERB
fcis-29234	73	7	three	three	NUM
fcis-29234	73	8	parallel	parallel	ADJ
fcis-29234	73	9	paths	path	NOUN
fcis-29234	73	10	to	to	PART
fcis-29234	73	11	extract	extract	VERB
fcis-29234	73	12	the	the	DET
fcis-29234	73	13	attention	attention	NOUN
fcis-29234	73	14	weight	weight	NOUN
fcis-29234	73	15	descriptors	descriptor	NOUN
fcis-29234	73	16	for	for	ADP
fcis-29234	73	17	the	the	DET
fcis-29234	73	18	grouped	group	VERB
fcis-29234	73	19	feature	feature	NOUN
fcis-29234	73	20	maps	map	NOUN
fcis-29234	73	21	.	.	PUNCT
fcis-29234	74	1	two	two	NUM
fcis-29234	74	2	of	of	ADP
fcis-29234	74	3	the	the	DET
fcis-29234	74	4	parallel	parallel	ADJ
fcis-29234	74	5	paths	path	NOUN
fcis-29234	74	6	belong	belong	VERB
fcis-29234	74	7	to	to	ADP
fcis-29234	74	8	the	the	DET
fcis-29234	74	9	1x1	1x1	NUM
fcis-29234	74	10	branch	branch	NOUN
fcis-29234	74	11	,	,	PUNCT
fcis-29234	74	12	and	and	CCONJ
fcis-29234	74	13	the	the	DET
fcis-29234	74	14	third	third	ADJ
fcis-29234	74	15	path	path	NOUN
fcis-29234	74	16	belongs	belong	VERB
fcis-29234	74	17	to	to	ADP
fcis-29234	74	18	the	the	DET
fcis-29234	74	19	3x3	3x3	NUM
fcis-29234	74	20	branch	branch	NOUN
fcis-29234	74	21	.	.	PUNCT
fcis-29234	75	1	to	to	PART
fcis-29234	75	2	capture	capture	VERB
fcis-29234	75	3	dependencies	dependency	NOUN
fcis-29234	75	4	across	across	ADP
fcis-29234	75	5	all	all	DET
fcis-29234	75	6	channels	channel	NOUN
fcis-29234	75	7	and	and	CCONJ
fcis-29234	75	8	reduce	reduce	VERB
fcis-29234	75	9	computational	computational	ADJ
fcis-29234	75	10	overhead	overhead	NOUN
fcis-29234	75	11	,	,	PUNCT
fcis-29234	75	12	ema	ema	PROPN
fcis-29234	75	13	models	model	VERB
fcis-29234	75	14	the	the	DET
fcis-29234	75	15	crosschannel	crosschannel	NOUN
fcis-29234	75	16	information	information	NOUN
fcis-29234	75	17	interaction	interaction	NOUN
fcis-29234	75	18	along	along	ADP
fcis-29234	75	19	the	the	DET
fcis-29234	75	20	channel	channel	NOUN
fcis-29234	75	21	direction	direction	NOUN
fcis-29234	75	22	.	.	PUNCT
fcis-29234	76	1	specifically	specifically	ADV
fcis-29234	76	2	,	,	PUNCT
fcis-29234	76	3	in	in	ADP
fcis-29234	76	4	the	the	DET
fcis-29234	76	5	1x1	1x1	NUM
fcis-29234	76	6	branch	branch	NOUN
fcis-29234	76	7	,	,	PUNCT
fcis-29234	76	8	two	two	NUM
fcis-29234	76	9	1d	1d	NUM
fcis-29234	76	10	global	global	ADJ
fcis-29234	76	11	average	average	ADJ
fcis-29234	76	12	pooling	pool	VERB
fcis-29234	76	13	operations	operation	NOUN
fcis-29234	76	14	are	be	AUX
fcis-29234	76	15	used	use	VERB
fcis-29234	76	16	to	to	PART
fcis-29234	76	17	encode	encode	VERB
fcis-29234	76	18	the	the	DET
fcis-29234	76	19	channels	channel	NOUN
fcis-29234	76	20	along	along	ADP
fcis-29234	76	21	two	two	NUM
fcis-29234	76	22	spatial	spatial	ADJ
fcis-29234	76	23	directions	direction	NOUN
fcis-29234	76	24	.	.	PUNCT
fcis-29234	77	1	in	in	ADP
fcis-29234	77	2	the	the	DET
fcis-29234	77	3	3x3	3x3	NUM
fcis-29234	77	4	branch	branch	NOUN
fcis-29234	77	5	,	,	PUNCT
fcis-29234	77	6	only	only	ADV
fcis-29234	77	7	one	one	NUM
fcis-29234	77	8	3x3	3x3	NUM
fcis-29234	77	9	convolution	convolution	NOUN
fcis-29234	77	10	kernel	kernel	NOUN
fcis-29234	77	11	is	be	AUX
fcis-29234	77	12	stacked	stack	VERB
fcis-29234	77	13	to	to	PART
fcis-29234	77	14	capture	capture	VERB
fcis-29234	77	15	multi	multi	ADJ
fcis-29234	77	16	-	-	ADJ
fcis-29234	77	17	scale	scale	ADJ
fcis-29234	77	18	feature	feature	NOUN
fcis-29234	77	19	representations	representation	NOUN
fcis-29234	77	20	.	.	PUNCT
fcis-29234	78	1	since	since	SCONJ
fcis-29234	78	2	the	the	DET
fcis-29234	78	3	convolution	convolution	NOUN
fcis-29234	78	4	function	function	NOUN
fcis-29234	78	5	does	do	AUX
fcis-29234	78	6	not	not	PART
fcis-29234	78	7	involve	involve	VERB
fcis-29234	78	8	batch	batch	NOUN
fcis-29234	78	9	coefficients	coefficient	NOUN
fcis-29234	78	10	in	in	ADP
fcis-29234	78	11	its	its	PRON
fcis-29234	78	12	dimensions	dimension	NOUN
fcis-29234	78	13	,	,	PUNCT
fcis-29234	78	14	the	the	DET
fcis-29234	78	15	number	number	NOUN
fcis-29234	78	16	of	of	ADP
fcis-29234	78	17	convolution	convolution	NOUN
fcis-29234	78	18	kernels	kernel	NOUN
fcis-29234	78	19	is	be	AUX
fcis-29234	78	20	independent	independent	ADJ
fcis-29234	78	21	of	of	ADP
fcis-29234	78	22	the	the	DET
fcis-29234	78	23	batch	batch	NOUN
fcis-29234	78	24	coefficients	coefficient	NOUN
fcis-29234	78	25	in	in	ADP
fcis-29234	78	26	the	the	DET
fcis-29234	78	27	forward	forward	ADJ
fcis-29234	78	28	pass	pass	NOUN
fcis-29234	78	29	.	.	PUNCT
fcis-29234	79	1	therefore	therefore	ADV
fcis-29234	79	2	,	,	PUNCT
fcis-29234	79	3	ema	ema	PROPN
fcis-29234	79	4	reshapes	reshape	VERB
fcis-29234	79	5	and	and	CCONJ
fcis-29234	79	6	arranges	arrange	VERB
fcis-29234	79	7	the	the	DET
fcis-29234	79	8	gg	gg	NOUN
fcis-29234	79	9	groups	group	NOUN
fcis-29234	79	10	into	into	ADP
fcis-29234	79	11	the	the	DET
fcis-29234	79	12	batch	batch	NOUN
fcis-29234	79	13	dimension	dimension	NOUN
fcis-29234	79	14	,	,	PUNCT
fcis-29234	79	15	redefining	redefine	VERB
fcis-29234	79	16	the	the	DET
fcis-29234	79	17	input	input	NOUN
fcis-29234	79	18	tensor	tensor	NOUN
fcis-29234	79	19	as	as	ADP
fcis-29234	79	20	shape	shape	NOUN
fcis-29234	79	21	c//g×h×w	c//g×h×w	PROPN
fcis-29234	79	22	.	.	PUNCT
fcis-29234	80	1	on	on	ADP
fcis-29234	80	2	one	one	NUM
fcis-29234	80	3	hand	hand	NOUN
fcis-29234	80	4	,	,	PUNCT
fcis-29234	80	5	similar	similar	ADJ
fcis-29234	80	6	to	to	ADP
fcis-29234	80	7	ca	ca	PROPN
fcis-29234	80	8	,	,	PUNCT
fcis-29234	80	9	ema	ema	PROPN
fcis-29234	80	10	concatenates	concatenate	VERB
fcis-29234	80	11	the	the	DET
fcis-29234	80	12	two	two	NUM
fcis-29234	80	13	encoded	encode	VERB
fcis-29234	80	14	features	feature	NOUN
fcis-29234	80	15	along	along	ADP
fcis-29234	80	16	the	the	DET
fcis-29234	80	17	height	height	NOUN
fcis-29234	80	18	of	of	ADP
fcis-29234	80	19	the	the	DET
fcis-29234	80	20	image	image	NOUN
fcis-29234	80	21	and	and	CCONJ
fcis-29234	80	22	shares	share	NOUN
fcis-29234	80	23	the	the	DET
fcis-29234	80	24	same	same	ADJ
fcis-29234	80	25	1x1	1x1	NUM
fcis-29234	80	26	convolution	convolution	NOUN
fcis-29234	80	27	operation	operation	NOUN
fcis-29234	80	28	without	without	ADP
fcis-29234	80	29	performing	perform	VERB
fcis-29234	80	30	dimensionality	dimensionality	NOUN
fcis-29234	80	31	compression	compression	NOUN
fcis-29234	80	32	in	in	ADP
fcis-29234	80	33	the	the	DET
fcis-29234	80	34	1x1	1x1	NUM
fcis-29234	80	35	branch	branch	NOUN
fcis-29234	80	36	.	.	PUNCT
fcis-29234	81	1	after	after	ADP
fcis-29234	81	2	decomposing	decompose	VERB
fcis-29234	81	3	the	the	DET
fcis-29234	81	4	output	output	NOUN
fcis-29234	81	5	of	of	ADP
fcis-29234	81	6	the	the	DET
fcis-29234	81	7	1x1	1x1	NUM
fcis-29234	81	8	convolution	convolution	NOUN
fcis-29234	81	9	into	into	ADP
fcis-29234	81	10	two	two	NUM
fcis-29234	81	11	vectors	vector	NOUN
fcis-29234	81	12	,	,	PUNCT
fcis-29234	81	13	two	two	NUM
fcis-29234	81	14	non	non	ADJ
fcis-29234	81	15	-	-	ADJ
fcis-29234	81	16	linear	linear	ADJ
fcis-29234	81	17	sigmoid	sigmoid	NOUN
fcis-29234	81	18	functions	function	NOUN
fcis-29234	81	19	are	be	AUX
fcis-29234	81	20	applied	apply	VERB
fcis-29234	81	21	to	to	PART
fcis-29234	81	22	fit	fit	VERB
fcis-29234	81	23	a	a	DET
fcis-29234	81	24	two	two	NUM
fcis-29234	81	25	-	-	PUNCT
fcis-29234	81	26	dimensional	dimensional	ADJ
fcis-29234	81	27	binomial	binomial	ADJ
fcis-29234	81	28	distribution	distribution	NOUN
fcis-29234	81	29	based	base	VERB
fcis-29234	81	30	on	on	ADP
fcis-29234	81	31	linear	linear	ADJ
fcis-29234	81	32	convolution	convolution	NOUN
fcis-29234	81	33	.	.	PUNCT
fcis-29234	82	1	to	to	PART
fcis-29234	82	2	achieve	achieve	VERB
fcis-29234	82	3	different	different	ADJ
fcis-29234	82	4	cross	cross	ADJ
fcis-29234	82	5	-	-	ADJ
fcis-29234	82	6	channel	channel	ADJ
fcis-29234	82	7	interaction	interaction	NOUN
fcis-29234	82	8	features	feature	NOUN
fcis-29234	82	9	between	between	ADP
fcis-29234	82	10	the	the	DET
fcis-29234	82	11	two	two	NUM
fcis-29234	82	12	parallel	parallel	ADJ
fcis-29234	82	13	paths	path	NOUN
fcis-29234	82	14	of	of	ADP
fcis-29234	82	15	the	the	DET
fcis-29234	82	16	1x1	1x1	NUM
fcis-29234	82	17	branch	branch	NOUN
fcis-29234	82	18	,	,	PUNCT
fcis-29234	82	19	ema	ema	PROPN
fcis-29234	82	20	aggregates	aggregate	VERB
fcis-29234	82	21	the	the	DET
fcis-29234	82	22	two	two	NUM
fcis-29234	82	23	channel	channel	NOUN
fcis-29234	82	24	attention	attention	NOUN
fcis-29234	82	25	maps	map	NOUN
fcis-29234	82	26	within	within	ADP
fcis-29234	82	27	each	each	DET
fcis-29234	82	28	group	group	NOUN
fcis-29234	82	29	by	by	ADP
fcis-29234	82	30	simple	simple	ADJ
fcis-29234	82	31	multiplication	multiplication	NOUN
fcis-29234	82	32	.	.	PUNCT
fcis-29234	83	1	on	on	ADP
fcis-29234	83	2	the	the	DET
fcis-29234	83	3	other	other	ADJ
fcis-29234	83	4	hand	hand	NOUN
fcis-29234	83	5	,	,	PUNCT
fcis-29234	83	6	the	the	DET
fcis-29234	83	7	3x3	3x3	NUM
fcis-29234	83	8	branch	branch	NOUN
fcis-29234	83	9	captures	capture	VERB
fcis-29234	83	10	local	local	ADJ
fcis-29234	83	11	crosschannel	crosschannel	NOUN
fcis-29234	83	12	interactions	interaction	NOUN
fcis-29234	83	13	via	via	ADP
fcis-29234	83	14	a	a	DET
fcis-29234	83	15	3x3	3x3	NUM
fcis-29234	83	16	convolution	convolution	NOUN
fcis-29234	83	17	to	to	PART
fcis-29234	83	18	expand	expand	VERB
fcis-29234	83	19	the	the	DET
fcis-29234	83	20	feature	feature	NOUN
fcis-29234	83	21	space	space	NOUN
fcis-29234	83	22	.	.	PUNCT
fcis-29234	84	1	in	in	ADP
fcis-29234	84	2	this	this	DET
fcis-29234	84	3	way	way	NOUN
fcis-29234	84	4	,	,	PUNCT
fcis-29234	84	5	ema	ema	PROPN
fcis-29234	84	6	not	not	PART
fcis-29234	84	7	only	only	ADV
fcis-29234	84	8	encodes	encodes	DET
fcis-29234	84	9	crosschannel	crosschannel	ADJ
fcis-29234	84	10	information	information	NOUN
fcis-29234	84	11	to	to	PART
fcis-29234	84	12	adjust	adjust	VERB
fcis-29234	84	13	the	the	DET
fcis-29234	84	14	importance	importance	NOUN
fcis-29234	84	15	of	of	ADP
fcis-29234	84	16	different	different	ADJ
fcis-29234	84	17	channels	channel	NOUN
fcis-29234	84	18	but	but	CCONJ
fcis-29234	84	19	also	also	ADV
fcis-29234	84	20	preserves	preserve	VERB
fcis-29234	84	21	precise	precise	ADJ
fcis-29234	84	22	spatial	spatial	ADJ
fcis-29234	84	23	structure	structure	NOUN
fcis-29234	84	24	information	information	NOUN
fcis-29234	84	25	within	within	ADP
fcis-29234	84	26	the	the	DET
fcis-29234	84	27	channels	channel	NOUN
fcis-29234	84	28	.	.	PUNCT
fcis-29234	85	1	advantages	advantage	NOUN
fcis-29234	85	2	of	of	ADP
fcis-29234	85	3	ema	ema	PROPN
fcis-29234	85	4	attention	attention	NOUN
fcis-29234	85	5	mechanism	mechanism	NOUN
fcis-29234	85	6	for	for	ADP
fcis-29234	85	7	small	small	ADJ
fcis-29234	85	8	object	object	NOUN
fcis-29234	85	9	detection	detection	NOUN
fcis-29234	85	10	:	:	PUNCT
fcis-29234	85	11	1	1	X
fcis-29234	85	12	.	.	NOUN
fcis-29234	85	13	enhances	enhance	VERB
fcis-29234	85	14	sensitivity	sensitivity	NOUN
fcis-29234	85	15	to	to	ADP
fcis-29234	85	16	small	small	ADJ
fcis-29234	85	17	objects	object	NOUN
fcis-29234	85	18	,	,	PUNCT
fcis-29234	85	19	improving	improve	VERB
fcis-29234	85	20	the	the	DET
fcis-29234	85	21	detection	detection	NOUN
fcis-29234	85	22	capability	capability	NOUN
fcis-29234	85	23	for	for	ADP
fcis-29234	85	24	tiny	tiny	ADJ
fcis-29234	85	25	targets	target	NOUN
fcis-29234	85	26	.	.	PUNCT
fcis-29234	86	1	2	2	X
fcis-29234	86	2	.	.	X
fcis-29234	86	3	smoothens	smoothen	VERB
fcis-29234	86	4	the	the	DET
fcis-29234	86	5	feature	feature	NOUN
fcis-29234	86	6	information	information	NOUN
fcis-29234	86	7	of	of	ADP
fcis-29234	86	8	small	small	ADJ
fcis-29234	86	9	objects	object	NOUN
fcis-29234	86	10	,	,	PUNCT
fcis-29234	86	11	reducing	reduce	VERB
fcis-29234	86	12	noise	noise	NOUN
fcis-29234	86	13	interference	interference	NOUN
fcis-29234	86	14	.	.	PUNCT
fcis-29234	87	1	2.2.2	2.2.2	X
fcis-29234	87	2	.	.	PUNCT
fcis-29234	87	3	yolov8	yolov8	NOUN
fcis-29234	87	4	model	model	PROPN
fcis-29234	87	5	with	with	ADP
fcis-29234	87	6	integrated	integrate	VERB
fcis-29234	87	7	rfb	rfb	PROPN
fcis-29234	87	8	structure	structure	NOUN
fcis-29234	87	9	rfb	rfb	PROPN
fcis-29234	87	10	(	(	PUNCT
fcis-29234	87	11	receptive	receptive	ADJ
fcis-29234	87	12	field	field	NOUN
fcis-29234	87	13	block	block	NOUN
fcis-29234	87	14	)	)	PUNCT
fcis-29234	87	15	is	be	AUX
fcis-29234	87	16	a	a	DET
fcis-29234	87	17	network	network	NOUN
fcis-29234	87	18	module	module	NOUN
fcis-29234	87	19	designed	design	VERB
fcis-29234	87	20	to	to	PART
fcis-29234	87	21	enhance	enhance	VERB
fcis-29234	87	22	feature	feature	NOUN
fcis-29234	87	23	extraction	extraction	NOUN
fcis-29234	87	24	capabilities	capability	NOUN
fcis-29234	87	25	by	by	ADP
fcis-29234	87	26	increasing	increase	VERB
fcis-29234	87	27	the	the	DET
fcis-29234	87	28	receptive	receptive	ADJ
fcis-29234	87	29	field	field	NOUN
fcis-29234	87	30	,	,	PUNCT
fcis-29234	87	31	which	which	PRON
fcis-29234	87	32	improves	improve	VERB
fcis-29234	87	33	the	the	DET
fcis-29234	87	34	performance	performance	NOUN
fcis-29234	87	35	of	of	ADP
fcis-29234	87	36	convolutional	convolutional	ADJ
fcis-29234	87	37	neural	neural	ADJ
fcis-29234	87	38	networks	network	NOUN
fcis-29234	87	39	(	(	PUNCT
fcis-29234	87	40	cnns	cnns	PROPN
fcis-29234	87	41	)	)	PUNCT
fcis-29234	87	42	in	in	ADP
fcis-29234	87	43	object	object	NOUN
fcis-29234	87	44	detection	detection	NOUN
fcis-29234	87	45	tasks	task	NOUN
fcis-29234	87	46	,	,	PUNCT
fcis-29234	87	47	further	far	ADV
fcis-29234	87	48	enhancing	enhance	VERB
fcis-29234	87	49	detection	detection	NOUN
fcis-29234	87	50	accuracy	accuracy	NOUN
fcis-29234	87	51	and	and	CCONJ
fcis-29234	87	52	speed	speed	NOUN
fcis-29234	87	53	.	.	PUNCT
fcis-29234	88	1	rfb	rfb	PROPN
fcis-29234	88	2	is	be	AUX
fcis-29234	88	3	a	a	DET
fcis-29234	88	4	novel	novel	ADJ
fcis-29234	88	5	feature	feature	NOUN
fcis-29234	88	6	extraction	extraction	NOUN
fcis-29234	88	7	module	module	NOUN
fcis-29234	88	8	that	that	PRON
fcis-29234	88	9	simulates	simulate	VERB
fcis-29234	88	10	the	the	DET
fcis-29234	88	11	receptive	receptive	ADJ
fcis-29234	88	12	field	field	NOUN
fcis-29234	88	13	of	of	ADP
fcis-29234	88	14	human	human	ADJ
fcis-29234	88	15	vision	vision	NOUN
fcis-29234	88	16	to	to	PART
fcis-29234	88	17	strengthen	strengthen	VERB
fcis-29234	88	18	the	the	DET
fcis-29234	88	19	network	network	NOUN
fcis-29234	88	20	's	's	PART
fcis-29234	88	21	feature	feature	NOUN
fcis-29234	88	22	extraction	extraction	NOUN
fcis-29234	88	23	capability	capability	NOUN
fcis-29234	88	24	.	.	PUNCT
fcis-29234	89	1	structurally	structurally	ADV
fcis-29234	89	2	,	,	PUNCT
fcis-29234	89	3	rfb	rfb	PROPN
fcis-29234	89	4	is	be	AUX
fcis-29234	89	5	inspired	inspire	VERB
fcis-29234	89	6	by	by	ADP
fcis-29234	89	7	the	the	DET
fcis-29234	89	8	multi	multi	ADJ
fcis-29234	89	9	-	-	ADJ
fcis-29234	89	10	branch	branch	ADJ
fcis-29234	89	11	network	network	NOUN
fcis-29234	89	12	architecture	architecture	NOUN
fcis-29234	89	13	of	of	ADP
fcis-29234	89	14	inception	inception	NOUN
fcis-29234	89	15	.	.	PUNCT
fcis-29234	90	1	it	it	PRON
fcis-29234	90	2	builds	build	VERB
fcis-29234	90	3	on	on	ADP
fcis-29234	90	4	inception	inception	NOUN
fcis-29234	90	5	by	by	ADP
fcis-29234	90	6	incorporating	incorporate	VERB
fcis-29234	90	7	dilated	dilated	ADJ
fcis-29234	90	8	convolutions	convolution	NOUN
fcis-29234	90	9	,	,	PUNCT
fcis-29234	90	10	where	where	SCONJ
fcis-29234	90	11	the	the	DET
fcis-29234	90	12	dilation	dilation	NOUN
fcis-29234	90	13	rate	rate	NOUN
fcis-29234	90	14	increases	increase	NOUN
fcis-29234	90	15	with	with	ADP
fcis-29234	90	16	the	the	DET
fcis-29234	90	17	size	size	NOUN
fcis-29234	90	18	of	of	ADP
fcis-29234	90	19	the	the	DET
fcis-29234	90	20	convolution	convolution	NOUN
fcis-29234	90	21	kernel	kernel	NOUN
fcis-29234	90	22	,	,	PUNCT
fcis-29234	90	23	effectively	effectively	ADV
fcis-29234	90	24	expanding	expand	VERB
fcis-29234	90	25	the	the	DET
fcis-29234	90	26	receptive	receptive	ADJ
fcis-29234	90	27	field	field	NOUN
fcis-29234	90	28	.	.	PUNCT
fcis-29234	91	1	the	the	DET
fcis-29234	91	2	rfb	rfb	PROPN
fcis-29234	91	3	network	network	NOUN
fcis-29234	91	4	structure	structure	NOUN
fcis-29234	91	5	,	,	PUNCT
fcis-29234	91	6	as	as	SCONJ
fcis-29234	91	7	shown	show	VERB
fcis-29234	91	8	in	in	ADP
fcis-29234	91	9	fig.4	fig.4	PROPN
fcis-29234	91	10	,	,	PUNCT
fcis-29234	91	11	uses	use	VERB
fcis-29234	91	12	1×1	1×1	NUM
fcis-29234	91	13	,	,	PUNCT
fcis-29234	91	14	3×3	3×3	NUM
fcis-29234	91	15	,	,	PUNCT
fcis-29234	91	16	and	and	CCONJ
fcis-29234	91	17	5×5	5×5	NUM
fcis-29234	91	18	convolution	convolution	NOUN
fcis-29234	91	19	kernels	kernel	NOUN
fcis-29234	91	20	for	for	ADP
fcis-29234	91	21	feature	feature	NOUN
fcis-29234	91	22	extraction	extraction	NOUN
fcis-29234	91	23	across	across	ADP
fcis-29234	91	24	three	three	NUM
fcis-29234	91	25	channels	channel	NOUN
fcis-29234	91	26	.	.	PUNCT
fcis-29234	92	1	it	it	PRON
fcis-29234	92	2	then	then	ADV
fcis-29234	92	3	applies	apply	VERB
fcis-29234	92	4	dilation	dilation	NOUN
fcis-29234	92	5	rates	rate	NOUN
fcis-29234	92	6	of	of	ADP
fcis-29234	92	7	1	1	NUM
fcis-29234	92	8	,	,	PUNCT
fcis-29234	92	9	3	3	NUM
fcis-29234	92	10	,	,	PUNCT
fcis-29234	92	11	and	and	CCONJ
fcis-29234	92	12	5	5	NUM
fcis-29234	92	13	to	to	ADP
fcis-29234	92	14	the	the	DET
fcis-29234	92	15	corresponding	correspond	VERB
fcis-29234	92	16	3×3	3×3	NUM
fcis-29234	92	17	convolutions	convolution	NOUN
fcis-29234	92	18	,	,	PUNCT
fcis-29234	92	19	respectively	respectively	ADV
fcis-29234	92	20	.	.	PUNCT
fcis-29234	93	1	the	the	DET
fcis-29234	93	2	effective	effective	ADJ
fcis-29234	93	3	feature	feature	NOUN
fcis-29234	93	4	layers	layer	NOUN
fcis-29234	93	5	at	at	ADP
fcis-29234	93	6	different	different	ADJ
fcis-29234	93	7	scales	scale	NOUN
fcis-29234	93	8	from	from	ADP
fcis-29234	93	9	the	the	DET
fcis-29234	93	10	three	three	NUM
fcis-29234	93	11	branches	branch	NOUN
fcis-29234	93	12	are	be	AUX
fcis-29234	93	13	concatenated	concatenate	VERB
fcis-29234	93	14	.	.	PUNCT
fcis-29234	94	1	finally	finally	ADV
fcis-29234	94	2	,	,	PUNCT
fcis-29234	94	3	a	a	DET
fcis-29234	94	4	1×1	1×1	NUM
fcis-29234	94	5	convolution	convolution	NOUN
fcis-29234	94	6	layer	layer	NOUN
fcis-29234	94	7	is	be	AUX
fcis-29234	94	8	applied	apply	VERB
fcis-29234	94	9	across	across	ADP
fcis-29234	94	10	channels	channel	NOUN
fcis-29234	94	11	to	to	PART
fcis-29234	94	12	perform	perform	VERB
fcis-29234	94	13	residual	residual	ADJ
fcis-29234	94	14	connections	connection	NOUN
fcis-29234	94	15	with	with	ADP
fcis-29234	94	16	the	the	DET
fcis-29234	94	17	input	input	NOUN
fcis-29234	94	18	's	's	PART
fcis-29234	94	19	effective	effective	ADJ
fcis-29234	94	20	feature	feature	NOUN
fcis-29234	94	21	layers	layer	NOUN
fcis-29234	94	22	,	,	PUNCT
fcis-29234	94	23	integrating	integrate	VERB
fcis-29234	94	24	different	different	ADJ
fcis-29234	94	25	-	-	PUNCT
fcis-29234	94	26	sized	sized	ADJ
fcis-29234	94	27	receptive	receptive	ADJ
fcis-29234	94	28	fields	field	NOUN
fcis-29234	94	29	.	.	PUNCT
fcis-29234	95	1	this	this	DET
fcis-29234	95	2	process	process	NOUN
fcis-29234	95	3	expands	expand	VERB
fcis-29234	95	4	feature	feature	NOUN
fcis-29234	95	5	information	information	NOUN
fcis-29234	95	6	extraction	extraction	NOUN
fcis-29234	95	7	,	,	PUNCT
fcis-29234	95	8	which	which	PRON
fcis-29234	95	9	further	far	ADV
fcis-29234	95	10	enhances	enhance	VERB
fcis-29234	95	11	detection	detection	NOUN
fcis-29234	95	12	accuracy	accuracy	NOUN
fcis-29234	95	13	and	and	CCONJ
fcis-29234	95	14	speed	speed	NOUN
fcis-29234	95	15	.	.	PUNCT
fcis-29234	96	1	advantages	advantage	NOUN
fcis-29234	96	2	of	of	ADP
fcis-29234	96	3	the	the	DET
fcis-29234	96	4	rfb	rfb	PROPN
fcis-29234	96	5	network	network	NOUN
fcis-29234	96	6	for	for	ADP
fcis-29234	96	7	small	small	ADJ
fcis-29234	96	8	object	object	NOUN
fcis-29234	96	9	detection	detection	NOUN
fcis-29234	96	10	:	:	PUNCT
fcis-29234	96	11	1	1	X
fcis-29234	96	12	.	.	X
fcis-29234	96	13	multi	multi	ADJ
fcis-29234	96	14	-	-	ADJ
fcis-29234	96	15	scale	scale	ADJ
fcis-29234	96	16	feature	feature	NOUN
fcis-29234	96	17	fusion	fusion	NOUN
fcis-29234	96	18	:	:	PUNCT
fcis-29234	96	19	rfb	rfb	PROPN
fcis-29234	96	20	extracts	extract	NOUN
fcis-29234	96	21	features	feature	NOUN
fcis-29234	96	22	using	use	VERB
fcis-29234	96	23	convolution	convolution	NOUN
fcis-29234	96	24	kernels	kernel	NOUN
fcis-29234	96	25	of	of	ADP
fcis-29234	96	26	different	different	ADJ
fcis-29234	96	27	scales	scale	NOUN
fcis-29234	96	28	,	,	PUNCT
fcis-29234	96	29	allowing	allow	VERB
fcis-29234	96	30	for	for	ADP
fcis-29234	96	31	better	well	ADJ
fcis-29234	96	32	identification	identification	NOUN
fcis-29234	96	33	of	of	ADP
fcis-29234	96	34	small	small	ADJ
fcis-29234	96	35	objects	object	NOUN
fcis-29234	96	36	.	.	PUNCT
fcis-29234	97	1	2	2	X
fcis-29234	97	2	.	.	NUM
fcis-29234	97	3	enhanced	enhance	VERB
fcis-29234	97	4	detail	detail	NOUN
fcis-29234	97	5	capturing	capturing	NOUN
fcis-29234	97	6	:	:	PUNCT
fcis-29234	97	7	by	by	ADP
fcis-29234	97	8	expanding	expand	VERB
fcis-29234	97	9	the	the	DET
fcis-29234	97	10	receptive	receptive	ADJ
fcis-29234	97	11	field	field	NOUN
fcis-29234	97	12	,	,	PUNCT
fcis-29234	97	13	rfb	rfb	PROPN
fcis-29234	97	14	captures	capture	VERB
fcis-29234	97	15	fine	fine	ADJ
fcis-29234	97	16	details	detail	NOUN
fcis-29234	97	17	of	of	ADP
fcis-29234	97	18	small	small	ADJ
fcis-29234	97	19	objects	object	NOUN
fcis-29234	97	20	,	,	PUNCT
fcis-29234	97	21	reducing	reduce	VERB
fcis-29234	97	22	the	the	DET
fcis-29234	97	23	likelihood	likelihood	NOUN
fcis-29234	97	24	of	of	ADP
fcis-29234	97	25	missing	miss	VERB
fcis-29234	97	26	detections	detection	NOUN
fcis-29234	97	27	.	.	PUNCT
fcis-29234	98	1	3	3	X
fcis-29234	98	2	.	.	NUM
fcis-29234	98	3	expanded	expand	VERB
fcis-29234	98	4	receptive	receptive	ADJ
fcis-29234	98	5	field	field	NOUN
fcis-29234	98	6	:	:	PUNCT
fcis-29234	98	7	the	the	DET
fcis-29234	98	8	use	use	NOUN
fcis-29234	98	9	of	of	ADP
fcis-29234	98	10	dilated	dilated	ADJ
fcis-29234	98	11	convolution	convolution	NOUN
fcis-29234	98	12	increases	increase	VERB
fcis-29234	98	13	the	the	DET
fcis-29234	98	14	receptive	receptive	ADJ
fcis-29234	98	15	field	field	NOUN
fcis-29234	98	16	,	,	PUNCT
fcis-29234	98	17	enabling	enable	VERB
fcis-29234	98	18	the	the	DET
fcis-29234	98	19	network	network	NOUN
fcis-29234	98	20	to	to	PART
fcis-29234	98	21	better	well	ADV
fcis-29234	98	22	capture	capture	VERB
fcis-29234	98	23	long	long	ADJ
fcis-29234	98	24	-	-	PUNCT
fcis-29234	98	25	distance	distance	NOUN
fcis-29234	98	26	spatial	spatial	ADJ
fcis-29234	98	27	semantic	semantic	ADJ
fcis-29234	98	28	information	information	NOUN
fcis-29234	98	29	.	.	PUNCT
fcis-29234	99	1	fig	fig	NOUN
fcis-29234	99	2	4	4	NUM
fcis-29234	99	3	.	.	PUNCT
fcis-29234	100	1	rfb	rfb	PROPN
fcis-29234	100	2	network	network	PROPN
fcis-29234	100	3	structure	structure	NOUN
fcis-29234	100	4	3	3	NUM
fcis-29234	100	5	.	.	PUNCT
fcis-29234	100	6	improvement	improvement	NOUN
fcis-29234	100	7	and	and	CCONJ
fcis-29234	100	8	optimization	optimization	NOUN
fcis-29234	100	9	of	of	ADP
fcis-29234	100	10	the	the	DET
fcis-29234	100	11	alignedreid	alignedreid	NOUN
fcis-29234	100	12	-	-	PUNCT
fcis-29234	100	13	based	base	VERB
fcis-29234	100	14	object	object	NOUN
fcis-29234	100	15	reidentification	reidentification	NOUN
fcis-29234	100	16	algorithm	algorithm	NOUN
fcis-29234	100	17	to	to	PART
fcis-29234	100	18	improve	improve	VERB
fcis-29234	100	19	the	the	DET
fcis-29234	100	20	accuracy	accuracy	NOUN
fcis-29234	100	21	of	of	ADP
fcis-29234	100	22	multi	multi	ADJ
fcis-29234	100	23	-	-	ADJ
fcis-29234	100	24	target	target	ADJ
fcis-29234	100	25	tracking	tracking	NOUN
fcis-29234	100	26	algorithms	algorithm	NOUN
fcis-29234	100	27	integrating	integrate	VERB
fcis-29234	100	28	target	target	NOUN
fcis-29234	100	29	re	re	NOUN
fcis-29234	100	30	-	-	NOUN
fcis-29234	100	31	identification	identification	NOUN
fcis-29234	100	32	on	on	ADP
fcis-29234	100	33	mobile	mobile	ADJ
fcis-29234	100	34	platforms	platform	NOUN
fcis-29234	100	35	,	,	PUNCT
fcis-29234	100	36	this	this	DET
fcis-29234	100	37	study	study	NOUN
fcis-29234	100	38	addresses	address	VERB
fcis-29234	100	39	the	the	DET
fcis-29234	100	40	challenges	challenge	NOUN
fcis-29234	100	41	of	of	ADP
fcis-29234	100	42	feature	feature	NOUN
fcis-29234	100	43	matching	match	VERB
fcis-29234	100	44	in	in	ADP
fcis-29234	100	45	target	target	NOUN
fcis-29234	100	46	re	re	NOUN
fcis-29234	100	47	-	-	NOUN
fcis-29234	100	48	identification	identification	NOUN
fcis-29234	100	49	by	by	ADP
fcis-29234	100	50	selecting	select	VERB
fcis-29234	100	51	alignedreid	alignedreid	NOUN
fcis-29234	100	52	as	as	ADP
fcis-29234	100	53	the	the	DET
fcis-29234	100	54	target	target	NOUN
fcis-29234	100	55	re	re	NOUN
fcis-29234	100	56	-	-	NOUN
fcis-29234	100	57	identification	identification	NOUN
fcis-29234	100	58	algorithm	algorithm	NOUN
fcis-29234	100	59	.	.	PUNCT
fcis-29234	101	1	optimization	optimization	NOUN
fcis-29234	101	2	of	of	ADP
fcis-29234	101	3	this	this	DET
fcis-29234	101	4	algorithm	algorithm	NOUN
fcis-29234	101	5	is	be	AUX
fcis-29234	101	6	carried	carry	VERB
fcis-29234	101	7	out	out	ADP
fcis-29234	101	8	to	to	PART
fcis-29234	101	9	enhance	enhance	VERB
fcis-29234	101	10	the	the	DET
fcis-29234	101	11	accuracy	accuracy	NOUN
fcis-29234	101	12	of	of	ADP
fcis-29234	101	13	reidentification	reidentification	NOUN
fcis-29234	101	14	for	for	ADP
fcis-29234	101	15	small	small	ADJ
fcis-29234	101	16	targets	target	NOUN
fcis-29234	101	17	at	at	ADP
fcis-29234	101	18	long	long	ADJ
fcis-29234	101	19	distances	distance	NOUN
fcis-29234	101	20	.	.	PUNCT
fcis-29234	102	1	alignedreid	alignedreid	PROPN
fcis-29234	102	2	is	be	AUX
fcis-29234	102	3	a	a	DET
fcis-29234	102	4	deep	deep	ADJ
fcis-29234	102	5	learning	learning	NOUN
fcis-29234	102	6	-	-	PUNCT
fcis-29234	102	7	based	base	VERB
fcis-29234	102	8	target	target	NOUN
fcis-29234	102	9	reidentification	reidentification	NOUN
fcis-29234	102	10	method	method	NOUN
fcis-29234	102	11	designed	design	VERB
fcis-29234	102	12	to	to	PART
fcis-29234	102	13	improve	improve	VERB
fcis-29234	102	14	matching	matching	NOUN
fcis-29234	102	15	accuracy	accuracy	NOUN
fcis-29234	102	16	across	across	ADP
fcis-29234	102	17	different	different	ADJ
fcis-29234	102	18	viewpoints	viewpoint	NOUN
fcis-29234	102	19	.	.	PUNCT
fcis-29234	103	1	the	the	DET
fcis-29234	103	2	core	core	NOUN
fcis-29234	103	3	of	of	ADP
fcis-29234	103	4	this	this	DET
fcis-29234	103	5	method	method	NOUN
fcis-29234	103	6	lies	lie	VERB
fcis-29234	103	7	in	in	ADP
fcis-29234	103	8	aligning	align	VERB
fcis-29234	103	9	feature	feature	NOUN
fcis-29234	103	10	maps	map	NOUN
fcis-29234	103	11	from	from	ADP
fcis-29234	103	12	different	different	ADJ
fcis-29234	103	13	images	image	NOUN
fcis-29234	103	14	to	to	PART
fcis-29234	103	15	capture	capture	VERB
fcis-29234	103	16	critical	critical	ADJ
fcis-29234	103	17	discriminative	discriminative	NOUN
fcis-29234	103	18	details	detail	NOUN
fcis-29234	103	19	,	,	PUNCT
fcis-29234	103	20	thereby	thereby	ADV
fcis-29234	103	21	achieving	achieve	VERB
fcis-29234	103	22	stronger	strong	ADJ
fcis-29234	103	23	reidentification	reidentification	NOUN
fcis-29234	103	24	performance	performance	NOUN
fcis-29234	103	25	.	.	PUNCT
fcis-29234	104	1	unlike	unlike	ADP
fcis-29234	104	2	traditional	traditional	ADJ
fcis-29234	104	3	reidentification	reidentification	NOUN
fcis-29234	104	4	methods	method	NOUN
fcis-29234	104	5	,	,	PUNCT
fcis-29234	104	6	alignedreid	alignedreid	PROPN
fcis-29234	104	7	dynamically	dynamically	ADV
fcis-29234	104	8	aligns	align	VERB
fcis-29234	104	9	local	local	ADJ
fcis-29234	104	10	features	feature	NOUN
fcis-29234	104	11	,	,	PUNCT
fcis-29234	104	12	making	make	VERB
fcis-29234	104	13	the	the	DET
fcis-29234	104	14	identity	identity	NOUN
fcis-29234	104	15	features	feature	NOUN
fcis-29234	104	16	of	of	ADP
fcis-29234	104	17	an	an	DET
fcis-29234	104	18	individual	individual	NOUN
fcis-29234	104	19	more	more	ADV
fcis-29234	104	20	accurate	accurate	ADJ
fcis-29234	104	21	and	and	CCONJ
fcis-29234	104	22	thus	thus	ADV
fcis-29234	104	23	improving	improve	VERB
fcis-29234	104	24	the	the	DET
fcis-29234	104	25	matching	matching	NOUN
fcis-29234	104	26	precision	precision	NOUN
fcis-29234	104	27	for	for	ADP
fcis-29234	104	28	small	small	ADJ
fcis-29234	104	29	targets	target	NOUN
fcis-29234	104	30	at	at	ADP
fcis-29234	104	31	long	long	ADJ
fcis-29234	104	32	distances	distance	NOUN
fcis-29234	104	33	.	.	PUNCT
fcis-29234	105	1	the	the	DET
fcis-29234	105	2	bottleneck	bottleneck	NOUN
fcis-29234	105	3	attention	attention	NOUN
fcis-29234	105	4	mechanism	mechanism	NOUN
fcis-29234	105	5	(	(	PUNCT
fcis-29234	105	6	bam	bam	NOUN
fcis-29234	105	7	)	)	PUNCT
fcis-29234	105	8	is	be	AUX
fcis-29234	105	9	a	a	DET
fcis-29234	105	10	simple	simple	ADJ
fcis-29234	105	11	yet	yet	CCONJ
fcis-29234	105	12	effective	effective	ADJ
fcis-29234	105	13	attention	attention	NOUN
fcis-29234	105	14	-	-	PUNCT
fcis-29234	105	15	based	base	VERB
fcis-29234	105	16	bottleneck	bottleneck	NOUN
fcis-29234	105	17	structure	structure	NOUN
fcis-29234	105	18	that	that	PRON
fcis-29234	105	19	dynamically	dynamically	ADV
fcis-29234	105	20	weights	weight	VERB
fcis-29234	105	21	features	feature	NOUN
fcis-29234	105	22	based	base	VERB
fcis-29234	105	23	on	on	ADP
fcis-29234	105	24	their	their	PRON
fcis-29234	105	25	importance	importance	NOUN
fcis-29234	105	26	,	,	PUNCT
fcis-29234	105	27	enabling	enable	VERB
fcis-29234	105	28	the	the	DET
fcis-29234	105	29	bottleneck	bottleneck	NOUN
fcis-29234	105	30	attention	attention	NOUN
fcis-29234	105	31	block	block	NOUN
fcis-29234	105	32	to	to	PART
fcis-29234	105	33	focus	focus	VERB
fcis-29234	105	34	on	on	ADP
fcis-29234	105	35	significant	significant	ADJ
fcis-29234	105	36	discriminative	discriminative	NOUN
fcis-29234	105	37	features	feature	NOUN
fcis-29234	105	38	while	while	SCONJ
fcis-29234	105	39	ignoring	ignore	VERB
fcis-29234	105	40	redundant	redundant	ADJ
fcis-29234	105	41	or	or	CCONJ
fcis-29234	105	42	irrelevant	irrelevant	ADJ
fcis-29234	105	43	ones	one	NOUN
fcis-29234	105	44	.	.	PUNCT
fcis-29234	106	1	the	the	DET
fcis-29234	106	2	structure	structure	NOUN
fcis-29234	106	3	of	of	ADP
fcis-29234	106	4	bam	bam	NOUN
fcis-29234	106	5	is	be	AUX
fcis-29234	106	6	shown	show	VERB
fcis-29234	106	7	in	in	ADP
fcis-29234	106	8	fig	fig	NOUN
fcis-29234	106	9	.	.	PUNCT
fcis-29234	107	1	5	5	X
fcis-29234	107	2	.	.	X
fcis-29234	107	3	in	in	ADP
fcis-29234	107	4	this	this	DET
fcis-29234	107	5	study	study	NOUN
fcis-29234	107	6	,	,	PUNCT
fcis-29234	107	7	the	the	DET
fcis-29234	107	8	bottleneck	bottleneck	NOUN
fcis-29234	107	9	attention	attention	NOUN
fcis-29234	107	10	mechanism	mechanism	NOUN
fcis-29234	107	11	module	module	NOUN
fcis-29234	107	12	is	be	AUX
fcis-29234	107	13	introduced	introduce	VERB
fcis-29234	107	14	into	into	ADP
fcis-29234	107	15	the	the	DET
fcis-29234	107	16	alignedreid	alignedreid	PROPN
fcis-29234	107	17	re	re	NOUN
fcis-29234	107	18	-	-	NOUN
fcis-29234	107	19	identification	identification	NOUN
fcis-29234	107	20	network	network	NOUN
fcis-29234	107	21	,	,	PUNCT
fcis-29234	107	22	enhancing	enhance	VERB
fcis-29234	107	23	the	the	DET
fcis-29234	107	24	feature	feature	NOUN
fcis-29234	107	25	extraction	extraction	NOUN
fcis-29234	107	26	capability	capability	NOUN
fcis-29234	107	27	through	through	ADP
fcis-29234	107	28	the	the	DET
fcis-29234	107	29	incorporation	incorporation	NOUN
fcis-29234	107	30	of	of	ADP
fcis-29234	107	31	57	57	NUM
fcis-29234	107	32	channel	channel	NOUN
fcis-29234	107	33	attention	attention	NOUN
fcis-29234	107	34	.	.	PUNCT
fcis-29234	108	1	the	the	DET
fcis-29234	108	2	introduction	introduction	NOUN
fcis-29234	108	3	of	of	ADP
fcis-29234	108	4	the	the	DET
fcis-29234	108	5	bottleneck	bottleneck	NOUN
fcis-29234	108	6	attention	attention	NOUN
fcis-29234	108	7	mechanism	mechanism	NOUN
fcis-29234	108	8	into	into	ADP
fcis-29234	108	9	the	the	DET
fcis-29234	108	10	alignedreid	alignedreid	NOUN
fcis-29234	108	11	algorithm	algorithm	NOUN
fcis-29234	108	12	brings	bring	VERB
fcis-29234	108	13	several	several	ADJ
fcis-29234	108	14	benefits	benefit	NOUN
fcis-29234	108	15	:	:	PUNCT
fcis-29234	108	16	enhanced	enhance	VERB
fcis-29234	108	17	feature	feature	NOUN
fcis-29234	108	18	discriminability	discriminability	NOUN
fcis-29234	108	19	small	small	ADJ
fcis-29234	108	20	targets	target	NOUN
fcis-29234	108	21	at	at	ADP
fcis-29234	108	22	long	long	ADJ
fcis-29234	108	23	distances	distance	NOUN
fcis-29234	108	24	often	often	ADV
fcis-29234	108	25	suffer	suffer	VERB
fcis-29234	108	26	from	from	ADP
fcis-29234	108	27	low	low	ADJ
fcis-29234	108	28	resolution	resolution	NOUN
fcis-29234	108	29	,	,	PUNCT
fcis-29234	108	30	making	make	VERB
fcis-29234	108	31	their	their	PRON
fcis-29234	108	32	features	feature	NOUN
fcis-29234	108	33	blurry	blurry	ADJ
fcis-29234	108	34	.	.	PUNCT
fcis-29234	109	1	by	by	ADP
fcis-29234	109	2	introducing	introduce	VERB
fcis-29234	109	3	the	the	DET
fcis-29234	109	4	bottleneck	bottleneck	NOUN
fcis-29234	109	5	attention	attention	NOUN
fcis-29234	109	6	mechanism	mechanism	NOUN
fcis-29234	109	7	into	into	ADP
fcis-29234	109	8	the	the	DET
fcis-29234	109	9	bottleneck	bottleneck	NOUN
fcis-29234	109	10	blocks	block	NOUN
fcis-29234	109	11	of	of	ADP
fcis-29234	109	12	the	the	DET
fcis-29234	109	13	resnet	resnet	NOUN
fcis-29234	109	14	model	model	NOUN
fcis-29234	109	15	,	,	PUNCT
fcis-29234	109	16	this	this	DET
fcis-29234	109	17	module	module	NOUN
fcis-29234	109	18	assigns	assign	VERB
fcis-29234	109	19	different	different	ADJ
fcis-29234	109	20	weights	weight	NOUN
fcis-29234	109	21	to	to	ADP
fcis-29234	109	22	each	each	DET
fcis-29234	109	23	channel	channel	NOUN
fcis-29234	109	24	,	,	PUNCT
fcis-29234	109	25	automatically	automatically	ADV
fcis-29234	109	26	focusing	focus	VERB
fcis-29234	109	27	on	on	ADP
fcis-29234	109	28	more	more	ADJ
fcis-29234	109	29	discriminative	discriminative	NOUN
fcis-29234	109	30	features	feature	NOUN
fcis-29234	109	31	.	.	PUNCT
fcis-29234	110	1	suppression	suppression	NOUN
fcis-29234	110	2	of	of	ADP
fcis-29234	110	3	redundant	redundant	ADJ
fcis-29234	110	4	information	information	NOUN
fcis-29234	110	5	:	:	PUNCT
fcis-29234	110	6	background	background	NOUN
fcis-29234	110	7	or	or	CCONJ
fcis-29234	110	8	irrelevant	irrelevant	ADJ
fcis-29234	110	9	information	information	NOUN
fcis-29234	110	10	in	in	ADP
fcis-29234	110	11	images	image	NOUN
fcis-29234	110	12	of	of	ADP
fcis-29234	110	13	small	small	ADJ
fcis-29234	110	14	targets	target	NOUN
fcis-29234	110	15	at	at	ADP
fcis-29234	110	16	long	long	ADJ
fcis-29234	110	17	distances	distance	NOUN
fcis-29234	110	18	can	can	AUX
fcis-29234	110	19	interfere	interfere	VERB
fcis-29234	110	20	with	with	ADP
fcis-29234	110	21	the	the	DET
fcis-29234	110	22	extraction	extraction	NOUN
fcis-29234	110	23	of	of	ADP
fcis-29234	110	24	target	target	NOUN
fcis-29234	110	25	features	feature	NOUN
fcis-29234	110	26	.	.	PUNCT
fcis-29234	111	1	the	the	DET
fcis-29234	111	2	bottleneck	bottleneck	NOUN
fcis-29234	111	3	attention	attention	NOUN
fcis-29234	111	4	mechanism	mechanism	NOUN
fcis-29234	111	5	dynamically	dynamically	ADV
fcis-29234	111	6	adjusts	adjust	VERB
fcis-29234	111	7	attention	attention	NOUN
fcis-29234	111	8	weights	weight	NOUN
fcis-29234	111	9	across	across	ADP
fcis-29234	111	10	channels	channel	NOUN
fcis-29234	111	11	to	to	PART
fcis-29234	111	12	suppress	suppress	VERB
fcis-29234	111	13	features	feature	NOUN
fcis-29234	111	14	that	that	PRON
fcis-29234	111	15	are	be	AUX
fcis-29234	111	16	unrelated	unrelated	ADJ
fcis-29234	111	17	to	to	PART
fcis-29234	111	18	target	target	VERB
fcis-29234	111	19	recognition	recognition	NOUN
fcis-29234	111	20	.	.	PUNCT
fcis-29234	112	1	improved	improve	VERB
fcis-29234	112	2	capturing	capturing	NOUN
fcis-29234	112	3	of	of	ADP
fcis-29234	112	4	small	small	ADJ
fcis-29234	112	5	target	target	NOUN
fcis-29234	112	6	details	detail	NOUN
fcis-29234	112	7	:	:	PUNCT
fcis-29234	112	8	the	the	DET
fcis-29234	112	9	bottleneck	bottleneck	NOUN
fcis-29234	112	10	attention	attention	NOUN
fcis-29234	112	11	mechanism	mechanism	NOUN
fcis-29234	112	12	uses	use	VERB
fcis-29234	112	13	global	global	ADJ
fcis-29234	112	14	pooling	pool	VERB
fcis-29234	112	15	operations	operation	NOUN
fcis-29234	112	16	to	to	PART
fcis-29234	112	17	generate	generate	VERB
fcis-29234	112	18	channel	channel	NOUN
fcis-29234	112	19	descriptors	descriptor	NOUN
fcis-29234	112	20	,	,	PUNCT
fcis-29234	112	21	allowing	allow	VERB
fcis-29234	112	22	the	the	DET
fcis-29234	112	23	model	model	NOUN
fcis-29234	112	24	to	to	PART
fcis-29234	112	25	learn	learn	VERB
fcis-29234	112	26	global	global	ADJ
fcis-29234	112	27	context	context	NOUN
fcis-29234	112	28	in	in	ADP
fcis-29234	112	29	the	the	DET
fcis-29234	112	30	image	image	NOUN
fcis-29234	112	31	and	and	CCONJ
fcis-29234	112	32	focus	focus	VERB
fcis-29234	112	33	on	on	ADP
fcis-29234	112	34	detailed	detailed	ADJ
fcis-29234	112	35	features	feature	NOUN
fcis-29234	112	36	.	.	PUNCT
fcis-29234	113	1	this	this	PRON
fcis-29234	113	2	is	be	AUX
fcis-29234	113	3	particularly	particularly	ADV
fcis-29234	113	4	important	important	ADJ
fcis-29234	113	5	for	for	ADP
fcis-29234	113	6	small	small	ADJ
fcis-29234	113	7	targets	target	NOUN
fcis-29234	113	8	at	at	ADP
fcis-29234	113	9	long	long	ADJ
fcis-29234	113	10	distances	distance	NOUN
fcis-29234	113	11	,	,	PUNCT
fcis-29234	113	12	as	as	SCONJ
fcis-29234	113	13	they	they	PRON
fcis-29234	113	14	often	often	ADV
fcis-29234	113	15	lack	lack	VERB
fcis-29234	113	16	significant	significant	ADJ
fcis-29234	113	17	largescale	largescale	NOUN
fcis-29234	113	18	features	feature	NOUN
fcis-29234	113	19	and	and	CCONJ
fcis-29234	113	20	rely	rely	VERB
fcis-29234	113	21	on	on	ADP
fcis-29234	113	22	finer	fine	ADJ
fcis-29234	113	23	details	detail	NOUN
fcis-29234	113	24	for	for	ADP
fcis-29234	113	25	differentiation	differentiation	NOUN
fcis-29234	113	26	.	.	PUNCT
fcis-29234	114	1	adaptation	adaptation	NOUN
fcis-29234	114	2	to	to	ADP
fcis-29234	114	3	multi	multi	ADJ
fcis-29234	114	4	-	-	ADJ
fcis-29234	114	5	scale	scale	ADJ
fcis-29234	114	6	feature	feature	NOUN
fcis-29234	114	7	extraction	extraction	NOUN
fcis-29234	114	8	:	:	PUNCT
fcis-29234	114	9	small	small	ADJ
fcis-29234	114	10	targets	target	NOUN
fcis-29234	114	11	at	at	ADP
fcis-29234	114	12	long	long	ADJ
fcis-29234	114	13	distances	distance	NOUN
fcis-29234	114	14	vary	vary	VERB
fcis-29234	114	15	greatly	greatly	ADV
fcis-29234	114	16	in	in	ADP
fcis-29234	114	17	size	size	NOUN
fcis-29234	114	18	,	,	PUNCT
fcis-29234	114	19	necessitating	necessitate	VERB
fcis-29234	114	20	multi	multi	ADJ
fcis-29234	114	21	-	-	ADJ
fcis-29234	114	22	scale	scale	ADJ
fcis-29234	114	23	feature	feature	NOUN
fcis-29234	114	24	extraction	extraction	NOUN
fcis-29234	114	25	for	for	ADP
fcis-29234	114	26	effective	effective	ADJ
fcis-29234	114	27	recognition	recognition	NOUN
fcis-29234	114	28	.	.	PUNCT
fcis-29234	115	1	the	the	DET
fcis-29234	115	2	introduction	introduction	NOUN
fcis-29234	115	3	of	of	ADP
fcis-29234	115	4	the	the	DET
fcis-29234	115	5	bottleneck	bottleneck	NOUN
fcis-29234	115	6	attention	attention	NOUN
fcis-29234	115	7	mechanism	mechanism	NOUN
fcis-29234	115	8	enhances	enhance	VERB
fcis-29234	115	9	the	the	DET
fcis-29234	115	10	model	model	NOUN
fcis-29234	115	11	’s	’s	PART
fcis-29234	115	12	ability	ability	NOUN
fcis-29234	115	13	to	to	PART
fcis-29234	115	14	adapt	adapt	VERB
fcis-29234	115	15	to	to	ADP
fcis-29234	115	16	multi	multi	ADJ
fcis-29234	115	17	-	-	ADJ
fcis-29234	115	18	scale	scale	ADJ
fcis-29234	115	19	information	information	NOUN
fcis-29234	115	20	by	by	ADP
fcis-29234	115	21	automatically	automatically	ADV
fcis-29234	115	22	adjusting	adjust	VERB
fcis-29234	115	23	the	the	DET
fcis-29234	115	24	attention	attention	NOUN
fcis-29234	115	25	weights	weight	NOUN
fcis-29234	115	26	across	across	ADP
fcis-29234	115	27	feature	feature	NOUN
fcis-29234	115	28	map	map	NOUN
fcis-29234	115	29	channels	channel	NOUN
fcis-29234	115	30	,	,	PUNCT
fcis-29234	115	31	allowing	allow	VERB
fcis-29234	115	32	the	the	DET
fcis-29234	115	33	model	model	NOUN
fcis-29234	115	34	to	to	PART
fcis-29234	115	35	extract	extract	VERB
fcis-29234	115	36	useful	useful	ADJ
fcis-29234	115	37	information	information	NOUN
fcis-29234	115	38	from	from	ADP
fcis-29234	115	39	various	various	ADJ
fcis-29234	115	40	scales	scale	NOUN
fcis-29234	115	41	.	.	PUNCT
fcis-29234	116	1	improved	improve	VERB
fcis-29234	116	2	robustness	robustness	NOUN
fcis-29234	116	3	and	and	CCONJ
fcis-29234	116	4	accuracy	accuracy	NOUN
fcis-29234	116	5	of	of	ADP
fcis-29234	116	6	the	the	DET
fcis-29234	116	7	network	network	NOUN
fcis-29234	116	8	:	:	PUNCT
fcis-29234	116	9	the	the	DET
fcis-29234	116	10	bottleneck	bottleneck	NOUN
fcis-29234	116	11	attention	attention	NOUN
fcis-29234	116	12	mechanism	mechanism	NOUN
fcis-29234	116	13	not	not	PART
fcis-29234	116	14	only	only	ADV
fcis-29234	116	15	focuses	focus	VERB
fcis-29234	116	16	on	on	ADP
fcis-29234	116	17	important	important	ADJ
fcis-29234	116	18	features	feature	NOUN
fcis-29234	116	19	but	but	CCONJ
fcis-29234	116	20	also	also	ADV
fcis-29234	116	21	adaptively	adaptively	ADV
fcis-29234	116	22	adjusts	adjust	VERB
fcis-29234	116	23	gradients	gradient	NOUN
fcis-29234	116	24	during	during	ADP
fcis-29234	116	25	backpropagation	backpropagation	NOUN
fcis-29234	116	26	to	to	PART
fcis-29234	116	27	strengthen	strengthen	VERB
fcis-29234	116	28	learning	learn	VERB
fcis-29234	116	29	from	from	ADP
fcis-29234	116	30	critical	critical	ADJ
fcis-29234	116	31	channels	channel	NOUN
fcis-29234	116	32	,	,	PUNCT
fcis-29234	116	33	thus	thus	ADV
fcis-29234	116	34	maintaining	maintain	VERB
fcis-29234	116	35	high	high	ADJ
fcis-29234	116	36	precision	precision	NOUN
fcis-29234	116	37	in	in	ADP
fcis-29234	116	38	target	target	NOUN
fcis-29234	116	39	recognition	recognition	NOUN
fcis-29234	116	40	in	in	ADP
fcis-29234	116	41	complex	complex	ADJ
fcis-29234	116	42	environments	environment	NOUN
fcis-29234	116	43	.	.	PUNCT
fcis-29234	117	1	in	in	ADP
fcis-29234	117	2	summary	summary	NOUN
fcis-29234	117	3	,	,	PUNCT
fcis-29234	117	4	introducing	introduce	VERB
fcis-29234	117	5	the	the	DET
fcis-29234	117	6	bottleneck	bottleneck	NOUN
fcis-29234	117	7	attention	attention	NOUN
fcis-29234	117	8	mechanism	mechanism	NOUN
fcis-29234	117	9	into	into	ADP
fcis-29234	117	10	alignedreid	alignedreid	PROPN
fcis-29234	117	11	significantly	significantly	ADV
fcis-29234	117	12	improves	improve	VERB
fcis-29234	117	13	the	the	DET
fcis-29234	117	14	reidentification	reidentification	NOUN
fcis-29234	117	15	performance	performance	NOUN
fcis-29234	117	16	of	of	ADP
fcis-29234	117	17	small	small	ADJ
fcis-29234	117	18	targets	target	NOUN
fcis-29234	117	19	at	at	ADP
fcis-29234	117	20	long	long	ADJ
fcis-29234	117	21	distances	distance	NOUN
fcis-29234	117	22	.	.	PUNCT
fcis-29234	118	1	this	this	PRON
fcis-29234	118	2	is	be	AUX
fcis-29234	118	3	achieved	achieve	VERB
fcis-29234	118	4	through	through	ADP
fcis-29234	118	5	enhanced	enhance	VERB
fcis-29234	118	6	feature	feature	NOUN
fcis-29234	118	7	discriminability	discriminability	NOUN
fcis-29234	118	8	,	,	PUNCT
fcis-29234	118	9	suppression	suppression	NOUN
fcis-29234	118	10	of	of	ADP
fcis-29234	118	11	redundant	redundant	ADJ
fcis-29234	118	12	information	information	NOUN
fcis-29234	118	13	,	,	PUNCT
fcis-29234	118	14	better	well	ADJ
fcis-29234	118	15	capture	capture	NOUN
fcis-29234	118	16	of	of	ADP
fcis-29234	118	17	detailed	detailed	ADJ
fcis-29234	118	18	features	feature	NOUN
fcis-29234	118	19	,	,	PUNCT
fcis-29234	118	20	improved	improve	VERB
fcis-29234	118	21	multi	multi	ADJ
fcis-29234	118	22	-	-	ADJ
fcis-29234	118	23	scale	scale	ADJ
fcis-29234	118	24	adaptability	adaptability	NOUN
fcis-29234	118	25	,	,	PUNCT
fcis-29234	118	26	and	and	CCONJ
fcis-29234	118	27	increased	increase	VERB
fcis-29234	118	28	robustness	robustness	NOUN
fcis-29234	118	29	.	.	PUNCT
fcis-29234	119	1	these	these	DET
fcis-29234	119	2	improvements	improvement	NOUN
fcis-29234	119	3	allow	allow	VERB
fcis-29234	119	4	the	the	DET
fcis-29234	119	5	network	network	NOUN
fcis-29234	119	6	to	to	PART
fcis-29234	119	7	effectively	effectively	ADV
fcis-29234	119	8	perform	perform	VERB
fcis-29234	119	9	small	small	ADJ
fcis-29234	119	10	target	target	NOUN
fcis-29234	119	11	re	re	NOUN
fcis-29234	119	12	-	-	NOUN
fcis-29234	119	13	identification	identification	NOUN
fcis-29234	119	14	even	even	ADV
fcis-29234	119	15	in	in	ADP
fcis-29234	119	16	the	the	DET
fcis-29234	119	17	presence	presence	NOUN
fcis-29234	119	18	of	of	ADP
fcis-29234	119	19	challenges	challenge	NOUN
fcis-29234	119	20	such	such	ADJ
fcis-29234	119	21	as	as	ADP
fcis-29234	119	22	long	long	ADJ
fcis-29234	119	23	distances	distance	NOUN
fcis-29234	119	24	,	,	PUNCT
fcis-29234	119	25	low	low	ADJ
fcis-29234	119	26	resolution	resolution	NOUN
fcis-29234	119	27	,	,	PUNCT
fcis-29234	119	28	and	and	CCONJ
fcis-29234	119	29	complex	complex	ADJ
fcis-29234	119	30	backgrounds	background	NOUN
fcis-29234	119	31	.	.	PUNCT
fcis-29234	120	1	fig	fig	NOUN
fcis-29234	120	2	5	5	NUM
fcis-29234	120	3	.	.	PUNCT
fcis-29234	120	4	bottleneck	bottleneck	NOUN
fcis-29234	120	5	attention	attention	NOUN
fcis-29234	120	6	mechanism	mechanism	NOUN
fcis-29234	120	7	module	module	NOUN
fcis-29234	120	8	4	4	NUM
fcis-29234	120	9	.	.	PUNCT
fcis-29234	120	10	application	application	NOUN
fcis-29234	120	11	example	example	NOUN
fcis-29234	120	12	comparison	comparison	NOUN
fcis-29234	120	13	in	in	ADP
fcis-29234	120	14	terms	term	NOUN
fcis-29234	120	15	of	of	ADP
fcis-29234	120	16	the	the	DET
fcis-29234	120	17	experimental	experimental	ADJ
fcis-29234	120	18	environment	environment	NOUN
fcis-29234	120	19	and	and	CCONJ
fcis-29234	120	20	configuration	configuration	NOUN
fcis-29234	120	21	,	,	PUNCT
fcis-29234	120	22	we	we	PRON
fcis-29234	120	23	utilized	utilize	VERB
fcis-29234	120	24	a	a	DET
fcis-29234	120	25	windows	window	NOUN
fcis-29234	120	26	-	-	PUNCT
fcis-29234	120	27	based	base	VERB
fcis-29234	120	28	software	software	NOUN
fcis-29234	120	29	and	and	CCONJ
fcis-29234	120	30	hardware	hardware	NOUN
fcis-29234	120	31	development	development	NOUN
fcis-29234	120	32	environment	environment	NOUN
fcis-29234	120	33	,	,	PUNCT
fcis-29234	120	34	featuring	feature	VERB
fcis-29234	120	35	a	a	DET
fcis-29234	120	36	13thgeneration	13thgeneration	NUM
fcis-29234	120	37	intel	intel	PROPN
fcis-29234	120	38	core	core	NOUN
fcis-29234	120	39	i9	i9	NOUN
fcis-29234	120	40	-	-	PUNCT
fcis-29234	120	41	13900hx	13900hx	NOUN
fcis-29234	120	42	cpu	cpu	NOUN
fcis-29234	120	43	,	,	PUNCT
fcis-29234	120	44	nvidia	nvidia	PROPN
fcis-29234	120	45	geforce	geforce	NOUN
fcis-29234	120	46	rtx	rtx	PROPN
fcis-29234	120	47	4090	4090	NUM
fcis-29234	120	48	gpu	gpu	PROPN
fcis-29234	120	49	,	,	PUNCT
fcis-29234	120	50	and	and	CCONJ
fcis-29234	120	51	the	the	DET
fcis-29234	120	52	pytorch	pytorch	NOUN
fcis-29234	120	53	deep	deep	ADJ
fcis-29234	120	54	learning	learning	NOUN
fcis-29234	120	55	framework	framework	NOUN
fcis-29234	120	56	.	.	PUNCT
fcis-29234	121	1	key	key	ADJ
fcis-29234	121	2	experimental	experimental	ADJ
fcis-29234	121	3	configurations	configuration	NOUN
fcis-29234	121	4	included	include	VERB
fcis-29234	121	5	learning	learning	NOUN
fcis-29234	121	6	rate	rate	NOUN
fcis-29234	121	7	,	,	PUNCT
fcis-29234	121	8	batch	batch	NOUN
fcis-29234	121	9	size	size	NOUN
fcis-29234	121	10	,	,	PUNCT
fcis-29234	121	11	and	and	CCONJ
fcis-29234	121	12	the	the	DET
fcis-29234	121	13	number	number	NOUN
fcis-29234	121	14	of	of	ADP
fcis-29234	121	15	iterations	iteration	NOUN
fcis-29234	121	16	,	,	PUNCT
fcis-29234	121	17	all	all	PRON
fcis-29234	121	18	of	of	ADP
fcis-29234	121	19	which	which	PRON
fcis-29234	121	20	significantly	significantly	ADV
fcis-29234	121	21	influenced	influence	VERB
fcis-29234	121	22	the	the	DET
fcis-29234	121	23	performance	performance	NOUN
fcis-29234	121	24	of	of	ADP
fcis-29234	121	25	the	the	DET
fcis-29234	121	26	algorithm	algorithm	NOUN
fcis-29234	121	27	.	.	PUNCT
fcis-29234	122	1	experimental	experimental	ADJ
fcis-29234	122	2	dataset	dataset	NOUN
fcis-29234	122	3	and	and	CCONJ
fcis-29234	122	4	evaluation	evaluation	NOUN
fcis-29234	122	5	metrics	metric	NOUN
fcis-29234	122	6	this	this	DET
fcis-29234	122	7	study	study	NOUN
fcis-29234	122	8	used	use	VERB
fcis-29234	122	9	publicly	publicly	ADV
fcis-29234	122	10	available	available	ADJ
fcis-29234	122	11	object	object	NOUN
fcis-29234	122	12	detection	detection	NOUN
fcis-29234	122	13	datasets	dataset	NOUN
fcis-29234	122	14	for	for	ADP
fcis-29234	122	15	experimental	experimental	ADJ
fcis-29234	122	16	validation	validation	NOUN
fcis-29234	122	17	.	.	PUNCT
fcis-29234	123	1	these	these	DET
fcis-29234	123	2	datasets	dataset	NOUN
fcis-29234	123	3	encompass	encompass	VERB
fcis-29234	123	4	images	image	NOUN
fcis-29234	123	5	of	of	ADP
fcis-29234	123	6	targets	target	NOUN
fcis-29234	123	7	in	in	ADP
fcis-29234	123	8	diverse	diverse	ADJ
fcis-29234	123	9	scenarios	scenario	NOUN
fcis-29234	123	10	,	,	PUNCT
fcis-29234	123	11	providing	provide	VERB
fcis-29234	123	12	high	high	ADJ
fcis-29234	123	13	representativeness	representativeness	ADJ
fcis-29234	123	14	and	and	CCONJ
fcis-29234	123	15	challenges	challenge	NOUN
fcis-29234	123	16	.	.	PUNCT
fcis-29234	124	1	the	the	DET
fcis-29234	124	2	main	main	ADJ
fcis-29234	124	3	evaluation	evaluation	NOUN
fcis-29234	124	4	metrics	metric	NOUN
fcis-29234	124	5	included	include	VERB
fcis-29234	124	6	mean	mean	ADJ
fcis-29234	124	7	average	average	ADJ
fcis-29234	124	8	precision	precision	NOUN
fcis-29234	124	9	(	(	PUNCT
fcis-29234	124	10	map	map	NOUN
fcis-29234	124	11	)	)	PUNCT
fcis-29234	124	12	,	,	PUNCT
fcis-29234	124	13	recall	recall	NOUN
fcis-29234	124	14	,	,	PUNCT
fcis-29234	124	15	f1	f1	NOUN
fcis-29234	124	16	score	score	NOUN
fcis-29234	124	17	,	,	PUNCT
fcis-29234	124	18	rank-1	rank-1	ADP
fcis-29234	124	19	,	,	PUNCT
fcis-29234	124	20	and	and	CCONJ
fcis-29234	124	21	rank-5	rank-5	NUM
fcis-29234	124	22	,	,	PUNCT
fcis-29234	124	23	offering	offer	VERB
fcis-29234	124	24	a	a	DET
fcis-29234	124	25	comprehensive	comprehensive	ADJ
fcis-29234	124	26	evaluation	evaluation	NOUN
fcis-29234	124	27	of	of	ADP
fcis-29234	124	28	the	the	DET
fcis-29234	124	29	algorithm	algorithm	NOUN
fcis-29234	124	30	's	's	PART
fcis-29234	124	31	performance	performance	NOUN
fcis-29234	124	32	.	.	PUNCT
fcis-29234	125	1	4.1	4.1	NUM
fcis-29234	125	2	.	.	PUNCT
fcis-29234	126	1	optimization	optimization	NOUN
fcis-29234	126	2	results	result	NOUN
fcis-29234	126	3	of	of	ADP
fcis-29234	126	4	object	object	NOUN
fcis-29234	126	5	detector	detector	NOUN
fcis-29234	126	6	algorithm	algorithm	NOUN
fcis-29234	126	7	as	as	SCONJ
fcis-29234	126	8	shown	show	VERB
fcis-29234	126	9	in	in	ADP
fcis-29234	126	10	the	the	DET
fcis-29234	126	11	table	table	NOUN
fcis-29234	126	12	1	1	NUM
fcis-29234	126	13	,	,	PUNCT
fcis-29234	126	14	the	the	DET
fcis-29234	126	15	experimental	experimental	ADJ
fcis-29234	126	16	results	result	NOUN
fcis-29234	126	17	summarize	summarize	VERB
fcis-29234	126	18	the	the	DET
fcis-29234	126	19	optimized	optimize	VERB
fcis-29234	126	20	yolov8	yolov8	NOUN
fcis-29234	126	21	-	-	PUNCT
fcis-29234	126	22	based	base	VERB
fcis-29234	126	23	object	object	NOUN
fcis-29234	126	24	detection	detection	NOUN
fcis-29234	126	25	algorithm	algorithm	NOUN
fcis-29234	126	26	with	with	ADP
fcis-29234	126	27	integrated	integrate	VERB
fcis-29234	126	28	enhancements	enhancement	NOUN
fcis-29234	126	29	.	.	PUNCT
fcis-29234	127	1	the	the	DET
fcis-29234	127	2	results	result	NOUN
fcis-29234	127	3	demonstrate	demonstrate	VERB
fcis-29234	127	4	that	that	SCONJ
fcis-29234	127	5	the	the	DET
fcis-29234	127	6	yolov8	yolov8	PROPN
fcis-29234	127	7	-	-	PUNCT
fcis-29234	127	8	ema	ema	PROPN
fcis-29234	127	9	-	-	PUNCT
fcis-29234	127	10	rfb	rfb	PROPN
fcis-29234	127	11	algorithm	algorithm	PROPN
fcis-29234	127	12	achieves	achieve	VERB
fcis-29234	127	13	higher	high	ADJ
fcis-29234	127	14	map	map	NOUN
fcis-29234	127	15	compared	compare	VERB
fcis-29234	127	16	to	to	ADP
fcis-29234	127	17	other	other	ADJ
fcis-29234	127	18	algorithms	algorithm	NOUN
fcis-29234	127	19	,	,	PUNCT
fcis-29234	127	20	with	with	ADP
fcis-29234	127	21	minimal	minimal	ADJ
fcis-29234	127	22	changes	change	NOUN
fcis-29234	127	23	in	in	ADP
fcis-29234	127	24	parameter	parameter	NOUN
fcis-29234	127	25	count	count	NOUN
fcis-29234	127	26	and	and	CCONJ
fcis-29234	127	27	frames	frame	NOUN
fcis-29234	127	28	per	per	ADP
fcis-29234	127	29	second	second	ADJ
fcis-29234	127	30	(	(	PUNCT
fcis-29234	127	31	fps	fps	PROPN
fcis-29234	127	32	)	)	PUNCT
fcis-29234	127	33	.	.	PUNCT
fcis-29234	128	1	this	this	PRON
fcis-29234	128	2	indicates	indicate	VERB
fcis-29234	128	3	the	the	DET
fcis-29234	128	4	effectiveness	effectiveness	NOUN
fcis-29234	128	5	of	of	ADP
fcis-29234	128	6	the	the	DET
fcis-29234	128	7	yolov8	yolov8	NOUN
fcis-29234	128	8	optimization	optimization	NOUN
fcis-29234	128	9	,	,	PUNCT
fcis-29234	128	10	contributing	contribute	VERB
fcis-29234	128	11	to	to	ADP
fcis-29234	128	12	improved	improved	ADJ
fcis-29234	128	13	accuracy	accuracy	NOUN
fcis-29234	128	14	in	in	ADP
fcis-29234	128	15	multi	multi	ADJ
fcis-29234	128	16	-	-	ADJ
fcis-29234	128	17	target	target	ADJ
fcis-29234	128	18	tracking	tracking	NOUN
fcis-29234	128	19	.	.	PUNCT
fcis-29234	129	1	table	table	NOUN
fcis-29234	129	2	1	1	NUM
fcis-29234	129	3	.	.	PUNCT
fcis-29234	129	4	optimized	optimize	VERB
fcis-29234	129	5	results	result	NOUN
fcis-29234	129	6	for	for	ADP
fcis-29234	129	7	the	the	DET
fcis-29234	129	8	yolov8	yolov8	NOUN
fcis-29234	129	9	-	-	PUNCT
fcis-29234	129	10	based	base	VERB
fcis-29234	129	11	object	object	NOUN
fcis-29234	129	12	detection	detection	NOUN
fcis-29234	129	13	algorithm	algorithm	NOUN
fcis-29234	129	14	integration	integration	NOUN
fcis-29234	129	15	yolov8	yolov8	NOUN
fcis-29234	129	16	yolov8	yolov8	PROPN
fcis-29234	129	17	-	-	PUNCT
fcis-29234	129	18	ema	ema	PROPN
fcis-29234	129	19	yolov8	yolov8	PROPN
fcis-29234	129	20	-	-	PUNCT
fcis-29234	129	21	ema	ema	PROPN
fcis-29234	129	22	-	-	PUNCT
fcis-29234	129	23	rfb	rfb	PROPN
fcis-29234	129	24	params	param	NOUN
fcis-29234	129	25	/m	/m	PUNCT
fcis-29234	130	1	map@0.5	map@0.5	NOUN
fcis-29234	130	2	fps/	fps/	ADJ
fcis-29234	130	3	frame	frame	NOUN
fcis-29234	130	4	.	.	PUNCT
fcis-29234	131	1	s-1	s-1	PROPN
fcis-29234	131	2	gflops	gflop	NOUN
fcis-29234	131	3	recall	recall	VERB
fcis-29234	131	4	f1	f1	PROPN
fcis-29234	131	5	√	√	PROPN
fcis-29234	131	6	6.02	6.02	NUM
fcis-29234	131	7	84.3	84.3	NUM
fcis-29234	131	8	74	74	NUM
fcis-29234	131	9	13.2	13.2	NUM
fcis-29234	131	10	0.87	0.87	NUM
fcis-29234	131	11	0.85	0.85	NUM
fcis-29234	131	12	√	√	NUM
fcis-29234	131	13	8.99	8.99	NUM
fcis-29234	131	14	86.8	86.8	NUM
fcis-29234	131	15	71	71	NUM
fcis-29234	131	16	15.6	15.6	NUM
fcis-29234	131	17	0.89	0.89	NUM
fcis-29234	131	18	0.88	0.88	NUM
fcis-29234	131	19	√	√	PROPN
fcis-29234	131	20	9.39	9.39	NUM
fcis-29234	131	21	88.2	88.2	NUM
fcis-29234	131	22	69	69	NUM
fcis-29234	131	23	20.1	20.1	NUM
fcis-29234	131	24	0.91	0.91	NUM
fcis-29234	131	25	0.91	0.91	NUM
fcis-29234	131	26	4.2	4.2	NUM
fcis-29234	131	27	.	.	PUNCT
fcis-29234	132	1	optimization	optimization	NOUN
fcis-29234	132	2	results	result	NOUN
fcis-29234	132	3	of	of	ADP
fcis-29234	132	4	the	the	DET
fcis-29234	132	5	object	object	NOUN
fcis-29234	132	6	reidentification	reidentification	NOUN
fcis-29234	132	7	algorithm	algorithm	NOUN
fcis-29234	132	8	as	as	SCONJ
fcis-29234	132	9	shown	show	VERB
fcis-29234	132	10	in	in	ADP
fcis-29234	132	11	the	the	DET
fcis-29234	132	12	table	table	NOUN
fcis-29234	132	13	,	,	PUNCT
fcis-29234	132	14	a	a	DET
fcis-29234	132	15	comparison	comparison	NOUN
fcis-29234	132	16	is	be	AUX
fcis-29234	132	17	made	make	VERB
fcis-29234	132	18	between	between	ADP
fcis-29234	132	19	the	the	DET
fcis-29234	132	20	original	original	ADJ
fcis-29234	132	21	alignedreid	alignedreid	NOUN
fcis-29234	132	22	algorithm	algorithm	NOUN
fcis-29234	132	23	and	and	CCONJ
fcis-29234	132	24	the	the	DET
fcis-29234	132	25	optimized	optimize	VERB
fcis-29234	132	26	alignedreid	alignedreid	PROPN
fcis-29234	132	27	-	-	PUNCT
fcis-29234	132	28	veh	veh	PROPN
fcis-29234	132	29	algorithm	algorithm	NOUN
fcis-29234	132	30	.	.	PUNCT
fcis-29234	133	1	by	by	ADP
fcis-29234	133	2	comparing	compare	VERB
fcis-29234	133	3	the	the	DET
fcis-29234	133	4	mean	mean	ADJ
fcis-29234	133	5	average	average	ADJ
fcis-29234	133	6	precision	precision	NOUN
fcis-29234	133	7	(	(	PUNCT
fcis-29234	133	8	map	map	NOUN
fcis-29234	133	9	)	)	PUNCT
fcis-29234	133	10	and	and	CCONJ
fcis-29234	133	11	key	key	ADJ
fcis-29234	133	12	re	re	ADJ
fcis-29234	133	13	-	-	NOUN
fcis-29234	133	14	identification	identification	NOUN
fcis-29234	133	15	parameters	parameter	NOUN
fcis-29234	133	16	,	,	PUNCT
fcis-29234	133	17	such	such	ADJ
fcis-29234	133	18	as	as	ADP
fcis-29234	133	19	rank-1	rank-1	NUM
fcis-29234	133	20	and	and	CCONJ
fcis-29234	133	21	rank-5	rank-5	NUM
fcis-29234	133	22	,	,	PUNCT
fcis-29234	133	23	before	before	ADP
fcis-29234	133	24	and	and	CCONJ
fcis-29234	133	25	after	after	ADP
fcis-29234	133	26	the	the	DET
fcis-29234	133	27	addition	addition	NOUN
fcis-29234	133	28	of	of	ADP
fcis-29234	133	29	the	the	DET
fcis-29234	133	30	bottleneck	bottleneck	NOUN
fcis-29234	133	31	attention	attention	NOUN
fcis-29234	133	32	mechanism	mechanism	NOUN
fcis-29234	133	33	,	,	PUNCT
fcis-29234	133	34	it	it	PRON
fcis-29234	133	35	is	be	AUX
fcis-29234	133	36	evident	evident	ADJ
fcis-29234	133	37	that	that	SCONJ
fcis-29234	133	38	the	the	DET
fcis-29234	133	39	incorporation	incorporation	NOUN
fcis-29234	133	40	of	of	ADP
fcis-29234	133	41	the	the	DET
fcis-29234	133	42	bottleneck	bottleneck	NOUN
fcis-29234	133	43	attention	attention	NOUN
fcis-29234	133	44	mechanism	mechanism	NOUN
fcis-29234	133	45	module	module	NOUN
fcis-29234	133	46	significantly	significantly	ADV
fcis-29234	133	47	enhances	enhance	VERB
fcis-29234	133	48	the	the	DET
fcis-29234	133	49	detection	detection	NOUN
fcis-29234	133	50	accuracy	accuracy	NOUN
fcis-29234	133	51	of	of	ADP
fcis-29234	133	52	the	the	DET
fcis-29234	133	53	reidentification	reidentification	NOUN
fcis-29234	133	54	algorithm	algorithm	NOUN
fcis-29234	133	55	.	.	PUNCT
fcis-29234	134	1	58	58	NUM
fcis-29234	134	2	table	table	NOUN
fcis-29234	134	3	2	2	NUM
fcis-29234	134	4	.	.	PUNCT
fcis-29234	134	5	three	three	NUM
fcis-29234	134	6	scheme	scheme	NOUN
fcis-29234	134	7	comparing	compare	VERB
fcis-29234	134	8	map	map	NOUN
fcis-29234	134	9	rank-1	rank-1	NUM
fcis-29234	134	10	rank-5	rank-5	NUM
fcis-29234	134	11	aligenedreid	aligenedreid	PROPN
fcis-29234	134	12	88.4	88.4	NUM
fcis-29234	134	13	92.8	92.8	NUM
fcis-29234	134	14	97.9	97.9	NUM
fcis-29234	134	15	aligenedreid	aligenedreid	PROPN
fcis-29234	134	16	-	-	PUNCT
fcis-29234	134	17	veh	veh	PROPN
fcis-29234	134	18	89.3	89.3	NUM
fcis-29234	134	19	93.9	93.9	NUM
fcis-29234	134	20	98.1	98.1	NUM
fcis-29234	134	21	therefore	therefore	ADV
fcis-29234	134	22	,	,	PUNCT
fcis-29234	134	23	regarding	regard	VERB
fcis-29234	134	24	the	the	DET
fcis-29234	134	25	optimization	optimization	NOUN
fcis-29234	134	26	of	of	ADP
fcis-29234	134	27	the	the	DET
fcis-29234	134	28	yolov8	yolov8	NOUN
fcis-29234	134	29	object	object	NOUN
fcis-29234	134	30	detection	detection	NOUN
fcis-29234	134	31	algorithm	algorithm	NOUN
fcis-29234	134	32	,	,	PUNCT
fcis-29234	134	33	experimental	experimental	ADJ
fcis-29234	134	34	results	result	NOUN
fcis-29234	134	35	validate	validate	VERB
fcis-29234	134	36	that	that	SCONJ
fcis-29234	134	37	the	the	DET
fcis-29234	134	38	yolov8	yolov8	PROPN
fcis-29234	134	39	-	-	PUNCT
fcis-29234	134	40	ema	ema	PROPN
fcis-29234	134	41	-	-	PUNCT
fcis-29234	134	42	rfb	rfb	PROPN
fcis-29234	134	43	object	object	NOUN
fcis-29234	134	44	detection	detection	NOUN
fcis-29234	134	45	algorithm	algorithm	NOUN
fcis-29234	134	46	achieves	achieve	VERB
fcis-29234	134	47	higher	high	ADJ
fcis-29234	134	48	mean	mean	ADJ
fcis-29234	134	49	average	average	ADJ
fcis-29234	134	50	precision	precision	NOUN
fcis-29234	134	51	(	(	PUNCT
fcis-29234	134	52	map	map	NOUN
fcis-29234	134	53	)	)	PUNCT
fcis-29234	134	54	with	with	ADP
fcis-29234	134	55	fewer	few	ADJ
fcis-29234	134	56	parameters	parameter	NOUN
fcis-29234	134	57	and	and	CCONJ
fcis-29234	134	58	lower	low	ADJ
fcis-29234	134	59	frames	frame	NOUN
fcis-29234	134	60	per	per	ADP
fcis-29234	134	61	second	second	ADJ
fcis-29234	134	62	(	(	PUNCT
fcis-29234	134	63	fps	fps	PROPN
fcis-29234	134	64	)	)	PUNCT
fcis-29234	134	65	,	,	PUNCT
fcis-29234	134	66	indicating	indicate	VERB
fcis-29234	134	67	that	that	SCONJ
fcis-29234	134	68	the	the	DET
fcis-29234	134	69	algorithm	algorithm	NOUN
fcis-29234	134	70	optimization	optimization	NOUN
fcis-29234	134	71	for	for	ADP
fcis-29234	134	72	yolov8	yolov8	NOUN
fcis-29234	134	73	is	be	AUX
fcis-29234	134	74	effective	effective	ADJ
fcis-29234	134	75	.	.	PUNCT
fcis-29234	135	1	at	at	ADP
fcis-29234	135	2	the	the	DET
fcis-29234	135	3	same	same	ADJ
fcis-29234	135	4	time	time	NOUN
fcis-29234	135	5	,	,	PUNCT
fcis-29234	135	6	for	for	ADP
fcis-29234	135	7	object	object	NOUN
fcis-29234	135	8	re	re	NOUN
fcis-29234	135	9	-	-	NOUN
fcis-29234	135	10	identification	identification	NOUN
fcis-29234	135	11	,	,	PUNCT
fcis-29234	135	12	experimental	experimental	ADJ
fcis-29234	135	13	data	datum	NOUN
fcis-29234	135	14	also	also	ADV
fcis-29234	135	15	demonstrate	demonstrate	VERB
fcis-29234	135	16	that	that	SCONJ
fcis-29234	135	17	the	the	DET
fcis-29234	135	18	introduction	introduction	NOUN
fcis-29234	135	19	of	of	ADP
fcis-29234	135	20	the	the	DET
fcis-29234	135	21	bottleneck	bottleneck	NOUN
fcis-29234	135	22	attention	attention	NOUN
fcis-29234	135	23	mechanism	mechanism	NOUN
fcis-29234	135	24	significantly	significantly	ADV
fcis-29234	135	25	improves	improve	VERB
fcis-29234	135	26	the	the	DET
fcis-29234	135	27	performance	performance	NOUN
fcis-29234	135	28	and	and	CCONJ
fcis-29234	135	29	efficiency	efficiency	NOUN
fcis-29234	135	30	of	of	ADP
fcis-29234	135	31	the	the	DET
fcis-29234	135	32	reid	reid	PROPN
fcis-29234	135	33	model	model	PROPN
fcis-29234	135	34	.	.	PUNCT
fcis-29234	136	1	specifically	specifically	ADV
fcis-29234	136	2	,	,	PUNCT
fcis-29234	136	3	the	the	DET
fcis-29234	136	4	addition	addition	NOUN
fcis-29234	136	5	of	of	ADP
fcis-29234	136	6	the	the	DET
fcis-29234	136	7	bottleneck	bottleneck	NOUN
fcis-29234	136	8	attention	attention	NOUN
fcis-29234	136	9	mechanism	mechanism	NOUN
fcis-29234	136	10	module	module	NOUN
fcis-29234	136	11	enhances	enhance	VERB
fcis-29234	136	12	the	the	DET
fcis-29234	136	13	map	map	NOUN
fcis-29234	136	14	and	and	CCONJ
fcis-29234	136	15	rank-1	rank-1	NUM
fcis-29234	136	16	accuracy	accuracy	NOUN
fcis-29234	136	17	across	across	ADP
fcis-29234	136	18	different	different	ADJ
fcis-29234	136	19	datasets	dataset	NOUN
fcis-29234	136	20	,	,	PUNCT
fcis-29234	136	21	accelerates	accelerate	VERB
fcis-29234	136	22	the	the	DET
fcis-29234	136	23	model	model	NOUN
fcis-29234	136	24	's	's	PART
fcis-29234	136	25	convergence	convergence	NOUN
fcis-29234	136	26	speed	speed	NOUN
fcis-29234	136	27	,	,	PUNCT
fcis-29234	136	28	and	and	CCONJ
fcis-29234	136	29	reduces	reduce	VERB
fcis-29234	136	30	the	the	DET
fcis-29234	136	31	model	model	NOUN
fcis-29234	136	32	's	's	PART
fcis-29234	136	33	parameter	parameter	NOUN
fcis-29234	136	34	count	count	NOUN
fcis-29234	136	35	.	.	PUNCT
fcis-29234	137	1	additionally	additionally	ADV
fcis-29234	137	2	,	,	PUNCT
fcis-29234	137	3	the	the	DET
fcis-29234	137	4	bottleneck	bottleneck	NOUN
fcis-29234	137	5	attention	attention	NOUN
fcis-29234	137	6	mechanism	mechanism	NOUN
fcis-29234	137	7	shows	show	VERB
fcis-29234	137	8	versatility	versatility	NOUN
fcis-29234	137	9	and	and	CCONJ
fcis-29234	137	10	advantages	advantage	NOUN
fcis-29234	137	11	across	across	ADP
fcis-29234	137	12	various	various	ADJ
fcis-29234	137	13	backbone	backbone	NOUN
fcis-29234	137	14	networks	network	NOUN
fcis-29234	137	15	by	by	ADP
fcis-29234	137	16	dynamically	dynamically	ADV
fcis-29234	137	17	adjusting	adjust	VERB
fcis-29234	137	18	channel	channel	NOUN
fcis-29234	137	19	weights	weight	NOUN
fcis-29234	137	20	,	,	PUNCT
fcis-29234	137	21	which	which	PRON
fcis-29234	137	22	enhances	enhance	VERB
fcis-29234	137	23	feature	feature	NOUN
fcis-29234	137	24	discrimination	discrimination	NOUN
fcis-29234	137	25	.	.	PUNCT
fcis-29234	138	1	overall	overall	ADV
fcis-29234	138	2	,	,	PUNCT
fcis-29234	138	3	the	the	DET
fcis-29234	138	4	bottleneck	bottleneck	NOUN
fcis-29234	138	5	attention	attention	NOUN
fcis-29234	138	6	mechanism	mechanism	NOUN
fcis-29234	138	7	module	module	NOUN
fcis-29234	138	8	strikes	strike	VERB
fcis-29234	138	9	an	an	DET
fcis-29234	138	10	excellent	excellent	ADJ
fcis-29234	138	11	balance	balance	NOUN
fcis-29234	138	12	between	between	ADP
fcis-29234	138	13	accuracy	accuracy	NOUN
fcis-29234	138	14	and	and	CCONJ
fcis-29234	138	15	efficiency	efficiency	NOUN
fcis-29234	138	16	,	,	PUNCT
fcis-29234	138	17	making	make	VERB
fcis-29234	138	18	it	it	PRON
fcis-29234	138	19	a	a	DET
fcis-29234	138	20	superior	superior	ADJ
fcis-29234	138	21	solution	solution	NOUN
fcis-29234	138	22	for	for	ADP
fcis-29234	138	23	attention	attention	NOUN
fcis-29234	138	24	mechanisms	mechanism	NOUN
fcis-29234	138	25	.	.	PUNCT
fcis-29234	139	1	acknowledgments	acknowledgment	NOUN
fcis-29234	139	2	national	national	PROPN
fcis-29234	139	3	natural	natural	PROPN
fcis-29234	139	4	science	science	PROPN
fcis-29234	139	5	foundation	foundation	PROPN
fcis-29234	139	6	of	of	ADP
fcis-29234	139	7	china	china	PROPN
fcis-29234	139	8	;	;	PUNCT
fcis-29234	139	9	national	national	PROPN
fcis-29234	139	10	science	science	PROPN
fcis-29234	139	11	foundation	foundation	PROPN
fcis-29234	139	12	of	of	ADP
fcis-29234	139	13	china	china	PROPN
fcis-29234	139	14	,	,	PUNCT
fcis-29234	139	15	xingxin	xingxin	PROPN
fcis-29234	140	1	li	li	PROPN
fcis-29234	140	2	,	,	PUNCT
fcis-29234	140	3	62206257	62206257	NUM
fcis-29234	140	4	.	.	PUNCT
fcis-29234	141	1	references	reference	NOUN
fcis-29234	141	2	[	[	X
fcis-29234	141	3	1	1	X
fcis-29234	141	4	]	]	PUNCT
fcis-29234	141	5	jin	jin	PROPN
fcis-29234	141	6	zifeng	zifeng	PROPN
fcis-29234	141	7	.	.	PUNCT
fcis-29234	142	1	research	research	NOUN
fcis-29234	142	2	on	on	ADP
fcis-29234	142	3	pedestrian	pedestrian	NOUN
fcis-29234	142	4	detection	detection	NOUN
fcis-29234	142	5	and	and	CCONJ
fcis-29234	142	6	reidentification	reidentification	NOUN
fcis-29234	142	7	technology	technology	NOUN
fcis-29234	142	8	in	in	ADP
fcis-29234	142	9	surveillance	surveillance	NOUN
fcis-29234	142	10	video	video	NOUN
fcis-29234	143	1	[	[	X
fcis-29234	143	2	d	d	X
fcis-29234	143	3	]	]	X
fcis-29234	143	4	.	.	PUNCT
fcis-29234	144	1	university	university	PROPN
fcis-29234	144	2	of	of	ADP
fcis-29234	144	3	chinese	chinese	PROPN
fcis-29234	144	4	academy	academy	PROPN
fcis-29234	144	5	of	of	ADP
fcis-29234	144	6	sciences	sciences	PROPN
fcis-29234	144	7	(	(	PUNCT
fcis-29234	144	8	national	national	ADJ
fcis-29234	144	9	center	center	NOUN
fcis-29234	144	10	for	for	ADP
fcis-29234	144	11	space	space	NOUN
fcis-29234	144	12	science	science	NOUN
fcis-29234	144	13	,	,	PUNCT
fcis-29234	144	14	chinese	chinese	PROPN
fcis-29234	144	15	academy	academy	PROPN
fcis-29234	144	16	of	of	ADP
fcis-29234	144	17	sciences	sciences	PROPN
fcis-29234	144	18	)	)	PUNCT
fcis-29234	144	19	,	,	PUNCT
fcis-29234	144	20	2021.doi	2021.doi	NUM
fcis-29234	144	21	:	:	PUNCT
fcis-29234	144	22	10.27562	10.27562	NUM
fcis-29234	144	23	/	/	SYM
fcis-29234	144	24	d.cnki.gkyyz.2021.000029	d.cnki.gkyyz.2021.000029	NOUN
fcis-29234	144	25	.	.	PUNCT
fcis-29234	145	1	[	[	X
fcis-29234	145	2	2	2	NUM
fcis-29234	145	3	]	]	X
fcis-29234	145	4	zhang	zhang	PROPN
fcis-29234	145	5	x	x	PROPN
fcis-29234	145	6	,	,	PUNCT
fcis-29234	145	7	zhang	zhang	PROPN
fcis-29234	145	8	r	r	PROPN
fcis-29234	145	9	,	,	PUNCT
fcis-29234	145	10	cao	cao	PROPN
fcis-29234	145	11	j	j	PROPN
fcis-29234	145	12	,	,	PUNCT
fcis-29234	145	13	et	et	NOUN
fcis-29234	145	14	al.part	al.part	ADV
fcis-29234	145	15	-	-	PUNCT
fcis-29234	145	16	guided	guide	VERB
fcis-29234	145	17	attention	attention	NOUN
fcis-29234	145	18	learning	learning	NOUN
fcis-29234	145	19	for	for	ADP
fcis-29234	145	20	vehicle	vehicle	NOUN
fcis-29234	145	21	instance	instance	PROPN
fcis-29234	145	22	retrieval[j].ieee	retrieval[j].ieee	NOUN
fcis-29234	145	23	transactions	transaction	NOUN
fcis-29234	145	24	on	on	ADP
fcis-29234	145	25	intelligent	intelligent	ADJ
fcis-29234	145	26	transportation	transportation	NOUN
fcis-29234	145	27	systems,2020,23(4):3048	systems,2020,23(4):3048	NOUN
fcis-29234	145	28	-	-	PUNCT
fcis-29234	145	29	3060	3060	NUM
fcis-29234	145	30	.	.	PUNCT
fcis-29234	146	1	[	[	X
fcis-29234	146	2	3	3	X
fcis-29234	146	3	]	]	X
fcis-29234	146	4	chen	chen	PROPN
fcis-29234	146	5	h	h	PROPN
fcis-29234	146	6	,	,	PUNCT
fcis-29234	146	7	lagadec	lagadec	PROPN
fcis-29234	146	8	b	b	NOUN
fcis-29234	146	9	,	,	PUNCT
fcis-29234	146	10	bremond	bremond	NOUN
fcis-29234	146	11	f.partition	f.partition	NOUN
fcis-29234	146	12	and	and	CCONJ
fcis-29234	146	13	reunion	reunion	NOUN
fcis-29234	146	14	:	:	PUNCT
fcis-29234	146	15	a	a	DET
fcis-29234	146	16	two	two	NUM
fcis-29234	146	17	-	-	PUNCT
fcis-29234	146	18	branch	branch	NOUN
fcis-29234	146	19	neural	neural	ADJ
fcis-29234	146	20	network	network	NOUN
fcis-29234	146	21	for	for	ADP
fcis-29234	146	22	vehicle	vehicle	NOUN
fcis-29234	146	23	reidentification	reidentification	NOUN
fcis-29234	146	24	[	[	X
fcis-29234	146	25	c]//2019	c]//2019	NUM
fcis-29234	146	26	ieee	ieee	NOUN
fcis-29234	146	27	/	/	SYM
fcis-29234	146	28	cvf	cvf	NOUN
fcis-29234	146	29	conference	conference	NOUN
fcis-29234	146	30	on	on	ADP
fcis-29234	146	31	computer	computer	NOUN
fcis-29234	146	32	vision	vision	NOUN
fcis-29234	146	33	and	and	CCONJ
fcis-29234	146	34	pattern	pattern	NOUN
fcis-29234	146	35	recognition	recognition	NOUN
fcis-29234	146	36	workshops	workshop	NOUN
fcis-29234	146	37	(	(	PUNCT
fcis-29234	146	38	cvprw),june	cvprw),june	NUM
fcis-29234	146	39	16	16	NUM
fcis-29234	146	40	-	-	SYM
fcis-29234	146	41	20	20	NUM
fcis-29234	146	42	,	,	PUNCT
fcis-29234	146	43	2019,long	2019,long	NUM
fcis-29234	146	44	beach	beach	NOUN
fcis-29234	146	45	,	,	PUNCT
fcis-29234	146	46	usa.new	usa.new	PROPN
fcis-29234	146	47	york	york	PROPN
fcis-29234	146	48	:	:	PUNCT
fcis-29234	146	49	ieee,2019:184	ieee,2019:184	VERB
fcis-29234	146	50	-	-	SYM
fcis-29234	146	51	192	192	NUM
fcis-29234	146	52	.	.	PUNCT
fcis-29234	147	1	[	[	X
fcis-29234	147	2	4	4	NUM
fcis-29234	147	3	]	]	X
fcis-29234	147	4	qian	qian	PROPN
fcis-29234	147	5	j	j	PROPN
fcis-29234	147	6	,	,	PUNCT
fcis-29234	147	7	jiang	jiang	PROPN
fcis-29234	147	8	w	w	PROPN
fcis-29234	147	9	,	,	PUNCT
fcis-29234	147	10	luo	luo	PROPN
fcis-29234	147	11	h	h	PROPN
fcis-29234	147	12	,	,	PUNCT
fcis-29234	147	13	et	et	NOUN
fcis-29234	147	14	al.stripe	al.stripe	NOUN
fcis-29234	147	15	-	-	PUNCT
fcis-29234	147	16	based	base	VERB
fcis-29234	147	17	and	and	CCONJ
fcis-29234	147	18	attributeaware	attributeaware	NOUN
fcis-29234	147	19	network	network	NOUN
fcis-29234	147	20	:	:	PUNCT
fcis-29234	147	21	a	a	DET
fcis-29234	147	22	two	two	NUM
fcis-29234	147	23	-	-	PUNCT
fcis-29234	147	24	branch	branch	NOUN
fcis-29234	147	25	deep	deep	ADJ
fcis-29234	147	26	model	model	NOUN
fcis-29234	147	27	for	for	ADP
fcis-29234	147	28	vehicle	vehicle	NOUN
fcis-29234	147	29	reidentificationj.measurement	reidentificationj.measurement	NUM
fcis-29234	147	30	science	science	NOUN
fcis-29234	147	31	and	and	CCONJ
fcis-29234	147	32	technology	technology	NOUN
fcis-29234	147	33	,	,	PUNCT
fcis-29234	147	34	2020,31	2020,31	NUM
fcis-29234	147	35	(	(	PUNCT
fcis-29234	147	36	9	9	NUM
fcis-29234	147	37	):	):	PUNCT
fcis-29234	147	38	095401	095401	NUM
fcis-29234	147	39	.	.	PUNCT
fcis-29234	148	1	[	[	X
fcis-29234	148	2	5	5	X
fcis-29234	148	3	]	]	PUNCT
fcis-29234	148	4	wang	wang	PROPN
fcis-29234	148	5	h	h	PROPN
fcis-29234	148	6	,	,	PUNCT
fcis-29234	148	7	peng	peng	PROPN
fcis-29234	148	8	j	j	PROPN
fcis-29234	148	9	,	,	PUNCT
fcis-29234	148	10	jiang	jiang	PROPN
fcis-29234	148	11	g	g	PROPN
fcis-29234	148	12	,	,	PUNCT
fcis-29234	148	13	et	et	NOUN
fcis-29234	148	14	al.discriminative	al.discriminative	ADJ
fcis-29234	148	15	feature	feature	NOUN
fcis-29234	148	16	and	and	CCONJ
fcis-29234	148	17	dictionary	dictionary	ADJ
fcis-29234	148	18	learning	learning	NOUN
fcis-29234	148	19	with	with	ADP
fcis-29234	148	20	part	part	ADJ
fcis-29234	148	21	-	-	PUNCT
fcis-29234	148	22	aware	aware	ADJ
fcis-29234	148	23	model	model	NOUN
fcis-29234	148	24	for	for	ADP
fcis-29234	148	25	vehicle	vehicle	NOUN
fcis-29234	148	26	reidentificationlj.neurocomputing,2021,438:55	reidentificationlj.neurocomputing,2021,438:55	PROPN
fcis-29234	148	27	-	-	PUNCT
fcis-29234	148	28	62	62	NUM
fcis-29234	148	29	.	.	PUNCT