id	sid	tid	token	lemma	pos
fcis-30156	1	1	frontiers	frontier	NOUN
fcis-30156	1	2	in	in	ADP
fcis-30156	1	3	computing	computing	NOUN
fcis-30156	1	4	and	and	CCONJ
fcis-30156	1	5	intelligent	intelligent	ADJ
fcis-30156	1	6	systems	system	NOUN
fcis-30156	1	7	issn	issn	VERB
fcis-30156	1	8	:	:	PUNCT
fcis-30156	1	9	2832	2832	NUM
fcis-30156	1	10	-	-	SYM
fcis-30156	1	11	6024	6024	NUM
fcis-30156	1	12	|	|	NOUN
fcis-30156	1	13	vol	vol	NOUN
fcis-30156	1	14	.	.	PROPN
fcis-30156	2	1	11	11	NUM
fcis-30156	2	2	,	,	PUNCT
fcis-30156	2	3	no	no	INTJ
fcis-30156	2	4	.	.	NOUN
fcis-30156	2	5	3	3	NUM
fcis-30156	2	6	,	,	PUNCT
fcis-30156	2	7	2025	2025	NUM
fcis-30156	2	8	108	108	NUM
fcis-30156	2	9	abnormal	abnormal	ADJ
fcis-30156	2	10	behavior	behavior	NOUN
fcis-30156	2	11	recognition	recognition	NOUN
fcis-30156	2	12	method	method	NOUN
fcis-30156	2	13	based	base	VERB
fcis-30156	2	14	on	on	ADP
fcis-30156	2	15	fine‐	fine‐	NUM
fcis-30156	2	16	grained	grain	VERB
fcis-30156	2	17	human	human	ADJ
fcis-30156	2	18	dynamic	dynamic	ADJ
fcis-30156	2	19	skeleton	skeleton	NOUN
fcis-30156	2	20	features	feature	VERB
fcis-30156	2	21	chenyue	chenyue	PROPN
fcis-30156	2	22	xu	xu	PROPN
fcis-30156	2	23	,	,	PUNCT
fcis-30156	2	24	rong	rong	PROPN
fcis-30156	2	25	wang	wang	PROPN
fcis-30156	2	26	*	*	PUNCT
fcis-30156	2	27	department	department	PROPN
fcis-30156	2	28	of	of	ADP
fcis-30156	2	29	information	information	NOUN
fcis-30156	2	30	and	and	CCONJ
fcis-30156	2	31	network	network	NOUN
fcis-30156	2	32	security	security	NOUN
fcis-30156	2	33	,	,	PUNCT
fcis-30156	2	34	people	people	NOUN
fcis-30156	2	35	's	's	PART
fcis-30156	2	36	public	public	ADJ
fcis-30156	2	37	security	security	NOUN
fcis-30156	2	38	university	university	PROPN
fcis-30156	2	39	of	of	ADP
fcis-30156	2	40	china	china	PROPN
fcis-30156	2	41	,	,	PUNCT
fcis-30156	2	42	beijing	beijing	PROPN
fcis-30156	2	43	,	,	PUNCT
fcis-30156	2	44	china	china	PROPN
fcis-30156	2	45	*	*	PUNCT
fcis-30156	2	46	corresponding	correspond	VERB
fcis-30156	2	47	author	author	NOUN
fcis-30156	2	48	:	:	PUNCT
fcis-30156	3	1	rong	rong	PROPN
fcis-30156	3	2	wang	wang	PROPN
fcis-30156	3	3	(	(	PUNCT
fcis-30156	3	4	email	email	NOUN
fcis-30156	3	5	:	:	PUNCT
fcis-30156	3	6	dbdxwangrong@163.com	dbdxwangrong@163.com	NOUN
fcis-30156	3	7	)	)	PUNCT
fcis-30156	3	8	abstract	abstract	NOUN
fcis-30156	3	9	:	:	PUNCT
fcis-30156	3	10	to	to	PART
fcis-30156	3	11	address	address	VERB
fcis-30156	3	12	the	the	DET
fcis-30156	3	13	insufficiency	insufficiency	NOUN
fcis-30156	3	14	of	of	ADP
fcis-30156	3	15	feature	feature	NOUN
fcis-30156	3	16	extraction	extraction	NOUN
fcis-30156	3	17	in	in	ADP
fcis-30156	3	18	existing	exist	VERB
fcis-30156	3	19	video	video	NOUN
fcis-30156	3	20	anomaly	anomaly	NOUN
fcis-30156	3	21	recognition	recognition	NOUN
fcis-30156	3	22	methods	method	NOUN
fcis-30156	3	23	,	,	PUNCT
fcis-30156	3	24	this	this	DET
fcis-30156	3	25	paper	paper	NOUN
fcis-30156	3	26	proposes	propose	VERB
fcis-30156	3	27	an	an	DET
fcis-30156	3	28	improved	improved	ADJ
fcis-30156	3	29	graph	graph	NOUN
fcis-30156	3	30	-	-	PUNCT
fcis-30156	3	31	embedded	embed	VERB
fcis-30156	3	32	pose	pose	NOUN
fcis-30156	3	33	clustering	clustering	NOUN
fcis-30156	3	34	-	-	PUNCT
fcis-30156	3	35	based	base	VERB
fcis-30156	3	36	abnormal	abnormal	ADJ
fcis-30156	3	37	behavior	behavior	NOUN
fcis-30156	3	38	recognition	recognition	NOUN
fcis-30156	3	39	method	method	NOUN
fcis-30156	3	40	.	.	PUNCT
fcis-30156	4	1	by	by	ADP
fcis-30156	4	2	employing	employ	VERB
fcis-30156	4	3	fine	fine	ADV
fcis-30156	4	4	-	-	PUNCT
fcis-30156	4	5	grained	grain	VERB
fcis-30156	4	6	feature	feature	NOUN
fcis-30156	4	7	extraction	extraction	NOUN
fcis-30156	4	8	,	,	PUNCT
fcis-30156	4	9	it	it	PRON
fcis-30156	4	10	enhances	enhance	VERB
fcis-30156	4	11	information	information	NOUN
fcis-30156	4	12	representation	representation	NOUN
fcis-30156	4	13	capabilities	capability	NOUN
fcis-30156	4	14	and	and	CCONJ
fcis-30156	4	15	improves	improve	VERB
fcis-30156	4	16	algorithm	algorithm	NOUN
fcis-30156	4	17	robustness	robustness	NOUN
fcis-30156	4	18	.	.	PUNCT
fcis-30156	5	1	the	the	DET
fcis-30156	5	2	framework	framework	NOUN
fcis-30156	5	3	is	be	AUX
fcis-30156	5	4	structured	structure	VERB
fcis-30156	5	5	as	as	SCONJ
fcis-30156	5	6	follows	follow	VERB
fcis-30156	5	7	:	:	PUNCT
fcis-30156	5	8	first	first	ADJ
fcis-30156	5	9	,	,	PUNCT
fcis-30156	5	10	spatial	spatial	ADJ
fcis-30156	5	11	and	and	CCONJ
fcis-30156	5	12	channel	channel	NOUN
fcis-30156	5	13	reconstruction	reconstruction	NOUN
fcis-30156	5	14	convolutions	convolution	NOUN
fcis-30156	5	15	are	be	AUX
fcis-30156	5	16	integrated	integrate	VERB
fcis-30156	5	17	into	into	ADP
fcis-30156	5	18	a	a	DET
fcis-30156	5	19	deep	deep	ADJ
fcis-30156	5	20	residual	residual	ADJ
fcis-30156	5	21	network	network	NOUN
fcis-30156	5	22	backbone	backbone	NOUN
fcis-30156	5	23	,	,	PUNCT
fcis-30156	5	24	effectively	effectively	ADV
fcis-30156	5	25	eliminating	eliminate	VERB
fcis-30156	5	26	spatial	spatial	ADJ
fcis-30156	5	27	-	-	PUNCT
fcis-30156	5	28	channel	channel	NOUN
fcis-30156	5	29	redundancy	redundancy	NOUN
fcis-30156	5	30	in	in	ADP
fcis-30156	5	31	extracted	extract	VERB
fcis-30156	5	32	features	feature	NOUN
fcis-30156	5	33	while	while	SCONJ
fcis-30156	5	34	reducing	reduce	VERB
fcis-30156	5	35	computational	computational	ADJ
fcis-30156	5	36	complexity	complexity	NOUN
fcis-30156	5	37	,	,	PUNCT
fcis-30156	5	38	thereby	thereby	ADV
fcis-30156	5	39	enhancing	enhance	VERB
fcis-30156	5	40	operational	operational	ADJ
fcis-30156	5	41	efficiency	efficiency	NOUN
fcis-30156	5	42	.	.	PUNCT
fcis-30156	6	1	second	second	ADJ
fcis-30156	6	2	,	,	PUNCT
fcis-30156	6	3	a	a	DET
fcis-30156	6	4	hierarchical	hierarchical	ADJ
fcis-30156	6	5	decomposed	decomposed	NOUN
fcis-30156	6	6	graph	graph	NOUN
fcis-30156	6	7	convolutional	convolutional	ADJ
fcis-30156	6	8	network	network	NOUN
fcis-30156	6	9	(	(	PUNCT
fcis-30156	6	10	modified	modify	VERB
fcis-30156	6	11	from	from	ADP
fcis-30156	6	12	spatiotemporal	spatiotemporal	ADJ
fcis-30156	6	13	graph	graph	NOUN
fcis-30156	6	14	convolutional	convolutional	ADJ
fcis-30156	6	15	networks	network	NOUN
fcis-30156	6	16	)	)	PUNCT
fcis-30156	6	17	is	be	AUX
fcis-30156	6	18	implemented	implement	VERB
fcis-30156	6	19	for	for	ADP
fcis-30156	6	20	dynamic	dynamic	ADJ
fcis-30156	6	21	skeleton	skeleton	NOUN
fcis-30156	6	22	feature	feature	NOUN
fcis-30156	6	23	extraction	extraction	NOUN
fcis-30156	6	24	,	,	PUNCT
fcis-30156	6	25	coupled	couple	VERB
fcis-30156	6	26	with	with	ADP
fcis-30156	6	27	an	an	DET
fcis-30156	6	28	attentionguided	attentionguide	VERB
fcis-30156	6	29	hierarchical	hierarchical	ADJ
fcis-30156	6	30	aggregation	aggregation	NOUN
fcis-30156	6	31	module	module	NOUN
fcis-30156	6	32	for	for	ADP
fcis-30156	6	33	multi	multi	ADJ
fcis-30156	6	34	-	-	ADJ
fcis-30156	6	35	level	level	ADJ
fcis-30156	6	36	feature	feature	NOUN
fcis-30156	6	37	fusion	fusion	NOUN
fcis-30156	6	38	.	.	PUNCT
fcis-30156	7	1	evaluations	evaluation	NOUN
fcis-30156	7	2	on	on	ADP
fcis-30156	7	3	shanghaitech	shanghaitech	NOUN
fcis-30156	7	4	and	and	CCONJ
fcis-30156	7	5	ntu	ntu	PROPN
fcis-30156	7	6	-	-	PUNCT
fcis-30156	7	7	rgb+d	rgb+d	PROPN
fcis-30156	7	8	datasets	dataset	NOUN
fcis-30156	7	9	demonstrate	demonstrate	VERB
fcis-30156	7	10	detection	detection	NOUN
fcis-30156	7	11	accuracies	accuracy	NOUN
fcis-30156	7	12	of	of	ADP
fcis-30156	7	13	76.4	76.4	NUM
fcis-30156	7	14	%	%	NOUN
fcis-30156	7	15	and	and	CCONJ
fcis-30156	7	16	74.6	74.6	NUM
fcis-30156	7	17	%	%	NOUN
fcis-30156	7	18	respectively	respectively	ADV
fcis-30156	7	19	,	,	PUNCT
fcis-30156	7	20	validating	validate	VERB
fcis-30156	7	21	the	the	DET
fcis-30156	7	22	method	method	NOUN
fcis-30156	7	23	's	's	PART
fcis-30156	7	24	effectiveness	effectiveness	NOUN
fcis-30156	7	25	.	.	PUNCT
fcis-30156	8	1	keywords	keyword	NOUN
fcis-30156	8	2	:	:	PUNCT
fcis-30156	8	3	abnormal	abnormal	ADJ
fcis-30156	8	4	behavior	behavior	NOUN
fcis-30156	8	5	recognition	recognition	NOUN
fcis-30156	8	6	;	;	PUNCT
fcis-30156	8	7	dynamic	dynamic	ADJ
fcis-30156	8	8	skeletal	skeletal	ADJ
fcis-30156	8	9	features	feature	NOUN
fcis-30156	8	10	;	;	PUNCT
fcis-30156	8	11	fine	fine	ADJ
fcis-30156	8	12	-	-	PUNCT
fcis-30156	8	13	grained	grain	VERB
fcis-30156	8	14	feature	feature	NOUN
fcis-30156	8	15	extraction	extraction	NOUN
fcis-30156	8	16	.	.	PUNCT
fcis-30156	9	1	1	1	X
fcis-30156	9	2	.	.	X
fcis-30156	9	3	introduction	introduction	NOUN
fcis-30156	9	4	abnormal	abnormal	ADJ
fcis-30156	9	5	behavior	behavior	NOUN
fcis-30156	9	6	recognition	recognition	NOUN
fcis-30156	9	7	[	[	X
fcis-30156	9	8	1	1	NUM
fcis-30156	9	9	-	-	SYM
fcis-30156	9	10	4	4	NUM
fcis-30156	9	11	]	]	PUNCT
fcis-30156	9	12	,	,	PUNCT
fcis-30156	9	13	a	a	DET
fcis-30156	9	14	prominent	prominent	ADJ
fcis-30156	9	15	research	research	NOUN
fcis-30156	9	16	direction	direction	NOUN
fcis-30156	9	17	in	in	ADP
fcis-30156	9	18	computer	computer	NOUN
fcis-30156	9	19	vision	vision	NOUN
fcis-30156	9	20	,	,	PUNCT
fcis-30156	9	21	involves	involve	VERB
fcis-30156	9	22	identifying	identify	VERB
fcis-30156	9	23	key	key	ADJ
fcis-30156	9	24	frames	frame	NOUN
fcis-30156	9	25	in	in	ADP
fcis-30156	9	26	videos	video	NOUN
fcis-30156	9	27	and	and	CCONJ
fcis-30156	9	28	determining	determine	VERB
fcis-30156	9	29	the	the	DET
fcis-30156	9	30	presence	presence	NOUN
fcis-30156	9	31	of	of	ADP
fcis-30156	9	32	anomalous	anomalous	ADJ
fcis-30156	9	33	activities	activity	NOUN
fcis-30156	9	34	.	.	PUNCT
fcis-30156	10	1	practical	practical	ADJ
fcis-30156	10	2	challenges	challenge	NOUN
fcis-30156	10	3	arise	arise	VERB
fcis-30156	10	4	from	from	ADP
fcis-30156	10	5	massive	massive	ADJ
fcis-30156	10	6	video	video	NOUN
fcis-30156	10	7	volumes	volume	NOUN
fcis-30156	10	8	and	and	CCONJ
fcis-30156	10	9	diverse	diverse	ADJ
fcis-30156	10	10	anomaly	anomaly	NOUN
fcis-30156	10	11	categories	category	NOUN
fcis-30156	10	12	,	,	PUNCT
fcis-30156	10	13	while	while	SCONJ
fcis-30156	10	14	existing	existing	ADJ
fcis-30156	10	15	frame	frame	NOUN
fcis-30156	10	16	-	-	PUNCT
fcis-30156	10	17	level	level	NOUN
fcis-30156	10	18	annotated	annotate	VERB
fcis-30156	10	19	datasets	dataset	NOUN
fcis-30156	10	20	remain	remain	VERB
fcis-30156	10	21	limited	limited	ADJ
fcis-30156	10	22	in	in	ADP
fcis-30156	10	23	scale	scale	NOUN
fcis-30156	10	24	—	—	PUNCT
fcis-30156	10	25	insufficient	insufficient	ADJ
fcis-30156	10	26	for	for	ADP
fcis-30156	10	27	training	train	VERB
fcis-30156	10	28	supervised	supervise	VERB
fcis-30156	10	29	video	video	NOUN
fcis-30156	10	30	anomaly	anomaly	NOUN
fcis-30156	10	31	recognition	recognition	NOUN
fcis-30156	10	32	models	model	NOUN
fcis-30156	10	33	.	.	PUNCT
fcis-30156	11	1	sato	sato	PROPN
fcis-30156	11	2	et	et	PROPN
fcis-30156	11	3	al	al	PROPN
fcis-30156	11	4	.	.	PUNCT
fcis-30156	12	1	[	[	X
fcis-30156	12	2	5	5	NUM
fcis-30156	12	3	]	]	X
fcis-30156	12	4	leveraged	leveraged	ADJ
fcis-30156	12	5	pre	pre	VERB
fcis-30156	12	6	-	-	VERB
fcis-30156	12	7	trained	train	VERB
fcis-30156	12	8	skeletal	skeletal	ADJ
fcis-30156	12	9	feature	feature	NOUN
fcis-30156	12	10	extractors	extractor	NOUN
fcis-30156	12	11	,	,	PUNCT
fcis-30156	12	12	aligning	align	VERB
fcis-30156	12	13	skeleton	skeleton	NOUN
fcis-30156	12	14	features	feature	NOUN
fcis-30156	12	15	with	with	ADP
fcis-30156	12	16	user	user	NOUN
fcis-30156	12	17	prompts	prompt	NOUN
fcis-30156	12	18	through	through	ADP
fcis-30156	12	19	common	common	ADJ
fcis-30156	12	20	spatial	spatial	ADJ
fcis-30156	12	21	domain	domain	NOUN
fcis-30156	12	22	alignment	alignment	NOUN
fcis-30156	12	23	to	to	PART
fcis-30156	12	24	establish	establish	VERB
fcis-30156	12	25	anomaly	anomaly	NOUN
fcis-30156	12	26	scores	score	NOUN
fcis-30156	12	27	.	.	PUNCT
fcis-30156	13	1	cho	cho	PROPN
fcis-30156	13	2	et	et	PROPN
fcis-30156	13	3	al	al	PROPN
fcis-30156	13	4	.	.	PUNCT
fcis-30156	14	1	[	[	X
fcis-30156	14	2	6	6	NUM
fcis-30156	14	3	]	]	PUNCT
fcis-30156	14	4	proposed	propose	VERB
fcis-30156	14	5	an	an	DET
fcis-30156	14	6	implicit	implicit	ADJ
fcis-30156	14	7	dual	dual	ADJ
fcis-30156	14	8	-	-	PUNCT
fcis-30156	14	9	path	path	NOUN
fcis-30156	14	10	autoencoder	autoencoder	NOUN
fcis-30156	14	11	to	to	PART
fcis-30156	14	12	enhance	enhance	VERB
fcis-30156	14	13	feature	feature	NOUN
fcis-30156	14	14	informativeness	informativeness	NOUN
fcis-30156	14	15	through	through	ADP
fcis-30156	14	16	differentiated	differentiated	ADJ
fcis-30156	14	17	learning	learning	NOUN
fcis-30156	14	18	strategies	strategy	NOUN
fcis-30156	14	19	.	.	PUNCT
fcis-30156	15	1	however	however	ADV
fcis-30156	15	2	,	,	PUNCT
fcis-30156	15	3	unsupervised	unsupervised	ADJ
fcis-30156	15	4	anomaly	anomaly	NOUN
fcis-30156	15	5	detection	detection	NOUN
fcis-30156	15	6	methods	method	NOUN
fcis-30156	15	7	[	[	X
fcis-30156	15	8	7,8	7,8	NUM
fcis-30156	15	9	]	]	PUNCT
fcis-30156	15	10	often	often	ADV
fcis-30156	15	11	suffer	suffer	VERB
fcis-30156	15	12	from	from	ADP
fcis-30156	15	13	weak	weak	ADJ
fcis-30156	15	14	feature	feature	NOUN
fcis-30156	15	15	expressiveness	expressiveness	NOUN
fcis-30156	15	16	and	and	CCONJ
fcis-30156	15	17	redundant	redundant	ADJ
fcis-30156	15	18	representations	representation	NOUN
fcis-30156	15	19	.	.	PUNCT
fcis-30156	16	1	to	to	PART
fcis-30156	16	2	address	address	VERB
fcis-30156	16	3	these	these	DET
fcis-30156	16	4	limitations	limitation	NOUN
fcis-30156	16	5	,	,	PUNCT
fcis-30156	16	6	we	we	PRON
fcis-30156	16	7	enhance	enhance	VERB
fcis-30156	16	8	the	the	DET
fcis-30156	16	9	graph	graph	NOUN
fcis-30156	16	10	-	-	PUNCT
fcis-30156	16	11	embedded	embed	VERB
fcis-30156	16	12	pose	pose	NOUN
fcis-30156	16	13	clustering	cluster	VERB
fcis-30156	16	14	(	(	PUNCT
fcis-30156	16	15	gepc	gepc	X
fcis-30156	16	16	)	)	PUNCT
fcis-30156	16	17	framework	framework	NOUN
fcis-30156	16	18	by	by	ADP
fcis-30156	16	19	introducing	introduce	VERB
fcis-30156	16	20	dynamic	dynamic	ADJ
fcis-30156	16	21	human	human	ADJ
fcis-30156	16	22	skeleton	skeleton	NOUN
fcis-30156	16	23	sequences	sequence	NOUN
fcis-30156	16	24	as	as	ADP
fcis-30156	16	25	model	model	NOUN
fcis-30156	16	26	inputs	input	NOUN
fcis-30156	16	27	.	.	PUNCT
fcis-30156	17	1	our	our	PRON
fcis-30156	17	2	proposed	propose	VERB
fcis-30156	17	3	solution	solution	NOUN
fcis-30156	17	4	combines	combine	VERB
fcis-30156	17	5	:	:	PUNCT
fcis-30156	17	6	2	2	X
fcis-30156	17	7	.	.	X
fcis-30156	17	8	organization	organization	NOUN
fcis-30156	17	9	of	of	ADP
fcis-30156	17	10	the	the	DET
fcis-30156	17	11	text	text	NOUN
fcis-30156	17	12	2.1	2.1	NUM
fcis-30156	17	13	.	.	PUNCT
fcis-30156	18	1	model	model	NOUN
fcis-30156	18	2	framework	framework	NOUN
fcis-30156	18	3	building	build	VERB
fcis-30156	18	4	upon	upon	SCONJ
fcis-30156	18	5	the	the	DET
fcis-30156	18	6	gepc	gepc	X
fcis-30156	18	7	framework	framework	NOUN
fcis-30156	18	8	,	,	PUNCT
fcis-30156	18	9	we	we	PRON
fcis-30156	18	10	have	have	AUX
fcis-30156	18	11	optimized	optimize	VERB
fcis-30156	18	12	both	both	CCONJ
fcis-30156	18	13	the	the	DET
fcis-30156	18	14	feature	feature	NOUN
fcis-30156	18	15	extraction	extraction	NOUN
fcis-30156	18	16	backbone	backbone	NOUN
fcis-30156	18	17	network	network	NOUN
fcis-30156	18	18	and	and	CCONJ
fcis-30156	18	19	the	the	DET
fcis-30156	18	20	spatiotemporal	spatiotemporal	ADJ
fcis-30156	18	21	graph	graph	NOUN
fcis-30156	18	22	convolution	convolution	NOUN
fcis-30156	18	23	module	module	NOUN
fcis-30156	18	24	.	.	PUNCT
fcis-30156	19	1	the	the	DET
fcis-30156	19	2	enhanced	enhanced	ADJ
fcis-30156	19	3	model	model	NOUN
fcis-30156	19	4	demonstrates	demonstrate	VERB
fcis-30156	19	5	superior	superior	ADJ
fcis-30156	19	6	capability	capability	NOUN
fcis-30156	19	7	in	in	ADP
fcis-30156	19	8	extracting	extract	VERB
fcis-30156	19	9	comprehensive	comprehensive	ADJ
fcis-30156	19	10	global	global	ADJ
fcis-30156	19	11	and	and	CCONJ
fcis-30156	19	12	spatio	spatio	PROPN
fcis-30156	19	13	-	-	PUNCT
fcis-30156	19	14	temporal	temporal	ADJ
fcis-30156	19	15	features	feature	NOUN
fcis-30156	19	16	from	from	ADP
fcis-30156	19	17	human	human	ADJ
fcis-30156	19	18	pose	pose	NOUN
fcis-30156	19	19	graph	graph	NOUN
fcis-30156	19	20	sequences	sequence	NOUN
fcis-30156	19	21	,	,	PUNCT
fcis-30156	19	22	significantly	significantly	ADV
fcis-30156	19	23	improving	improve	VERB
fcis-30156	19	24	abnormal	abnormal	ADJ
fcis-30156	19	25	behavior	behavior	NOUN
fcis-30156	19	26	recognition	recognition	NOUN
fcis-30156	19	27	accuracy	accuracy	NOUN
fcis-30156	19	28	.	.	PUNCT
fcis-30156	20	1	the	the	DET
fcis-30156	20	2	improved	improved	ADJ
fcis-30156	20	3	model	model	NOUN
fcis-30156	20	4	can	can	AUX
fcis-30156	20	5	be	be	AUX
fcis-30156	20	6	divided	divide	VERB
fcis-30156	20	7	into	into	ADP
fcis-30156	20	8	three	three	NUM
fcis-30156	20	9	modules	module	NOUN
fcis-30156	20	10	:	:	PUNCT
fcis-30156	20	11	the	the	DET
fcis-30156	20	12	pose	pose	NOUN
fcis-30156	20	13	estimation	estimation	NOUN
fcis-30156	20	14	feature	feature	NOUN
fcis-30156	20	15	enhancement	enhancement	NOUN
fcis-30156	20	16	module	module	NOUN
fcis-30156	20	17	,	,	PUNCT
fcis-30156	20	18	the	the	DET
fcis-30156	20	19	encoding	encoding	NOUN
fcis-30156	20	20	module	module	NOUN
fcis-30156	20	21	,	,	PUNCT
fcis-30156	20	22	and	and	CCONJ
fcis-30156	20	23	the	the	DET
fcis-30156	20	24	clustering	cluster	VERB
fcis-30156	20	25	module	module	NOUN
fcis-30156	20	26	.	.	PUNCT
fcis-30156	21	1	the	the	DET
fcis-30156	21	2	overall	overall	ADJ
fcis-30156	21	3	structure	structure	NOUN
fcis-30156	21	4	is	be	AUX
fcis-30156	21	5	shown	show	VERB
fcis-30156	21	6	in	in	ADP
fcis-30156	21	7	figure	figure	NOUN
fcis-30156	21	8	1	1	NUM
fcis-30156	21	9	.	.	PUNCT
fcis-30156	22	1	in	in	ADP
fcis-30156	22	2	the	the	DET
fcis-30156	22	3	pose	pose	NOUN
fcis-30156	22	4	estimation	estimation	NOUN
fcis-30156	22	5	module	module	NOUN
fcis-30156	22	6	,	,	PUNCT
fcis-30156	22	7	the	the	DET
fcis-30156	22	8	model	model	NOUN
fcis-30156	22	9	employs	employ	VERB
fcis-30156	22	10	the	the	DET
fcis-30156	22	11	alphapose	alphapose	ADJ
fcis-30156	22	12	module	module	NOUN
fcis-30156	22	13	to	to	PART
fcis-30156	22	14	extract	extract	VERB
fcis-30156	22	15	dynamic	dynamic	ADJ
fcis-30156	22	16	human	human	ADJ
fcis-30156	22	17	skeleton	skeleton	NOUN
fcis-30156	22	18	graphs	graph	NOUN
fcis-30156	22	19	from	from	ADP
fcis-30156	22	20	video	video	NOUN
fcis-30156	22	21	frames	frame	NOUN
fcis-30156	22	22	.	.	PUNCT
fcis-30156	23	1	to	to	PART
fcis-30156	23	2	extend	extend	VERB
fcis-30156	23	3	the	the	DET
fcis-30156	23	4	human	human	ADJ
fcis-30156	23	5	skeleton	skeleton	NOUN
fcis-30156	23	6	graphs	graph	NOUN
fcis-30156	23	7	from	from	ADP
fcis-30156	23	8	the	the	DET
fcis-30156	23	9	spatial	spatial	ADJ
fcis-30156	23	10	dimension	dimension	NOUN
fcis-30156	23	11	to	to	ADP
fcis-30156	23	12	the	the	DET
fcis-30156	23	13	temporal	temporal	ADJ
fcis-30156	23	14	dimension	dimension	NOUN
fcis-30156	23	15	,	,	PUNCT
fcis-30156	23	16	spatio	spatio	ADJ
fcis-30156	23	17	-	-	PUNCT
fcis-30156	23	18	temporal	temporal	ADJ
fcis-30156	23	19	feature	feature	NOUN
fcis-30156	23	20	vectors	vector	NOUN
fcis-30156	23	21	of	of	ADP
fcis-30156	23	22	the	the	DET
fcis-30156	23	23	dynamic	dynamic	ADJ
fcis-30156	23	24	skeleton	skeleton	NOUN
fcis-30156	23	25	graphs	graph	NOUN
fcis-30156	23	26	are	be	AUX
fcis-30156	23	27	extracted	extract	VERB
fcis-30156	23	28	.	.	PUNCT
fcis-30156	24	1	in	in	ADP
fcis-30156	24	2	the	the	DET
fcis-30156	24	3	encoding	encoding	NOUN
fcis-30156	24	4	module	module	NOUN
fcis-30156	24	5	,	,	PUNCT
fcis-30156	24	6	an	an	DET
fcis-30156	24	7	improved	improved	ADJ
fcis-30156	24	8	deep	deep	ADJ
fcis-30156	24	9	temporal	temporal	ADJ
fcis-30156	24	10	graph	graph	NOUN
fcis-30156	24	11	autoencoder	autoencoder	NOUN
fcis-30156	24	12	structure	structure	NOUN
fcis-30156	24	13	is	be	AUX
fcis-30156	24	14	proposed	propose	VERB
fcis-30156	24	15	to	to	PART
fcis-30156	24	16	embed	embed	VERB
fcis-30156	24	17	the	the	DET
fcis-30156	24	18	dynamic	dynamic	ADJ
fcis-30156	24	19	human	human	ADJ
fcis-30156	24	20	skeleton	skeleton	NOUN
fcis-30156	24	21	graphs	graph	NOUN
fcis-30156	24	22	.	.	PUNCT
fcis-30156	25	1	the	the	DET
fcis-30156	25	2	encoder	encoder	NOUN
fcis-30156	25	3	is	be	AUX
fcis-30156	25	4	designed	design	VERB
fcis-30156	25	5	based	base	VERB
fcis-30156	25	6	on	on	ADP
fcis-30156	25	7	spatio	spatio	PROPN
fcis-30156	25	8	-	-	PUNCT
fcis-30156	25	9	temporal	temporal	ADJ
fcis-30156	25	10	graph	graph	NOUN
fcis-30156	25	11	convolution	convolution	NOUN
fcis-30156	25	12	,	,	PUNCT
fcis-30156	25	13	and	and	CCONJ
fcis-30156	25	14	the	the	DET
fcis-30156	25	15	original	original	ADJ
fcis-30156	25	16	model	model	NOUN
fcis-30156	25	17	is	be	AUX
fcis-30156	25	18	optimized	optimize	VERB
fcis-30156	25	19	using	use	VERB
fcis-30156	25	20	an	an	DET
fcis-30156	25	21	attention	attention	NOUN
fcis-30156	25	22	-	-	PUNCT
fcis-30156	25	23	guided	guide	VERB
fcis-30156	25	24	hierarchical	hierarchical	ADJ
fcis-30156	25	25	method	method	NOUN
fcis-30156	25	26	for	for	ADP
fcis-30156	25	27	extracting	extract	VERB
fcis-30156	25	28	features	feature	NOUN
fcis-30156	25	29	from	from	ADP
fcis-30156	25	30	dynamic	dynamic	ADJ
fcis-30156	25	31	skeleton	skeleton	NOUN
fcis-30156	25	32	graphs	graph	NOUN
fcis-30156	25	33	,	,	PUNCT
fcis-30156	25	34	constructing	construct	VERB
fcis-30156	25	35	a	a	DET
fcis-30156	25	36	hierarchically	hierarchically	ADV
fcis-30156	25	37	decomposed	decompose	VERB
fcis-30156	25	38	spatio	spatio	ADJ
fcis-30156	25	39	-	-	PUNCT
fcis-30156	25	40	temporal	temporal	ADJ
fcis-30156	25	41	graph	graph	NOUN
fcis-30156	25	42	convolutional	convolutional	ADJ
fcis-30156	25	43	autoencoder	autoencoder	NOUN
fcis-30156	25	44	.	.	PUNCT
fcis-30156	26	1	in	in	ADP
fcis-30156	26	2	the	the	DET
fcis-30156	26	3	clustering	clustering	NOUN
fcis-30156	26	4	module	module	NOUN
fcis-30156	26	5	,	,	PUNCT
fcis-30156	26	6	the	the	DET
fcis-30156	26	7	training	training	NOUN
fcis-30156	26	8	set	set	NOUN
fcis-30156	26	9	is	be	AUX
fcis-30156	26	10	first	first	ADV
fcis-30156	26	11	jointly	jointly	ADV
fcis-30156	26	12	embedded	embed	VERB
fcis-30156	26	13	into	into	ADP
fcis-30156	26	14	a	a	DET
fcis-30156	26	15	latent	latent	NOUN
fcis-30156	26	16	space	space	NOUN
fcis-30156	26	17	and	and	CCONJ
fcis-30156	26	18	subjected	subject	VERB
fcis-30156	26	19	to	to	ADP
fcis-30156	26	20	clustering	cluster	VERB
fcis-30156	26	21	operations	operation	NOUN
fcis-30156	26	22	to	to	PART
fcis-30156	26	23	build	build	VERB
fcis-30156	26	24	an	an	DET
fcis-30156	26	25	underlying	underlying	ADJ
fcis-30156	26	26	action	action	NOUN
fcis-30156	26	27	dictionary	dictionary	NOUN
fcis-30156	26	28	.	.	PUNCT
fcis-30156	27	1	then	then	ADV
fcis-30156	27	2	,	,	PUNCT
fcis-30156	27	3	each	each	DET
fcis-30156	27	4	sample	sample	NOUN
fcis-30156	27	5	is	be	AUX
fcis-30156	27	6	represented	represent	VERB
fcis-30156	27	7	by	by	ADP
fcis-30156	27	8	a	a	DET
fcis-30156	27	9	probability	probability	NOUN
fcis-30156	27	10	distribution	distribution	NOUN
fcis-30156	27	11	over	over	ADP
fcis-30156	27	12	the	the	DET
fcis-30156	27	13	clustered	clustered	ADJ
fcis-30156	27	14	underlying	underlying	ADJ
fcis-30156	27	15	actions	action	NOUN
fcis-30156	27	16	.	.	PUNCT
fcis-30156	28	1	the	the	DET
fcis-30156	28	2	clustering	cluster	VERB
fcis-30156	28	3	module	module	NOUN
fcis-30156	28	4	consists	consist	VERB
fcis-30156	28	5	of	of	ADP
fcis-30156	28	6	three	three	NUM
fcis-30156	28	7	parts	part	NOUN
fcis-30156	28	8	:	:	PUNCT
fcis-30156	28	9	an	an	DET
fcis-30156	28	10	encoder	encoder	NOUN
fcis-30156	28	11	,	,	PUNCT
fcis-30156	28	12	a	a	DET
fcis-30156	28	13	decoder	decoder	NOUN
fcis-30156	28	14	,	,	PUNCT
fcis-30156	28	15	and	and	CCONJ
fcis-30156	28	16	a	a	DET
fcis-30156	28	17	soft	soft	ADJ
fcis-30156	28	18	clustering	clustering	ADJ
fcis-30156	28	19	layer	layer	NOUN
fcis-30156	28	20	.	.	PUNCT
fcis-30156	29	1	the	the	DET
fcis-30156	29	2	encoder	encoder	NOUN
fcis-30156	29	3	preserves	preserve	VERB
fcis-30156	29	4	the	the	DET
fcis-30156	29	5	input	input	NOUN
fcis-30156	29	6	graph	graph	NOUN
fcis-30156	29	7	structure	structure	NOUN
fcis-30156	29	8	and	and	CCONJ
fcis-30156	29	9	compresses	compress	VERB
fcis-30156	29	10	the	the	DET
fcis-30156	29	11	dynamic	dynamic	ADJ
fcis-30156	29	12	skeleton	skeleton	NOUN
fcis-30156	29	13	graph	graph	NOUN
fcis-30156	29	14	sequence	sequence	NOUN
fcis-30156	29	15	into	into	ADP
fcis-30156	29	16	a	a	DET
fcis-30156	29	17	latent	latent	NOUN
fcis-30156	29	18	vector	vector	NOUN
fcis-30156	29	19	through	through	ADP
fcis-30156	29	20	convolution	convolution	NOUN
fcis-30156	29	21	.	.	PUNCT
fcis-30156	30	1	the	the	DET
fcis-30156	30	2	decoder	decoder	NOUN
fcis-30156	30	3	then	then	ADV
fcis-30156	30	4	uses	use	VERB
fcis-30156	30	5	temporal	temporal	ADJ
fcis-30156	30	6	upsampling	upsample	VERB
fcis-30156	30	7	layers	layer	NOUN
fcis-30156	30	8	and	and	CCONJ
fcis-30156	30	9	additional	additional	ADJ
fcis-30156	30	10	graph	graph	NOUN
fcis-30156	30	11	convolution	convolution	NOUN
fcis-30156	30	12	blocks	block	NOUN
fcis-30156	30	13	to	to	PART
fcis-30156	30	14	gradually	gradually	ADV
fcis-30156	30	15	restore	restore	VERB
fcis-30156	30	16	the	the	DET
fcis-30156	30	17	original	original	ADJ
fcis-30156	30	18	channel	channel	NOUN
fcis-30156	30	19	count	count	NOUN
fcis-30156	30	20	and	and	CCONJ
fcis-30156	30	21	temporal	temporal	ADJ
fcis-30156	30	22	dimensions	dimension	NOUN
fcis-30156	30	23	.	.	PUNCT
fcis-30156	31	1	finally	finally	ADV
fcis-30156	31	2	,	,	PUNCT
fcis-30156	31	3	the	the	DET
fcis-30156	31	4	initial	initial	ADJ
fcis-30156	31	5	reconstruction	reconstruction	NOUN
fcis-30156	31	6	-	-	PUNCT
fcis-30156	31	7	based	base	VERB
fcis-30156	31	8	embeddings	embedding	NOUN
fcis-30156	31	9	are	be	AUX
fcis-30156	31	10	fine	fine	ADV
fcis-30156	31	11	-	-	PUNCT
fcis-30156	31	12	tuned	tune	VERB
fcis-30156	31	13	during	during	ADP
fcis-30156	31	14	the	the	DET
fcis-30156	31	15	clustering	clustering	ADJ
fcis-30156	31	16	optimization	optimization	NOUN
fcis-30156	31	17	stage	stage	NOUN
fcis-30156	31	18	to	to	PART
fcis-30156	31	19	achieve	achieve	VERB
fcis-30156	31	20	the	the	DET
fcis-30156	31	21	final	final	ADJ
fcis-30156	31	22	optimized	optimize	VERB
fcis-30156	31	23	clustering	clustering	NOUN
fcis-30156	31	24	embeddings	embedding	NOUN
fcis-30156	31	25	.	.	PUNCT
fcis-30156	32	1	2.2	2.2	NUM
fcis-30156	32	2	.	.	PUNCT
fcis-30156	33	1	feature	feature	NOUN
fcis-30156	33	2	preprocessing	preprocesse	VERB
fcis-30156	33	3	network	network	NOUN
fcis-30156	33	4	the	the	DET
fcis-30156	33	5	spatial	spatial	ADJ
fcis-30156	33	6	and	and	CCONJ
fcis-30156	33	7	channel	channel	NOUN
fcis-30156	33	8	reconstruction	reconstruction	NOUN
fcis-30156	33	9	convolution	convolution	NOUN
fcis-30156	33	10	(	(	PUNCT
fcis-30156	33	11	scconv	scconv	NOUN
fcis-30156	33	12	)	)	PUNCT
fcis-30156	34	1	[	[	X
fcis-30156	34	2	9	9	NUM
fcis-30156	34	3	]	]	PUNCT
fcis-30156	34	4	is	be	AUX
fcis-30156	34	5	introduced	introduce	VERB
fcis-30156	34	6	into	into	ADP
fcis-30156	34	7	the	the	DET
fcis-30156	34	8	resnet	resnet	ADJ
fcis-30156	34	9	residual	residual	ADJ
fcis-30156	34	10	network	network	NOUN
fcis-30156	34	11	structure	structure	NOUN
fcis-30156	34	12	,	,	PUNCT
fcis-30156	34	13	replacing	replace	VERB
fcis-30156	34	14	the	the	DET
fcis-30156	34	15	original	original	ADJ
fcis-30156	34	16	3×3	3×3	NUM
fcis-30156	34	17	convolutional	convolutional	ADJ
fcis-30156	34	18	network	network	NOUN
fcis-30156	34	19	.	.	PUNCT
fcis-30156	35	1	by	by	ADP
fcis-30156	35	2	utilizing	utilize	VERB
fcis-30156	35	3	two	two	NUM
fcis-30156	35	4	sub	sub	NOUN
fcis-30156	35	5	-	-	NOUN
fcis-30156	35	6	modules	module	NOUN
fcis-30156	35	7	,	,	PUNCT
fcis-30156	35	8	the	the	DET
fcis-30156	35	9	spatial	spatial	ADJ
fcis-30156	35	10	reconstruction	reconstruction	NOUN
fcis-30156	35	11	unit	unit	NOUN
fcis-30156	35	12	(	(	PUNCT
fcis-30156	35	13	sru	sru	PROPN
fcis-30156	35	14	)	)	PUNCT
fcis-30156	35	15	and	and	CCONJ
fcis-30156	35	16	the	the	DET
fcis-30156	35	17	channel	channel	NOUN
fcis-30156	35	18	reconstruction	reconstruction	NOUN
fcis-30156	35	19	unit	unit	NOUN
fcis-30156	35	20	(	(	PUNCT
fcis-30156	35	21	cru	cru	PROPN
fcis-30156	35	22	)	)	PUNCT
fcis-30156	35	23	,	,	PUNCT
fcis-30156	35	24	global	global	ADJ
fcis-30156	35	25	feature	feature	NOUN
fcis-30156	35	26	extraction	extraction	NOUN
fcis-30156	35	27	is	be	AUX
fcis-30156	35	28	performed	perform	VERB
fcis-30156	35	29	,	,	PUNCT
fcis-30156	35	30	effectively	effectively	ADV
fcis-30156	35	31	reducing	reduce	VERB
fcis-30156	35	32	redundancy	redundancy	NOUN
fcis-30156	35	33	in	in	ADP
fcis-30156	35	34	intermediate	intermediate	ADJ
fcis-30156	35	35	feature	feature	NOUN
fcis-30156	35	36	maps	map	NOUN
fcis-30156	35	37	.	.	PUNCT
fcis-30156	36	1	this	this	DET
fcis-30156	36	2	approach	approach	NOUN
fcis-30156	36	3	decreases	decrease	VERB
fcis-30156	36	4	the	the	DET
fcis-30156	36	5	number	number	NOUN
fcis-30156	36	6	of	of	ADP
fcis-30156	36	7	parameters	parameter	NOUN
fcis-30156	36	8	and	and	CCONJ
fcis-30156	36	9	computational	computational	ADJ
fcis-30156	36	10	load	load	NOUN
fcis-30156	36	11	while	while	SCONJ
fcis-30156	36	12	enhancing	enhance	VERB
fcis-30156	36	13	the	the	DET
fcis-30156	36	14	overall	overall	ADJ
fcis-30156	36	15	performance	performance	NOUN
fcis-30156	36	16	of	of	ADP
fcis-30156	36	17	the	the	DET
fcis-30156	36	18	model	model	NOUN
fcis-30156	36	19	.	.	PUNCT
fcis-30156	37	1	in	in	ADP
fcis-30156	37	2	the	the	DET
fcis-30156	37	3	spatial	spatial	ADJ
fcis-30156	37	4	feature	feature	NOUN
fcis-30156	37	5	refinement	refinement	NOUN
fcis-30156	37	6	stage	stage	NOUN
fcis-30156	37	7	,	,	PUNCT
fcis-30156	37	8	the	the	DET
fcis-30156	37	9	sru	sru	PROPN
fcis-30156	37	10	submodule	submodule	PROPN
fcis-30156	37	11	employs	employ	VERB
fcis-30156	37	12	a	a	DET
fcis-30156	37	13	separation	separation	NOUN
fcis-30156	37	14	and	and	CCONJ
fcis-30156	37	15	reconstruction	reconstruction	NOUN
fcis-30156	37	16	joint	joint	ADJ
fcis-30156	37	17	operation	operation	NOUN
fcis-30156	37	18	to	to	PART
fcis-30156	37	19	eliminate	eliminate	VERB
fcis-30156	37	20	spatial	spatial	ADJ
fcis-30156	37	21	redundancy	redundancy	NOUN
fcis-30156	37	22	in	in	ADP
fcis-30156	37	23	the	the	DET
fcis-30156	37	24	features	feature	NOUN
fcis-30156	37	25	.	.	PUNCT
fcis-30156	38	1	in	in	ADP
fcis-30156	38	2	the	the	DET
fcis-30156	38	3	channel	channel	NOUN
fcis-30156	38	4	feature	feature	NOUN
fcis-30156	38	5	refinement	refinement	NOUN
fcis-30156	38	6	stage	stage	NOUN
fcis-30156	38	7	,	,	PUNCT
fcis-30156	38	8	to	to	PART
fcis-30156	38	9	address	address	VERB
fcis-30156	38	10	the	the	DET
fcis-30156	38	11	channel	channel	NOUN
fcis-30156	38	12	109	109	NUM
fcis-30156	38	13	redundancy	redundancy	NOUN
fcis-30156	38	14	in	in	ADP
fcis-30156	38	15	feature	feature	NOUN
fcis-30156	38	16	maps	map	NOUN
fcis-30156	38	17	caused	cause	VERB
fcis-30156	38	18	by	by	ADP
fcis-30156	38	19	k×k	k×k	PROPN
fcis-30156	38	20	standard	standard	ADJ
fcis-30156	38	21	convolution	convolution	NOUN
fcis-30156	38	22	,	,	PUNCT
fcis-30156	38	23	the	the	DET
fcis-30156	38	24	cru	cru	PROPN
fcis-30156	38	25	sub	sub	NOUN
fcis-30156	38	26	-	-	NOUN
fcis-30156	38	27	module	module	NOUN
fcis-30156	38	28	uses	use	VERB
fcis-30156	38	29	a	a	DET
fcis-30156	38	30	split	split	ADJ
fcis-30156	38	31	-	-	PUNCT
fcis-30156	38	32	transformmerge	transformmerge	NOUN
fcis-30156	38	33	method	method	NOUN
fcis-30156	38	34	to	to	PART
fcis-30156	38	35	remove	remove	VERB
fcis-30156	38	36	redundant	redundant	ADJ
fcis-30156	38	37	channel	channel	NOUN
fcis-30156	38	38	features	feature	NOUN
fcis-30156	38	39	.	.	PUNCT
fcis-30156	39	1	the	the	DET
fcis-30156	39	2	k×k	k×k	PROPN
fcis-30156	39	3	standard	standard	ADJ
fcis-30156	39	4	convolution	convolution	NOUN
fcis-30156	39	5	can	can	AUX
fcis-30156	39	6	be	be	AUX
fcis-30156	39	7	expressed	express	VERB
fcis-30156	39	8	as	as	ADP
fcis-30156	39	9	ky	ky	PROPN
fcis-30156	39	10	m	m	PROPN
fcis-30156	39	11	x	x	PROPN
fcis-30156	39	12	,	,	PUNCT
fcis-30156	39	13	where	where	SCONJ
fcis-30156	39	14	k	k	PROPN
fcis-30156	39	15	c	c	PROPN
fcis-30156	39	16	k	k	PROPN
fcis-30156	39	17	km	km	PROPN
fcis-30156	39	18			PROPN
fcis-30156	39	19			PROPN
fcis-30156	39	20	represents	represent	VERB
fcis-30156	39	21	the	the	DET
fcis-30156	39	22	convolution	convolution	NOUN
fcis-30156	39	23	kernel	kernel	NOUN
fcis-30156	39	24	of	of	ADP
fcis-30156	39	25	the	the	DET
fcis-30156	39	26	k×k	k×k	PROPN
fcis-30156	39	27	standard	standard	ADJ
fcis-30156	39	28	convolution	convolution	NOUN
fcis-30156	39	29	,	,	PUNCT
fcis-30156	39	30	and	and	CCONJ
fcis-30156	39	31	,	,	PUNCT
fcis-30156	39	32	c	c	NOUN
fcis-30156	39	33	h	h	NOUN
fcis-30156	39	34	wx	wx	PROPN
fcis-30156	39	35	y	y	PROPN
fcis-30156	39	36			PROPN
fcis-30156	39	37			PROPN
fcis-30156	39	38	denote	denote	VERB
fcis-30156	39	39	the	the	DET
fcis-30156	39	40	input	input	NOUN
fcis-30156	39	41	and	and	CCONJ
fcis-30156	39	42	output	output	NOUN
fcis-30156	39	43	features	feature	NOUN
fcis-30156	39	44	of	of	ADP
fcis-30156	39	45	the	the	DET
fcis-30156	39	46	convolution	convolution	NOUN
fcis-30156	39	47	operation	operation	NOUN
fcis-30156	39	48	,	,	PUNCT
fcis-30156	39	49	respectively	respectively	ADV
fcis-30156	39	50	.	.	PUNCT
fcis-30156	40	1	fig	fig	NOUN
fcis-30156	40	2	1	1	NUM
fcis-30156	40	3	.	.	PUNCT
fcis-30156	40	4	overall	overall	ADJ
fcis-30156	40	5	structure	structure	NOUN
fcis-30156	40	6	in	in	ADP
fcis-30156	40	7	the	the	DET
fcis-30156	40	8	split	split	NOUN
fcis-30156	40	9	operation	operation	NOUN
fcis-30156	40	10	,	,	PUNCT
fcis-30156	40	11	first	first	ADV
fcis-30156	40	12	,	,	PUNCT
fcis-30156	40	13	the	the	DET
fcis-30156	40	14	spatially	spatially	ADV
fcis-30156	40	15	refined	refined	ADJ
fcis-30156	40	16	features	feature	NOUN
fcis-30156	40	17	output	output	NOUN
fcis-30156	40	18	from	from	ADP
fcis-30156	40	19	the	the	DET
fcis-30156	40	20	sru	sru	PROPN
fcis-30156	40	21	sub	sub	NOUN
fcis-30156	40	22	-	-	NOUN
fcis-30156	40	23	module	module	NOUN
fcis-30156	40	24	are	be	AUX
fcis-30156	40	25	divided	divide	VERB
fcis-30156	40	26	into	into	ADP
fcis-30156	40	27	two	two	NUM
fcis-30156	40	28	parts	part	NOUN
fcis-30156	40	29	,	,	PUNCT
fcis-30156	40	30	c	c	NOUN
fcis-30156	40	31	and	and	CCONJ
fcis-30156	40	32	(	(	PUNCT
fcis-30156	40	33	1	1	NUM
fcis-30156	40	34	)	)	PUNCT
fcis-30156	40	35	c	c	PROPN
fcis-30156	40	36	,	,	PUNCT
fcis-30156	40	37	along	along	ADP
fcis-30156	40	38	the	the	DET
fcis-30156	40	39	channel	channel	NOUN
fcis-30156	40	40	dimension	dimension	NOUN
fcis-30156	40	41	according	accord	VERB
fcis-30156	40	42	to	to	ADP
fcis-30156	40	43	the	the	DET
fcis-30156	40	44	split	split	ADJ
fcis-30156	40	45	ratio	ratio	NOUN
fcis-30156	40	46	(	(	PUNCT
fcis-30156	40	47	0,1)	0,1)	ADJ
fcis-30156	40	48			NOUN
fcis-30156	40	49	.	.	PUNCT
fcis-30156	41	1	second	second	ADJ
fcis-30156	41	2	,	,	PUNCT
fcis-30156	41	3	a	a	DET
fcis-30156	41	4	1×1	1×1	NUM
fcis-30156	41	5	convolution	convolution	NOUN
fcis-30156	41	6	is	be	AUX
fcis-30156	41	7	used	use	VERB
fcis-30156	41	8	to	to	PART
fcis-30156	41	9	compress	compress	VERB
fcis-30156	41	10	the	the	DET
fcis-30156	41	11	channel	channel	NOUN
fcis-30156	41	12	dimensions	dimension	NOUN
fcis-30156	41	13	of	of	ADP
fcis-30156	41	14	the	the	DET
fcis-30156	41	15	feature	feature	NOUN
fcis-30156	41	16	maps	map	NOUN
fcis-30156	41	17	to	to	PART
fcis-30156	41	18	improve	improve	VERB
fcis-30156	41	19	computational	computational	ADJ
fcis-30156	41	20	efficiency	efficiency	NOUN
fcis-30156	41	21	.	.	PUNCT
fcis-30156	42	1	after	after	ADP
fcis-30156	42	2	the	the	DET
fcis-30156	42	3	splitting	splitting	NOUN
fcis-30156	42	4	and	and	CCONJ
fcis-30156	42	5	compression	compression	NOUN
fcis-30156	42	6	operations	operation	NOUN
fcis-30156	42	7	,	,	PUNCT
fcis-30156	42	8	the	the	DET
fcis-30156	42	9	spatially	spatially	ADV
fcis-30156	42	10	refined	refined	ADJ
fcis-30156	42	11	features	feature	NOUN
fcis-30156	42	12	can	can	AUX
fcis-30156	42	13	be	be	AUX
fcis-30156	42	14	divided	divide	VERB
fcis-30156	42	15	into	into	ADP
fcis-30156	42	16	upper	upper	ADJ
fcis-30156	42	17	and	and	CCONJ
fcis-30156	42	18	lower	low	ADJ
fcis-30156	42	19	parts	part	NOUN
fcis-30156	42	20	.	.	PUNCT
fcis-30156	43	1	in	in	ADP
fcis-30156	43	2	the	the	DET
fcis-30156	43	3	merge	merge	NOUN
fcis-30156	43	4	operation	operation	NOUN
fcis-30156	43	5	stage	stage	NOUN
fcis-30156	43	6	,	,	PUNCT
fcis-30156	43	7	a	a	DET
fcis-30156	43	8	simplified	simplified	ADJ
fcis-30156	43	9	selective	selective	ADJ
fcis-30156	43	10	kernel	kernel	NOUN
fcis-30156	43	11	network	network	NOUN
fcis-30156	43	12	is	be	AUX
fcis-30156	43	13	utilized	utilize	VERB
fcis-30156	43	14	to	to	PART
fcis-30156	43	15	adaptively	adaptively	ADV
fcis-30156	43	16	combine	combine	VERB
fcis-30156	43	17	the	the	DET
fcis-30156	43	18	output	output	NOUN
fcis-30156	43	19	features	feature	VERB
fcis-30156	43	20	from	from	ADP
fcis-30156	43	21	the	the	DET
fcis-30156	43	22	upper	upper	ADJ
fcis-30156	43	23	and	and	CCONJ
fcis-30156	43	24	lower	low	ADJ
fcis-30156	43	25	transformation	transformation	NOUN
fcis-30156	43	26	stages	stage	NOUN
fcis-30156	43	27	.	.	PUNCT
fcis-30156	44	1	2.3	2.3	NUM
fcis-30156	44	2	.	.	PUNCT
fcis-30156	44	3	fine	fine	ADV
fcis-30156	44	4	-	-	PUNCT
fcis-30156	44	5	grained	grain	VERB
fcis-30156	44	6	human	human	ADJ
fcis-30156	44	7	dynamic	dynamic	ADJ
fcis-30156	44	8	skeleton	skeleton	NOUN
fcis-30156	44	9	feature	feature	NOUN
fcis-30156	44	10	extraction	extraction	NOUN
fcis-30156	44	11	the	the	DET
fcis-30156	44	12	proposed	propose	VERB
fcis-30156	44	13	method	method	NOUN
fcis-30156	44	14	introduces	introduce	VERB
fcis-30156	44	15	hierarchically	hierarchically	ADV
fcis-30156	44	16	decomposed	decompose	VERB
fcis-30156	44	17	graph	graph	NOUN
fcis-30156	44	18	convolutional	convolutional	ADJ
fcis-30156	44	19	networks	network	NOUN
fcis-30156	44	20	(	(	PUNCT
fcis-30156	44	21	hdgcn	hdgcn	PROPN
fcis-30156	44	22	)	)	PUNCT
fcis-30156	45	1	[	[	X
fcis-30156	45	2	10	10	NUM
fcis-30156	45	3	]	]	PUNCT
fcis-30156	45	4	based	base	VERB
fcis-30156	45	5	on	on	ADP
fcis-30156	45	6	spatio	spatio	PROPN
fcis-30156	45	7	-	-	PUNCT
fcis-30156	45	8	temporal	temporal	ADJ
fcis-30156	45	9	graph	graph	NOUN
fcis-30156	45	10	convolutional	convolutional	ADJ
fcis-30156	45	11	networks	network	NOUN
fcis-30156	45	12	,	,	PUNCT
fcis-30156	45	13	focusing	focus	VERB
fcis-30156	45	14	on	on	ADP
fcis-30156	45	15	the	the	DET
fcis-30156	45	16	relationships	relationship	NOUN
fcis-30156	45	17	between	between	ADP
fcis-30156	45	18	distant	distant	ADJ
fcis-30156	45	19	joints	joint	NOUN
fcis-30156	45	20	and	and	CCONJ
fcis-30156	45	21	the	the	DET
fcis-30156	45	22	features	feature	NOUN
fcis-30156	45	23	of	of	ADP
fcis-30156	45	24	key	key	ADJ
fcis-30156	45	25	edges	edge	NOUN
fcis-30156	45	26	,	,	PUNCT
fcis-30156	45	27	effectively	effectively	ADV
fcis-30156	45	28	improving	improve	VERB
fcis-30156	45	29	the	the	DET
fcis-30156	45	30	overall	overall	ADJ
fcis-30156	45	31	performance	performance	NOUN
fcis-30156	45	32	of	of	ADP
fcis-30156	45	33	the	the	DET
fcis-30156	45	34	model	model	NOUN
fcis-30156	45	35	.	.	PUNCT
fcis-30156	46	1	hd	hd	NOUN
fcis-30156	46	2	-	-	PUNCT
fcis-30156	46	3	gcn	gcn	NOUN
fcis-30156	46	4	consists	consist	VERB
fcis-30156	46	5	of	of	ADP
fcis-30156	46	6	two	two	NUM
fcis-30156	46	7	modules	module	NOUN
fcis-30156	46	8	:	:	PUNCT
fcis-30156	46	9	a	a	DET
fcis-30156	46	10	graph	graph	NOUN
fcis-30156	46	11	module	module	NOUN
fcis-30156	46	12	that	that	PRON
fcis-30156	46	13	establishes	establish	VERB
fcis-30156	46	14	hierarchical	hierarchical	ADJ
fcis-30156	46	15	decomposition	decomposition	NOUN
fcis-30156	46	16	levels	level	NOUN
fcis-30156	46	17	and	and	CCONJ
fcis-30156	46	18	an	an	DET
fcis-30156	46	19	attention	attention	NOUN
fcis-30156	46	20	-	-	PUNCT
fcis-30156	46	21	guided	guide	VERB
fcis-30156	46	22	hierarchical	hierarchical	ADJ
fcis-30156	46	23	aggregation	aggregation	NOUN
fcis-30156	46	24	module	module	NOUN
fcis-30156	46	25	.	.	PUNCT
fcis-30156	47	1	the	the	DET
fcis-30156	47	2	improved	improved	ADJ
fcis-30156	47	3	model	model	NOUN
fcis-30156	47	4	extracts	extract	VERB
fcis-30156	47	5	fine	fine	ADV
fcis-30156	47	6	-	-	PUNCT
fcis-30156	47	7	grained	grain	VERB
fcis-30156	47	8	human	human	ADJ
fcis-30156	47	9	dynamic	dynamic	ADJ
fcis-30156	47	10	skeleton	skeleton	NOUN
fcis-30156	47	11	features	feature	NOUN
fcis-30156	47	12	,	,	PUNCT
fcis-30156	47	13	enhancing	enhance	VERB
fcis-30156	47	14	the	the	DET
fcis-30156	47	15	expressive	expressive	ADJ
fcis-30156	47	16	capability	capability	NOUN
fcis-30156	47	17	of	of	ADP
fcis-30156	47	18	the	the	DET
fcis-30156	47	19	extracted	extract	VERB
fcis-30156	47	20	features	feature	NOUN
fcis-30156	47	21	and	and	CCONJ
fcis-30156	47	22	significantly	significantly	ADV
fcis-30156	47	23	boosting	boost	VERB
fcis-30156	47	24	the	the	DET
fcis-30156	47	25	overall	overall	ADJ
fcis-30156	47	26	performance	performance	NOUN
fcis-30156	47	27	of	of	ADP
fcis-30156	47	28	the	the	DET
fcis-30156	47	29	model	model	NOUN
fcis-30156	47	30	.	.	PUNCT
fcis-30156	48	1	2.3.1	2.3.1	NUM
fcis-30156	48	2	.	.	PUNCT
fcis-30156	48	3	fine	fine	ADV
fcis-30156	48	4	-	-	PUNCT
fcis-30156	48	5	grained	grain	VERB
fcis-30156	48	6	feature	feature	NOUN
fcis-30156	48	7	extraction	extraction	NOUN
fcis-30156	48	8	the	the	DET
fcis-30156	48	9	spatial	spatial	ADJ
fcis-30156	48	10	graph	graph	NOUN
fcis-30156	48	11	convolution	convolution	NOUN
fcis-30156	48	12	module	module	NOUN
fcis-30156	48	13	of	of	ADP
fcis-30156	48	14	the	the	DET
fcis-30156	48	15	hierarchically	hierarchically	ADV
fcis-30156	48	16	decomposed	decompose	VERB
fcis-30156	48	17	spatio	spatio	ADJ
fcis-30156	48	18	-	-	PUNCT
fcis-30156	48	19	temporal	temporal	ADJ
fcis-30156	48	20	graph	graph	NOUN
fcis-30156	48	21	convolution	convolution	NOUN
fcis-30156	48	22	introduces	introduce	VERB
fcis-30156	48	23	a	a	DET
fcis-30156	48	24	hierarchically	hierarchically	ADV
fcis-30156	48	25	decomposed	decompose	VERB
fcis-30156	48	26	graph	graph	NOUN
fcis-30156	48	27	convolution	convolution	NOUN
fcis-30156	48	28	module	module	NOUN
fcis-30156	48	29	.	.	PUNCT
fcis-30156	49	1	this	this	DET
fcis-30156	49	2	module	module	NOUN
fcis-30156	49	3	divides	divide	VERB
fcis-30156	49	4	the	the	DET
fcis-30156	49	5	human	human	ADJ
fcis-30156	49	6	skeleton	skeleton	NOUN
fcis-30156	49	7	graph	graph	NOUN
fcis-30156	49	8	into	into	ADP
fcis-30156	49	9	three	three	NUM
fcis-30156	49	10	levels	level	NOUN
fcis-30156	49	11	and	and	CCONJ
fcis-30156	49	12	extracts	extract	VERB
fcis-30156	49	13	spatial	spatial	ADJ
fcis-30156	49	14	features	feature	NOUN
fcis-30156	49	15	of	of	ADP
fcis-30156	49	16	the	the	DET
fcis-30156	49	17	human	human	ADJ
fcis-30156	49	18	skeleton	skeleton	NOUN
fcis-30156	49	19	for	for	ADP
fcis-30156	49	20	each	each	DET
fcis-30156	49	21	level	level	NOUN
fcis-30156	49	22	separately	separately	ADV
fcis-30156	49	23	.	.	PUNCT
fcis-30156	50	1	during	during	ADP
fcis-30156	50	2	the	the	DET
fcis-30156	50	3	construction	construction	NOUN
fcis-30156	50	4	of	of	ADP
fcis-30156	50	5	the	the	DET
fcis-30156	50	6	hierarchically	hierarchically	ADV
fcis-30156	50	7	decomposed	decompose	VERB
fcis-30156	50	8	graph	graph	NOUN
fcis-30156	50	9	,	,	PUNCT
fcis-30156	50	10	a	a	DET
fcis-30156	50	11	tree	tree	NOUN
fcis-30156	50	12	-	-	PUNCT
fcis-30156	50	13	like	like	ADJ
fcis-30156	50	14	graph	graph	NOUN
fcis-30156	50	15	with	with	ADP
fcis-30156	50	16	a	a	DET
fcis-30156	50	17	root	root	NOUN
fcis-30156	50	18	node	node	NOUN
fcis-30156	50	19	is	be	AUX
fcis-30156	50	20	first	first	ADV
fcis-30156	50	21	established	establish	VERB
fcis-30156	50	22	based	base	VERB
fcis-30156	50	23	on	on	ADP
fcis-30156	50	24	the	the	DET
fcis-30156	50	25	joints	joint	NOUN
fcis-30156	50	26	and	and	CCONJ
fcis-30156	50	27	physically	physically	ADV
fcis-30156	50	28	connected	connected	ADJ
fcis-30156	50	29	edges	edge	NOUN
fcis-30156	50	30	of	of	ADP
fcis-30156	50	31	the	the	DET
fcis-30156	50	32	human	human	ADJ
fcis-30156	50	33	dynamic	dynamic	ADJ
fcis-30156	50	34	skeleton	skeleton	NOUN
fcis-30156	50	35	graph	graph	NOUN
fcis-30156	50	36	.	.	PUNCT
fcis-30156	51	1	the	the	DET
fcis-30156	51	2	hierarchically	hierarchically	ADV
fcis-30156	51	3	decomposed	decompose	VERB
fcis-30156	51	4	graph	graph	NOUN
fcis-30156	51	5	convolution	convolution	NOUN
fcis-30156	51	6	module	module	NOUN
fcis-30156	51	7	has	have	VERB
fcis-30156	51	8	four	four	NUM
fcis-30156	51	9	parallel	parallel	ADJ
fcis-30156	51	10	computational	computational	ADJ
fcis-30156	51	11	branches	branch	NOUN
fcis-30156	51	12	,	,	PUNCT
fcis-30156	51	13	including	include	VERB
fcis-30156	51	14	three	three	NUM
fcis-30156	51	15	graph	graph	NOUN
fcis-30156	51	16	convolution	convolution	NOUN
fcis-30156	51	17	branches	branch	NOUN
fcis-30156	51	18	and	and	CCONJ
fcis-30156	51	19	an	an	DET
fcis-30156	51	20	additional	additional	ADJ
fcis-30156	51	21	edge	edge	NOUN
fcis-30156	51	22	convolution	convolution	NOUN
fcis-30156	51	23	branch	branch	NOUN
fcis-30156	51	24	.	.	PUNCT
fcis-30156	52	1	to	to	PART
fcis-30156	52	2	reduce	reduce	VERB
fcis-30156	52	3	computational	computational	ADJ
fcis-30156	52	4	complexity	complexity	NOUN
fcis-30156	52	5	,	,	PUNCT
fcis-30156	52	6	all	all	DET
fcis-30156	52	7	four	four	NUM
fcis-30156	52	8	branches	branch	NOUN
fcis-30156	52	9	undergo	undergo	VERB
fcis-30156	52	10	linear	linear	ADJ
fcis-30156	52	11	transformation	transformation	NOUN
fcis-30156	52	12	.	.	PUNCT
fcis-30156	53	1	in	in	ADP
fcis-30156	53	2	the	the	DET
fcis-30156	53	3	three	three	NUM
fcis-30156	53	4	graph	graph	NOUN
fcis-30156	53	5	convolution	convolution	NOUN
fcis-30156	53	6	branches	branch	NOUN
fcis-30156	53	7	,	,	PUNCT
fcis-30156	53	8	the	the	DET
fcis-30156	53	9	same	same	ADJ
fcis-30156	53	10	graph	graph	NOUN
fcis-30156	53	11	convolution	convolution	NOUN
fcis-30156	53	12	operation	operation	NOUN
fcis-30156	53	13	is	be	AUX
fcis-30156	53	14	performed	perform	VERB
fcis-30156	53	15	on	on	ADP
fcis-30156	53	16	each	each	DET
fcis-30156	53	17	hierarchical	hierarchical	ADJ
fcis-30156	53	18	edge	edge	NOUN
fcis-30156	53	19	set	set	NOUN
fcis-30156	53	20	containing	contain	VERB
fcis-30156	53	21	three	three	NUM
fcis-30156	53	22	edge	edge	NOUN
fcis-30156	53	23	subsets	subset	NOUN
fcis-30156	53	24	,	,	PUNCT
fcis-30156	53	25	and	and	CCONJ
fcis-30156	53	26	the	the	DET
fcis-30156	53	27	output	output	NOUN
fcis-30156	53	28	values	value	NOUN
fcis-30156	53	29	are	be	AUX
fcis-30156	53	30	concatenated	concatenate	VERB
fcis-30156	53	31	along	along	ADP
fcis-30156	53	32	the	the	DET
fcis-30156	53	33	channel	channel	NOUN
fcis-30156	53	34	dimension	dimension	NOUN
fcis-30156	53	35	.	.	PUNCT
fcis-30156	54	1	to	to	PART
fcis-30156	54	2	extract	extract	VERB
fcis-30156	54	3	samplelevel	samplelevel	NOUN
fcis-30156	54	4	edge	edge	PROPN
fcis-30156	54	5	association	association	NOUN
fcis-30156	54	6	information	information	NOUN
fcis-30156	54	7	reflecting	reflect	VERB
fcis-30156	54	8	the	the	DET
fcis-30156	54	9	similarity	similarity	NOUN
fcis-30156	54	10	between	between	ADP
fcis-30156	54	11	nodes	node	NOUN
fcis-30156	54	12	in	in	ADP
fcis-30156	54	13	the	the	DET
fcis-30156	54	14	feature	feature	NOUN
fcis-30156	54	15	space	space	NOUN
fcis-30156	54	16	,	,	PUNCT
fcis-30156	54	17	the	the	DET
fcis-30156	54	18	module	module	NOUN
fcis-30156	54	19	employs	employ	VERB
fcis-30156	54	20	edge	edge	NOUN
fcis-30156	54	21	convolution	convolution	NOUN
fcis-30156	54	22	(	(	PUNCT
fcis-30156	54	23	edgeconv	edgeconv	NOUN
fcis-30156	54	24	)	)	PUNCT
fcis-30156	55	1	[	[	X
fcis-30156	55	2	11	11	NUM
fcis-30156	55	3	]	]	PUNCT
fcis-30156	55	4	,	,	PUNCT
fcis-30156	55	5	which	which	PRON
fcis-30156	55	6	extracts	extract	VERB
fcis-30156	55	7	features	feature	NOUN
fcis-30156	55	8	of	of	ADP
fcis-30156	55	9	the	the	DET
fcis-30156	55	10	human	human	ADJ
fcis-30156	55	11	dynamic	dynamic	ADJ
fcis-30156	55	12	skeleton	skeleton	NOUN
fcis-30156	55	13	graph	graph	NOUN
fcis-30156	55	14	through	through	ADP
fcis-30156	55	15	local	local	ADJ
fcis-30156	55	16	neighborhood	neighborhood	NOUN
fcis-30156	55	17	graphs	graph	NOUN
fcis-30156	55	18	in	in	ADP
fcis-30156	55	19	the	the	DET
fcis-30156	55	20	feature	feature	NOUN
fcis-30156	55	21	space	space	NOUN
fcis-30156	55	22	.	.	PUNCT
fcis-30156	56	1	first	first	ADV
fcis-30156	56	2	,	,	PUNCT
fcis-30156	56	3	the	the	DET
fcis-30156	56	4	edge	edge	NOUN
fcis-30156	56	5	convolution	convolution	NOUN
fcis-30156	56	6	branch	branch	NOUN
fcis-30156	56	7	uses	use	VERB
fcis-30156	56	8	average	average	ADJ
fcis-30156	56	9	pooling	pooling	NOUN
fcis-30156	56	10	to	to	PART
fcis-30156	56	11	improve	improve	VERB
fcis-30156	56	12	computational	computational	ADJ
fcis-30156	56	13	efficiency	efficiency	NOUN
fcis-30156	56	14	.	.	PUNCT
fcis-30156	57	1	then	then	ADV
fcis-30156	57	2	,	,	PUNCT
fcis-30156	57	3	the	the	DET
fcis-30156	57	4	k	k	NOUN
fcis-30156	57	5	-	-	PUNCT
fcis-30156	57	6	nearest	near	ADJ
fcis-30156	57	7	neighbor	neighbor	NOUN
fcis-30156	57	8	algorithm	algorithm	NOUN
fcis-30156	57	9	is	be	AUX
fcis-30156	57	10	applied	apply	VERB
fcis-30156	57	11	based	base	VERB
fcis-30156	57	12	on	on	ADP
fcis-30156	57	13	euclidean	euclidean	ADJ
fcis-30156	57	14	distance	distance	NOUN
fcis-30156	57	15	to	to	PART
fcis-30156	57	16	obtain	obtain	VERB
fcis-30156	57	17	the	the	DET
fcis-30156	57	18	adjacency	adjacency	NOUN
fcis-30156	57	19	edge	edge	NOUN
fcis-30156	57	20	set	set	NOUN
fcis-30156	57	21	.	.	PUNCT
fcis-30156	58	1	2.3.2	2.3.2	NUM
fcis-30156	58	2	.	.	PUNCT
fcis-30156	58	3	attention	attention	NOUN
fcis-30156	58	4	-	-	PUNCT
fcis-30156	58	5	guided	guide	VERB
fcis-30156	58	6	feature	feature	NOUN
fcis-30156	58	7	fusion	fusion	NOUN
fcis-30156	58	8	after	after	ADP
fcis-30156	58	9	the	the	DET
fcis-30156	58	10	hierarchically	hierarchically	ADV
fcis-30156	58	11	decomposed	decompose	VERB
fcis-30156	58	12	graph	graph	NOUN
fcis-30156	58	13	convolution	convolution	NOUN
fcis-30156	58	14	module	module	NOUN
fcis-30156	58	15	,	,	PUNCT
fcis-30156	58	16	human	human	ADJ
fcis-30156	58	17	skeleton	skeleton	NOUN
fcis-30156	58	18	features	feature	NOUN
fcis-30156	58	19	extracted	extract	VERB
fcis-30156	58	20	from	from	ADP
fcis-30156	58	21	different	different	ADJ
fcis-30156	58	22	levels	level	NOUN
fcis-30156	58	23	are	be	AUX
fcis-30156	58	24	obtained	obtain	VERB
fcis-30156	58	25	.	.	PUNCT
fcis-30156	59	1	for	for	ADP
fcis-30156	59	2	the	the	DET
fcis-30156	59	3	fusion	fusion	NOUN
fcis-30156	59	4	of	of	ADP
fcis-30156	59	5	features	feature	NOUN
fcis-30156	59	6	at	at	ADP
fcis-30156	59	7	different	different	ADJ
fcis-30156	59	8	levels	level	NOUN
fcis-30156	59	9	,	,	PUNCT
fcis-30156	59	10	this	this	DET
fcis-30156	59	11	paper	paper	NOUN
fcis-30156	59	12	uses	use	VERB
fcis-30156	59	13	an	an	DET
fcis-30156	59	14	attention	attention	NOUN
fcis-30156	59	15	-	-	PUNCT
fcis-30156	59	16	guided	guide	VERB
fcis-30156	59	17	hierarchical	hierarchical	ADJ
fcis-30156	59	18	aggregation	aggregation	NOUN
fcis-30156	59	19	module	module	NOUN
fcis-30156	59	20	.	.	PUNCT
fcis-30156	60	1	this	this	DET
fcis-30156	60	2	feature	feature	NOUN
fcis-30156	60	3	fusion	fusion	NOUN
fcis-30156	60	4	module	module	NOUN
fcis-30156	60	5	focuses	focus	VERB
fcis-30156	60	6	on	on	ADP
fcis-30156	60	7	the	the	DET
fcis-30156	60	8	relationships	relationship	NOUN
fcis-30156	60	9	and	and	CCONJ
fcis-30156	60	10	importance	importance	NOUN
fcis-30156	60	11	of	of	ADP
fcis-30156	60	12	key	key	ADJ
fcis-30156	60	13	edges	edge	NOUN
fcis-30156	60	14	,	,	PUNCT
fcis-30156	60	15	applying	apply	VERB
fcis-30156	60	16	an	an	DET
fcis-30156	60	17	attention	attention	NOUN
fcis-30156	60	18	-	-	PUNCT
fcis-30156	60	19	weighted	weight	VERB
fcis-30156	60	20	strategy	strategy	NOUN
fcis-30156	60	21	to	to	ADP
fcis-30156	60	22	the	the	DET
fcis-30156	60	23	combined	combine	VERB
fcis-30156	60	24	outputs	output	NOUN
fcis-30156	60	25	of	of	ADP
fcis-30156	60	26	different	different	ADJ
fcis-30156	60	27	levels	level	NOUN
fcis-30156	60	28	,	,	PUNCT
fcis-30156	60	29	effectively	effectively	ADV
fcis-30156	60	30	enhancing	enhance	VERB
fcis-30156	60	31	the	the	DET
fcis-30156	60	32	expressive	expressive	ADJ
fcis-30156	60	33	capability	capability	NOUN
fcis-30156	60	34	of	of	ADP
fcis-30156	60	35	the	the	DET
fcis-30156	60	36	extracted	extract	VERB
fcis-30156	60	37	features	feature	NOUN
fcis-30156	60	38	.	.	PUNCT
fcis-30156	61	1	the	the	DET
fcis-30156	61	2	input	input	NOUN
fcis-30156	61	3	to	to	ADP
fcis-30156	61	4	the	the	DET
fcis-30156	61	5	attention	attention	NOUN
fcis-30156	61	6	-	-	PUNCT
fcis-30156	61	7	guided	guide	VERB
fcis-30156	61	8	hierarchical	hierarchical	ADJ
fcis-30156	61	9	aggregation	aggregation	NOUN
fcis-30156	61	10	module	module	NOUN
fcis-30156	61	11	is	be	AUX
fcis-30156	61	12	the	the	DET
fcis-30156	61	13	output	output	NOUN
fcis-30156	61	14	of	of	ADP
fcis-30156	61	15	the	the	DET
fcis-30156	61	16	graph	graph	NOUN
fcis-30156	61	17	convolution	convolution	NOUN
fcis-30156	61	18	operation	operation	NOUN
fcis-30156	61	19	.	.	PUNCT
fcis-30156	62	1	for	for	ADP
fcis-30156	62	2	this	this	DET
fcis-30156	62	3	output	output	NOUN
fcis-30156	62	4	,	,	PUNCT
fcis-30156	62	5	the	the	DET
fcis-30156	62	6	frame	frame	NOUN
fcis-30156	62	7	with	with	ADP
fcis-30156	62	8	the	the	DET
fcis-30156	62	9	highest	high	ADJ
fcis-30156	62	10	score	score	NOUN
fcis-30156	62	11	is	be	AUX
fcis-30156	62	12	extracted	extract	VERB
fcis-30156	62	13	.	.	PUNCT
fcis-30156	63	1	first	first	ADV
fcis-30156	63	2	,	,	PUNCT
fcis-30156	63	3	considering	consider	VERB
fcis-30156	63	4	that	that	SCONJ
fcis-30156	63	5	each	each	DET
fcis-30156	63	6	level	level	NOUN
fcis-30156	63	7	has	have	VERB
fcis-30156	63	8	representative	representative	ADJ
fcis-30156	63	9	key	key	ADJ
fcis-30156	63	10	points	point	NOUN
fcis-30156	63	11	,	,	PUNCT
fcis-30156	63	12	a	a	DET
fcis-30156	63	13	representative	representative	ADJ
fcis-30156	63	14	spatial	spatial	ADJ
fcis-30156	63	15	average	average	ADJ
fcis-30156	63	16	pooling	pooling	NOUN
fcis-30156	63	17	(	(	PUNCT
fcis-30156	63	18	rsap	rsap	NOUN
fcis-30156	63	19	)	)	PUNCT
fcis-30156	63	20	layer	layer	NOUN
fcis-30156	63	21	ψ	ψ	NOUN
fcis-30156	63	22	is	be	AUX
fcis-30156	63	23	introduced	introduce	VERB
fcis-30156	63	24	after	after	ADP
fcis-30156	63	25	the	the	DET
fcis-30156	63	26	multi	multi	ADJ
fcis-30156	63	27	-	-	ADJ
fcis-30156	63	28	level	level	ADJ
fcis-30156	63	29	feature	feature	NOUN
fcis-30156	63	30	extraction	extraction	NOUN
fcis-30156	63	31	output	output	NOUN
fcis-30156	63	32	to	to	PART
fcis-30156	63	33	avoid	avoid	VERB
fcis-30156	63	34	scaling	scale	VERB
fcis-30156	63	35	bias	bias	NOUN
fcis-30156	63	36	during	during	ADP
fcis-30156	63	37	computation	computation	NOUN
fcis-30156	63	38	,	,	PUNCT
fcis-30156	63	39	assigning	assign	VERB
fcis-30156	63	40	higher	high	ADJ
fcis-30156	63	41	weights	weight	NOUN
fcis-30156	63	42	to	to	PART
fcis-30156	63	43	representative	representative	VERB
fcis-30156	63	44	key	key	ADJ
fcis-30156	63	45	points	point	NOUN
fcis-30156	63	46	.	.	PUNCT
fcis-30156	64	1	then	then	ADV
fcis-30156	64	2	,	,	PUNCT
fcis-30156	64	3	hierarchical	hierarchical	ADJ
fcis-30156	64	4	edge	edge	NOUN
fcis-30156	64	5	convolution	convolution	NOUN
fcis-30156	64	6	is	be	AUX
fcis-30156	64	7	applied	apply	VERB
fcis-30156	64	8	after	after	ADP
fcis-30156	64	9	the	the	DET
fcis-30156	64	10	representative	representative	ADJ
fcis-30156	64	11	spatial	spatial	ADJ
fcis-30156	64	12	pooling	pool	VERB
fcis-30156	64	13	layer	layer	NOUN
fcis-30156	64	14	,	,	PUNCT
fcis-30156	64	15	fusing	fuse	VERB
fcis-30156	64	16	the	the	DET
fcis-30156	64	17	human	human	ADJ
fcis-30156	64	18	skeleton	skeleton	NOUN
fcis-30156	64	19	graph	graph	NOUN
fcis-30156	64	20	features	feature	NOUN
fcis-30156	64	21	optimized	optimize	VERB
fcis-30156	64	22	by	by	ADP
fcis-30156	64	23	the	the	DET
fcis-30156	64	24	attention	attention	NOUN
fcis-30156	64	25	module	module	NOUN
fcis-30156	64	26	at	at	ADP
fcis-30156	64	27	each	each	DET
fcis-30156	64	28	level	level	NOUN
fcis-30156	64	29	into	into	ADP
fcis-30156	64	30	an	an	DET
fcis-30156	64	31	overall	overall	ADJ
fcis-30156	64	32	feature	feature	NOUN
fcis-30156	64	33	.	.	PUNCT
fcis-30156	65	1	2.4	2.4	NUM
fcis-30156	65	2	.	.	PUNCT
fcis-30156	66	1	clustering	cluster	VERB
fcis-30156	66	2	during	during	ADP
fcis-30156	66	3	the	the	DET
fcis-30156	66	4	clustering	clustering	ADJ
fcis-30156	66	5	phase	phase	NOUN
fcis-30156	66	6	,	,	PUNCT
fcis-30156	66	7	the	the	DET
fcis-30156	66	8	hierarchically	hierarchically	ADV
fcis-30156	66	9	decomposed	decompose	VERB
fcis-30156	66	10	spatio	spatio	ADJ
fcis-30156	66	11	-	-	PUNCT
fcis-30156	66	12	temporal	temporal	ADJ
fcis-30156	66	13	graph	graph	NOUN
fcis-30156	66	14	convolutional	convolutional	ADJ
fcis-30156	66	15	model	model	NOUN
fcis-30156	66	16	obtains	obtain	VERB
fcis-30156	66	17	spatiotemporal	spatiotemporal	ADJ
fcis-30156	66	18	features	feature	NOUN
fcis-30156	66	19	of	of	ADP
fcis-30156	66	20	human	human	ADJ
fcis-30156	66	21	dynamic	dynamic	ADJ
fcis-30156	66	22	skeletons	skeleton	NOUN
fcis-30156	66	23	containing	contain	VERB
fcis-30156	66	24	rich	rich	ADJ
fcis-30156	66	25	information	information	NOUN
fcis-30156	66	26	from	from	ADP
fcis-30156	66	27	training	training	NOUN
fcis-30156	66	28	samples	sample	NOUN
fcis-30156	66	29	and	and	CCONJ
fcis-30156	66	30	constructs	construct	VERB
fcis-30156	66	31	an	an	DET
fcis-30156	66	32	underlying	underlying	ADJ
fcis-30156	66	33	action	action	NOUN
fcis-30156	66	34	dictionary	dictionary	NOUN
fcis-30156	66	35	.	.	PUNCT
fcis-30156	67	1	in	in	ADP
fcis-30156	67	2	the	the	DET
fcis-30156	67	3	clustering	clustering	ADJ
fcis-30156	67	4	phase	phase	NOUN
fcis-30156	67	5	,	,	PUNCT
fcis-30156	67	6	the	the	DET
fcis-30156	67	7	model	model	NOUN
fcis-30156	67	8	maps	map	VERB
fcis-30156	67	9	the	the	DET
fcis-30156	67	10	learned	learn	VERB
fcis-30156	67	11	features	feature	NOUN
fcis-30156	67	12	to	to	ADP
fcis-30156	67	13	a	a	DET
fcis-30156	67	14	latent	latent	NOUN
fcis-30156	67	15	space	space	NOUN
fcis-30156	67	16	and	and	CCONJ
fcis-30156	67	17	embeds	embed	VERB
fcis-30156	67	18	them	they	PRON
fcis-30156	67	19	into	into	ADP
fcis-30156	67	20	clusters	cluster	NOUN
fcis-30156	67	21	,	,	PUNCT
fcis-30156	67	22	calculating	calculate	VERB
fcis-30156	67	23	the	the	DET
fcis-30156	67	24	probability	probability	NOUN
fcis-30156	67	25	of	of	ADP
fcis-30156	67	26	video	video	NOUN
fcis-30156	67	27	sequences	sequence	NOUN
fcis-30156	67	28	containing	contain	VERB
fcis-30156	67	29	abnormal	abnormal	ADJ
fcis-30156	67	30	behaviors	behavior	NOUN
fcis-30156	67	31	through	through	ADP
fcis-30156	67	32	clustering	clustering	NOUN
fcis-30156	67	33	.	.	PUNCT
fcis-30156	68	1	3	3	X
fcis-30156	68	2	.	.	X
fcis-30156	68	3	experiments	experiment	NOUN
fcis-30156	68	4	3.1	3.1	NUM
fcis-30156	68	5	.	.	PUNCT
fcis-30156	69	1	experiments	experiment	NOUN
fcis-30156	69	2	setting	set	VERB
fcis-30156	69	3	the	the	DET
fcis-30156	69	4	experimental	experimental	ADJ
fcis-30156	69	5	design	design	NOUN
fcis-30156	69	6	is	be	AUX
fcis-30156	69	7	divided	divide	VERB
fcis-30156	69	8	into	into	ADP
fcis-30156	69	9	two	two	NUM
fcis-30156	69	10	parts	part	NOUN
fcis-30156	69	11	.	.	PUNCT
fcis-30156	70	1	first	first	ADV
fcis-30156	70	2	,	,	PUNCT
fcis-30156	70	3	the	the	DET
fcis-30156	70	4	model	model	NOUN
fcis-30156	70	5	conducts	conduct	VERB
fcis-30156	70	6	fine	fine	ADV
fcis-30156	70	7	-	-	PUNCT
fcis-30156	70	8	grained	grain	VERB
fcis-30156	70	9	abnormal	abnormal	ADJ
fcis-30156	70	10	behavior	behavior	NOUN
fcis-30156	70	11	recognition	recognition	NOUN
fcis-30156	70	12	experiments	experiment	NOUN
fcis-30156	70	13	on	on	ADP
fcis-30156	70	14	the	the	DET
fcis-30156	70	15	shanghaitech	shanghaitech	NOUN
fcis-30156	70	16	dataset	dataset	VERB
fcis-30156	70	17	.	.	PUNCT
fcis-30156	71	1	the	the	DET
fcis-30156	71	2	experiment	experiment	NOUN
fcis-30156	71	3	is	be	AUX
fcis-30156	71	4	divided	divide	VERB
fcis-30156	71	5	into	into	ADP
fcis-30156	71	6	training	training	NOUN
fcis-30156	71	7	and	and	CCONJ
fcis-30156	71	8	testing	testing	NOUN
fcis-30156	71	9	phases	phase	NOUN
fcis-30156	71	10	,	,	PUNCT
fcis-30156	71	11	where	where	SCONJ
fcis-30156	71	12	the	the	DET
fcis-30156	71	13	training	training	NOUN
fcis-30156	71	14	dataset	dataset	NOUN
fcis-30156	71	15	includes	include	VERB
fcis-30156	71	16	only	only	ADV
fcis-30156	71	17	normal	normal	ADJ
fcis-30156	71	18	samples	sample	NOUN
fcis-30156	71	19	,	,	PUNCT
fcis-30156	71	20	while	while	SCONJ
fcis-30156	71	21	the	the	DET
fcis-30156	71	22	testing	testing	NOUN
fcis-30156	71	23	dataset	dataset	NOUN
fcis-30156	71	24	includes	include	VERB
fcis-30156	71	25	both	both	CCONJ
fcis-30156	71	26	normal	normal	ADJ
fcis-30156	71	27	and	and	CCONJ
fcis-30156	71	28	abnormal	abnormal	ADJ
fcis-30156	71	29	samples	sample	NOUN
fcis-30156	71	30	.	.	PUNCT
fcis-30156	72	1	for	for	ADP
fcis-30156	72	2	each	each	DET
fcis-30156	72	3	input	input	NOUN
fcis-30156	72	4	video	video	NOUN
fcis-30156	72	5	sequence	sequence	NOUN
fcis-30156	72	6	,	,	PUNCT
fcis-30156	72	7	the	the	DET
fcis-30156	72	8	model	model	NOUN
fcis-30156	72	9	uses	use	VERB
fcis-30156	72	10	a	a	DET
fcis-30156	72	11	sliding	slide	VERB
fcis-30156	72	12	window	window	NOUN
fcis-30156	72	13	approach	approach	NOUN
fcis-30156	72	14	to	to	PART
fcis-30156	72	15	crop	crop	VERB
fcis-30156	72	16	the	the	DET
fcis-30156	72	17	sequence	sequence	NOUN
fcis-30156	72	18	into	into	ADP
fcis-30156	72	19	fixed	fix	VERB
fcis-30156	72	20	-	-	PUNCT
fcis-30156	72	21	length	length	NOUN
fcis-30156	72	22	segments	segment	NOUN
fcis-30156	72	23	and	and	CCONJ
fcis-30156	72	24	assigns	assign	VERB
fcis-30156	72	25	a	a	DET
fcis-30156	72	26	score	score	NOUN
fcis-30156	72	27	to	to	ADP
fcis-30156	72	28	each	each	DET
fcis-30156	72	29	video	video	NOUN
fcis-30156	72	30	frame	frame	NOUN
fcis-30156	72	31	.	.	PUNCT
fcis-30156	73	1	in	in	ADP
fcis-30156	73	2	cases	case	NOUN
fcis-30156	73	3	where	where	SCONJ
fcis-30156	73	4	multiple	multiple	ADJ
fcis-30156	73	5	individuals	individual	NOUN
fcis-30156	73	6	appear	appear	VERB
fcis-30156	73	7	in	in	ADP
fcis-30156	73	8	a	a	DET
fcis-30156	73	9	video	video	NOUN
fcis-30156	73	10	frame	frame	NOUN
fcis-30156	73	11	,	,	PUNCT
fcis-30156	73	12	the	the	DET
fcis-30156	73	13	model	model	NOUN
fcis-30156	73	14	scores	score	VERB
fcis-30156	73	15	each	each	DET
fcis-30156	73	16	individual	individual	NOUN
fcis-30156	73	17	separately	separately	ADV
fcis-30156	73	18	and	and	CCONJ
fcis-30156	73	19	takes	take	VERB
fcis-30156	73	20	the	the	DET
fcis-30156	73	21	highest	high	ADJ
fcis-30156	73	22	score	score	NOUN
fcis-30156	73	23	as	as	ADP
fcis-30156	73	24	the	the	DET
fcis-30156	73	25	110	110	NUM
fcis-30156	73	26	anomaly	anomaly	NOUN
fcis-30156	73	27	score	score	NOUN
fcis-30156	73	28	for	for	ADP
fcis-30156	73	29	that	that	DET
fcis-30156	73	30	frame	frame	NOUN
fcis-30156	73	31	.	.	PUNCT
fcis-30156	74	1	then	then	ADV
fcis-30156	74	2	,	,	PUNCT
fcis-30156	74	3	the	the	DET
fcis-30156	74	4	model	model	NOUN
fcis-30156	74	5	performs	perform	VERB
fcis-30156	74	6	coarse	coarse	NOUN
fcis-30156	74	7	-	-	PUNCT
fcis-30156	74	8	grained	grain	VERB
fcis-30156	74	9	abnormal	abnormal	ADJ
fcis-30156	74	10	behavior	behavior	NOUN
fcis-30156	74	11	recognition	recognition	NOUN
fcis-30156	74	12	experiments	experiment	NOUN
fcis-30156	74	13	on	on	ADP
fcis-30156	74	14	the	the	DET
fcis-30156	74	15	ntu	ntu	PROPN
fcis-30156	74	16	-	-	PUNCT
fcis-30156	74	17	rgb+d	rgb+d	PROPN
fcis-30156	74	18	dataset	dataset	NOUN
fcis-30156	74	19	.	.	PUNCT
fcis-30156	75	1	during	during	ADP
fcis-30156	75	2	the	the	DET
fcis-30156	75	3	training	training	NOUN
fcis-30156	75	4	phase	phase	NOUN
fcis-30156	75	5	,	,	PUNCT
fcis-30156	75	6	the	the	DET
fcis-30156	75	7	model	model	NOUN
fcis-30156	75	8	is	be	AUX
fcis-30156	75	9	trained	train	VERB
fcis-30156	75	10	on	on	ADP
fcis-30156	75	11	an	an	DET
fcis-30156	75	12	unsupervised	unsupervised	ADJ
fcis-30156	75	13	dataset	dataset	NOUN
fcis-30156	75	14	containing	contain	VERB
fcis-30156	75	15	only	only	ADV
fcis-30156	75	16	normal	normal	ADJ
fcis-30156	75	17	samples	sample	NOUN
fcis-30156	75	18	.	.	PUNCT
fcis-30156	76	1	in	in	ADP
fcis-30156	76	2	the	the	DET
fcis-30156	76	3	testing	testing	NOUN
fcis-30156	76	4	phase	phase	NOUN
fcis-30156	76	5	,	,	PUNCT
fcis-30156	76	6	the	the	DET
fcis-30156	76	7	model	model	NOUN
fcis-30156	76	8	distinguishes	distinguish	VERB
fcis-30156	76	9	between	between	ADP
fcis-30156	76	10	normal	normal	ADJ
fcis-30156	76	11	and	and	CCONJ
fcis-30156	76	12	abnormal	abnormal	ADJ
fcis-30156	76	13	samples	sample	NOUN
fcis-30156	76	14	by	by	ADP
fcis-30156	76	15	measuring	measure	VERB
fcis-30156	76	16	the	the	DET
fcis-30156	76	17	difference	difference	NOUN
fcis-30156	76	18	between	between	ADP
fcis-30156	76	19	each	each	DET
fcis-30156	76	20	test	test	NOUN
fcis-30156	76	21	sample	sample	NOUN
fcis-30156	76	22	and	and	CCONJ
fcis-30156	76	23	the	the	DET
fcis-30156	76	24	training	training	NOUN
fcis-30156	76	25	samples	sample	NOUN
fcis-30156	76	26	.	.	PUNCT
fcis-30156	77	1	3.2	3.2	NUM
fcis-30156	77	2	.	.	PUNCT
fcis-30156	77	3	ablation	ablation	NOUN
fcis-30156	77	4	study	study	NOUN
fcis-30156	77	5	table	table	NOUN
fcis-30156	77	6	1	1	NUM
fcis-30156	77	7	.	.	PUNCT
fcis-30156	77	8	ablation	ablation	NOUN
fcis-30156	77	9	experiment	experiment	NOUN
fcis-30156	77	10	results	result	NOUN
fcis-30156	77	11	method	method	VERB
fcis-30156	77	12	shanghaitech	shanghaitech	NOUN
fcis-30156	77	13	ntu	ntu	PROPN
fcis-30156	77	14	-	-	PUNCT
fcis-30156	77	15	rgb+d	rgb+d	PROPN
fcis-30156	77	16	gepc	gepc	X
fcis-30156	77	17	0.752	0.752	NUM
fcis-30156	77	18	0.730	0.730	NUM
fcis-30156	77	19	gepc+ssconv	gepc+ssconv	PROPN
fcis-30156	77	20	0.760	0.760	NUM
fcis-30156	77	21	0.741	0.741	NUM
fcis-30156	77	22	gepc+hdgcn	gepc+hdgcn	PROPN
fcis-30156	77	23	gepc+ssconv+hdgcn	gepc+ssconv+hdgcn	PUNCT
fcis-30156	77	24	0.757	0.757	NUM
fcis-30156	77	25	0.764	0.764	NUM
fcis-30156	77	26	0.738	0.738	NUM
fcis-30156	77	27	0.746	0.746	NUM
fcis-30156	77	28	from	from	ADP
fcis-30156	77	29	the	the	DET
fcis-30156	77	30	table	table	NOUN
fcis-30156	77	31	above	above	ADV
fcis-30156	77	32	,	,	PUNCT
fcis-30156	77	33	we	we	PRON
fcis-30156	77	34	can	can	AUX
fcis-30156	77	35	observe	observe	VERB
fcis-30156	77	36	that	that	SCONJ
fcis-30156	77	37	when	when	SCONJ
fcis-30156	77	38	training	train	VERB
fcis-30156	77	39	with	with	ADP
fcis-30156	77	40	the	the	DET
fcis-30156	77	41	shanghaitech	shanghaitech	NOUN
fcis-30156	77	42	and	and	CCONJ
fcis-30156	77	43	ntu	ntu	PROPN
fcis-30156	77	44	-	-	PUNCT
fcis-30156	77	45	rgb+d	rgb+d	PROPN
fcis-30156	77	46	datasets	dataset	NOUN
fcis-30156	77	47	,	,	PUNCT
fcis-30156	77	48	the	the	DET
fcis-30156	77	49	addition	addition	NOUN
fcis-30156	77	50	of	of	ADP
fcis-30156	77	51	the	the	DET
fcis-30156	77	52	human	human	ADJ
fcis-30156	77	53	dynamic	dynamic	ADJ
fcis-30156	77	54	skeleton	skeleton	NOUN
fcis-30156	77	55	feature	feature	NOUN
fcis-30156	77	56	preprocessing	preprocesse	VERB
fcis-30156	77	57	network	network	NOUN
fcis-30156	77	58	enhances	enhance	VERB
fcis-30156	77	59	the	the	DET
fcis-30156	77	60	expressive	expressive	ADJ
fcis-30156	77	61	capability	capability	NOUN
fcis-30156	77	62	of	of	ADP
fcis-30156	77	63	feature	feature	NOUN
fcis-30156	77	64	information	information	NOUN
fcis-30156	77	65	,	,	PUNCT
fcis-30156	77	66	leading	lead	VERB
fcis-30156	77	67	to	to	ADP
fcis-30156	77	68	an	an	DET
fcis-30156	77	69	overall	overall	ADJ
fcis-30156	77	70	improvement	improvement	NOUN
fcis-30156	77	71	in	in	ADP
fcis-30156	77	72	model	model	NOUN
fcis-30156	77	73	performance	performance	NOUN
fcis-30156	77	74	.	.	PUNCT
fcis-30156	78	1	after	after	ADP
fcis-30156	78	2	incorporating	incorporate	VERB
fcis-30156	78	3	the	the	DET
fcis-30156	78	4	fine	fine	ADV
fcis-30156	78	5	-	-	PUNCT
fcis-30156	78	6	grained	grain	VERB
fcis-30156	78	7	human	human	ADJ
fcis-30156	78	8	dynamic	dynamic	ADJ
fcis-30156	78	9	skeleton	skeleton	NOUN
fcis-30156	78	10	feature	feature	NOUN
fcis-30156	78	11	extraction	extraction	NOUN
fcis-30156	78	12	module	module	NOUN
fcis-30156	78	13	,	,	PUNCT
fcis-30156	78	14	the	the	DET
fcis-30156	78	15	model	model	NOUN
fcis-30156	78	16	's	's	PART
fcis-30156	78	17	ability	ability	NOUN
fcis-30156	78	18	to	to	PART
fcis-30156	78	19	extract	extract	VERB
fcis-30156	78	20	information	information	NOUN
fcis-30156	78	21	from	from	ADP
fcis-30156	78	22	human	human	ADJ
fcis-30156	78	23	dynamic	dynamic	ADJ
fcis-30156	78	24	skeleton	skeleton	NOUN
fcis-30156	78	25	features	feature	NOUN
fcis-30156	78	26	is	be	AUX
fcis-30156	78	27	enhanced	enhance	VERB
fcis-30156	78	28	compared	compare	VERB
fcis-30156	78	29	to	to	ADP
fcis-30156	78	30	the	the	DET
fcis-30156	78	31	pure	pure	ADJ
fcis-30156	78	32	spatiotemporal	spatiotemporal	ADJ
fcis-30156	78	33	graph	graph	NOUN
fcis-30156	78	34	convolutional	convolutional	ADJ
fcis-30156	78	35	network	network	NOUN
fcis-30156	78	36	model	model	NOUN
fcis-30156	78	37	,	,	PUNCT
fcis-30156	78	38	resulting	result	VERB
fcis-30156	78	39	in	in	ADP
fcis-30156	78	40	an	an	DET
fcis-30156	78	41	overall	overall	ADJ
fcis-30156	78	42	performance	performance	NOUN
fcis-30156	78	43	improvement	improvement	NOUN
fcis-30156	78	44	.	.	PUNCT
fcis-30156	79	1	therefore	therefore	ADV
fcis-30156	79	2	,	,	PUNCT
fcis-30156	79	3	this	this	DET
fcis-30156	79	4	ablation	ablation	NOUN
fcis-30156	79	5	experiment	experiment	NOUN
fcis-30156	79	6	demonstrates	demonstrate	VERB
fcis-30156	79	7	the	the	DET
fcis-30156	79	8	effectiveness	effectiveness	NOUN
fcis-30156	79	9	of	of	ADP
fcis-30156	79	10	both	both	DET
fcis-30156	79	11	individual	individual	ADJ
fcis-30156	79	12	modules	module	NOUN
fcis-30156	79	13	and	and	CCONJ
fcis-30156	79	14	the	the	DET
fcis-30156	79	15	fused	fuse	VERB
fcis-30156	79	16	modules	module	NOUN
fcis-30156	79	17	for	for	ADP
fcis-30156	79	18	the	the	DET
fcis-30156	79	19	improved	improve	VERB
fcis-30156	79	20	gepc	gepc	PRON
fcis-30156	79	21	network	network	NOUN
fcis-30156	79	22	model	model	NOUN
fcis-30156	79	23	.	.	PUNCT
fcis-30156	80	1	3.3	3.3	NUM
fcis-30156	80	2	.	.	PUNCT
fcis-30156	81	1	comparative	comparative	ADJ
fcis-30156	81	2	experiments	experiment	NOUN
fcis-30156	81	3	table	table	VERB
fcis-30156	81	4	2	2	NUM
fcis-30156	81	5	.	.	PUNCT
fcis-30156	81	6	comparative	comparative	ADJ
fcis-30156	81	7	experiment	experiment	NOUN
fcis-30156	81	8	results	result	NOUN
fcis-30156	81	9	method	method	VERB
fcis-30156	81	10	shanghaitech	shanghaitech	NOUN
fcis-30156	81	11	ntu	ntu	PROPN
fcis-30156	81	12	-	-	PUNCT
fcis-30156	81	13	rgb+d	rgb+d	PROPN
fcis-30156	81	14	pgm	pgm	NOUN
fcis-30156	82	1	[	[	X
fcis-30156	82	2	12	12	NUM
fcis-30156	82	3	]	]	PUNCT
fcis-30156	82	4	0.612	0.612	NUM
fcis-30156	82	5	astnet[13	astnet[13	NOUN
fcis-30156	82	6	]	]	PUNCT
fcis-30156	83	1	0.736	0.736	NUM
fcis-30156	83	2	proposed	propose	VERB
fcis-30156	83	3	method	method	NOUN
fcis-30156	83	4	0.764	0.764	NUM
fcis-30156	83	5	0.746	0.746	NUM
fcis-30156	83	6	from	from	ADP
fcis-30156	83	7	the	the	DET
fcis-30156	83	8	table	table	NOUN
fcis-30156	83	9	above	above	ADV
fcis-30156	83	10	,	,	PUNCT
fcis-30156	83	11	compared	compare	VERB
fcis-30156	83	12	to	to	ADP
fcis-30156	83	13	pgm	pgm	PROPN
fcis-30156	83	14	,	,	PUNCT
fcis-30156	83	15	which	which	PRON
fcis-30156	83	16	proposes	propose	VERB
fcis-30156	83	17	a	a	DET
fcis-30156	83	18	video	video	NOUN
fcis-30156	83	19	anomaly	anomaly	NOUN
fcis-30156	83	20	detection	detection	NOUN
fcis-30156	83	21	method	method	NOUN
fcis-30156	83	22	based	base	VERB
fcis-30156	83	23	on	on	ADP
fcis-30156	83	24	target	target	NOUN
fcis-30156	83	25	bounding	bounding	NOUN
fcis-30156	83	26	box	box	NOUN
fcis-30156	83	27	probability	probability	NOUN
fcis-30156	83	28	analysis	analysis	NOUN
fcis-30156	83	29	,	,	PUNCT
fcis-30156	83	30	improving	improve	VERB
fcis-30156	83	31	anonymity	anonymity	NOUN
fcis-30156	83	32	and	and	CCONJ
fcis-30156	83	33	computational	computational	ADJ
fcis-30156	83	34	efficiency	efficiency	NOUN
fcis-30156	83	35	by	by	ADP
fcis-30156	83	36	simplifying	simplify	VERB
fcis-30156	83	37	data	datum	NOUN
fcis-30156	83	38	representation	representation	NOUN
fcis-30156	83	39	and	and	CCONJ
fcis-30156	83	40	making	make	VERB
fcis-30156	83	41	it	it	PRON
fcis-30156	83	42	suitable	suitable	ADJ
fcis-30156	83	43	for	for	ADP
fcis-30156	83	44	edge	edge	NOUN
fcis-30156	83	45	devices	device	NOUN
fcis-30156	83	46	,	,	PUNCT
fcis-30156	83	47	and	and	CCONJ
fcis-30156	83	48	astnet	astnet	NOUN
fcis-30156	83	49	,	,	PUNCT
fcis-30156	83	50	which	which	PRON
fcis-30156	83	51	introduces	introduce	VERB
fcis-30156	83	52	a	a	DET
fcis-30156	83	53	spatio	spatio	NOUN
fcis-30156	83	54	-	-	PUNCT
fcis-30156	83	55	temporal	temporal	ADJ
fcis-30156	83	56	dual	dual	ADJ
fcis-30156	83	57	-	-	PUNCT
fcis-30156	83	58	branch	branch	NOUN
fcis-30156	83	59	residual	residual	ADJ
fcis-30156	83	60	autoencoder	autoencoder	NOUN
fcis-30156	83	61	network	network	NOUN
fcis-30156	83	62	achieving	achieve	VERB
fcis-30156	83	63	unsupervised	unsupervised	ADJ
fcis-30156	83	64	video	video	NOUN
fcis-30156	83	65	anomaly	anomaly	NOUN
fcis-30156	83	66	detection	detection	NOUN
fcis-30156	83	67	through	through	ADP
fcis-30156	83	68	channel	channel	NOUN
fcis-30156	83	69	attention	attention	NOUN
fcis-30156	83	70	modules	module	NOUN
fcis-30156	83	71	and	and	CCONJ
fcis-30156	83	72	temporal	temporal	ADJ
fcis-30156	83	73	shift	shift	NOUN
fcis-30156	83	74	methods	method	NOUN
fcis-30156	83	75	,	,	PUNCT
fcis-30156	83	76	the	the	DET
fcis-30156	83	77	hierarchically	hierarchically	ADV
fcis-30156	83	78	decomposed	decompose	VERB
fcis-30156	83	79	graph	graph	NOUN
fcis-30156	83	80	convolutional	convolutional	ADJ
fcis-30156	83	81	network	network	NOUN
fcis-30156	83	82	used	use	VERB
fcis-30156	83	83	in	in	ADP
fcis-30156	83	84	this	this	DET
fcis-30156	83	85	paper	paper	NOUN
fcis-30156	83	86	can	can	AUX
fcis-30156	83	87	extract	extract	VERB
fcis-30156	83	88	dynamic	dynamic	ADJ
fcis-30156	83	89	human	human	ADJ
fcis-30156	83	90	skeleton	skeleton	NOUN
fcis-30156	83	91	features	feature	VERB
fcis-30156	83	92	in	in	ADP
fcis-30156	83	93	a	a	DET
fcis-30156	83	94	fine	fine	ADV
fcis-30156	83	95	-	-	PUNCT
fcis-30156	83	96	grained	grain	VERB
fcis-30156	83	97	manner	manner	NOUN
fcis-30156	83	98	,	,	PUNCT
fcis-30156	83	99	leading	lead	VERB
fcis-30156	83	100	to	to	ADP
fcis-30156	83	101	improved	improved	ADJ
fcis-30156	83	102	accuracy	accuracy	NOUN
fcis-30156	83	103	in	in	ADP
fcis-30156	83	104	abnormal	abnormal	ADJ
fcis-30156	83	105	behavior	behavior	NOUN
fcis-30156	83	106	recognition	recognition	NOUN
fcis-30156	83	107	on	on	ADP
fcis-30156	83	108	both	both	CCONJ
fcis-30156	83	109	the	the	DET
fcis-30156	83	110	shanghaitech	shanghaitech	NOUN
fcis-30156	83	111	and	and	CCONJ
fcis-30156	83	112	ntu	ntu	PROPN
fcis-30156	83	113	-	-	PUNCT
fcis-30156	83	114	rgb+d	rgb+d	PROPN
fcis-30156	83	115	datasets	dataset	NOUN
fcis-30156	83	116	.	.	PUNCT
fcis-30156	84	1	3.4	3.4	NUM
fcis-30156	84	2	.	.	PUNCT
fcis-30156	84	3	visualization	visualization	NOUN
fcis-30156	84	4	fig	fig	NOUN
fcis-30156	84	5	2	2	NUM
fcis-30156	84	6	.	.	PUNCT
fcis-30156	85	1	visualization	visualization	NOUN
fcis-30156	85	2	the	the	DET
fcis-30156	85	3	visualization	visualization	NOUN
fcis-30156	85	4	results	result	NOUN
fcis-30156	85	5	of	of	ADP
fcis-30156	85	6	the	the	DET
fcis-30156	85	7	model	model	NOUN
fcis-30156	85	8	on	on	ADP
fcis-30156	85	9	the	the	DET
fcis-30156	85	10	shanghaitech	shanghaitech	NOUN
fcis-30156	85	11	dataset	dataset	VERB
fcis-30156	85	12	are	be	AUX
fcis-30156	85	13	shown	show	VERB
fcis-30156	85	14	in	in	ADP
fcis-30156	85	15	figure	figure	NOUN
fcis-30156	85	16	2	2	NUM
fcis-30156	85	17	,	,	PUNCT
fcis-30156	85	18	where	where	SCONJ
fcis-30156	85	19	the	the	DET
fcis-30156	85	20	red	red	ADJ
fcis-30156	85	21	parts	part	NOUN
fcis-30156	85	22	represent	represent	VERB
fcis-30156	85	23	the	the	DET
fcis-30156	85	24	originally	originally	ADV
fcis-30156	85	25	annotated	annotate	VERB
fcis-30156	85	26	abnormal	abnormal	ADJ
fcis-30156	85	27	frames	frame	NOUN
fcis-30156	85	28	in	in	ADP
fcis-30156	85	29	the	the	DET
fcis-30156	85	30	dataset	dataset	NOUN
fcis-30156	85	31	,	,	PUNCT
fcis-30156	85	32	and	and	CCONJ
fcis-30156	85	33	the	the	DET
fcis-30156	85	34	blue	blue	ADJ
fcis-30156	85	35	line	line	NOUN
fcis-30156	85	36	represents	represent	VERB
fcis-30156	85	37	the	the	DET
fcis-30156	85	38	predicted	predict	VERB
fcis-30156	85	39	anomaly	anomaly	NOUN
fcis-30156	85	40	scores	score	NOUN
fcis-30156	85	41	of	of	ADP
fcis-30156	85	42	the	the	DET
fcis-30156	85	43	video	video	NOUN
fcis-30156	85	44	frames	frame	NOUN
fcis-30156	85	45	.	.	PUNCT
fcis-30156	86	1	the	the	DET
fcis-30156	86	2	visualization	visualization	NOUN
fcis-30156	86	3	results	result	NOUN
fcis-30156	86	4	demonstrate	demonstrate	VERB
fcis-30156	86	5	that	that	SCONJ
fcis-30156	86	6	the	the	DET
fcis-30156	86	7	model	model	NOUN
fcis-30156	86	8	assigns	assign	VERB
fcis-30156	86	9	higher	high	ADJ
fcis-30156	86	10	anomaly	anomaly	NOUN
fcis-30156	86	11	scores	score	NOUN
fcis-30156	86	12	to	to	ADP
fcis-30156	86	13	video	video	NOUN
fcis-30156	86	14	frames	frame	NOUN
fcis-30156	86	15	containing	contain	VERB
fcis-30156	86	16	abnormal	abnormal	ADJ
fcis-30156	86	17	behaviors	behavior	NOUN
fcis-30156	86	18	and	and	CCONJ
fcis-30156	86	19	lower	low	ADJ
fcis-30156	86	20	anomaly	anomaly	NOUN
fcis-30156	86	21	scores	score	NOUN
fcis-30156	86	22	to	to	ADP
fcis-30156	86	23	normal	normal	ADJ
fcis-30156	86	24	video	video	NOUN
fcis-30156	86	25	frames	frame	NOUN
fcis-30156	86	26	,	,	PUNCT
fcis-30156	86	27	proving	prove	VERB
fcis-30156	86	28	the	the	DET
fcis-30156	86	29	effectiveness	effectiveness	NOUN
fcis-30156	86	30	of	of	ADP
fcis-30156	86	31	the	the	DET
fcis-30156	86	32	model	model	NOUN
fcis-30156	86	33	.	.	PUNCT
fcis-30156	87	1	4	4	X
fcis-30156	87	2	.	.	X
fcis-30156	87	3	summary	summary	VERB
fcis-30156	87	4	the	the	DET
fcis-30156	87	5	proposed	propose	VERB
fcis-30156	87	6	method	method	NOUN
fcis-30156	87	7	improves	improve	VERB
fcis-30156	87	8	upon	upon	SCONJ
fcis-30156	87	9	the	the	DET
fcis-30156	87	10	gepc	gepc	ADJ
fcis-30156	87	11	model	model	NOUN
fcis-30156	87	12	by	by	ADP
fcis-30156	87	13	first	first	ADV
fcis-30156	87	14	enhancing	enhance	VERB
fcis-30156	87	15	the	the	DET
fcis-30156	87	16	residual	residual	ADJ
fcis-30156	87	17	modules	module	NOUN
fcis-30156	87	18	of	of	ADP
fcis-30156	87	19	the	the	DET
fcis-30156	87	20	backbone	backbone	NOUN
fcis-30156	87	21	network	network	NOUN
fcis-30156	87	22	resnet	resnet	VERB
fcis-30156	87	23	through	through	ADP
fcis-30156	87	24	the	the	DET
fcis-30156	87	25	introduction	introduction	NOUN
fcis-30156	87	26	of	of	ADP
fcis-30156	87	27	the	the	DET
fcis-30156	87	28	ssconv	ssconv	ADJ
fcis-30156	87	29	module	module	NOUN
fcis-30156	87	30	,	,	PUNCT
fcis-30156	87	31	thereby	thereby	ADV
fcis-30156	87	32	improving	improve	VERB
fcis-30156	87	33	the	the	DET
fcis-30156	87	34	model	model	NOUN
fcis-30156	87	35	's	's	PART
fcis-30156	87	36	ability	ability	NOUN
fcis-30156	87	37	to	to	PART
fcis-30156	87	38	express	express	VERB
fcis-30156	87	39	the	the	DET
fcis-30156	87	40	extracted	extract	VERB
fcis-30156	87	41	111	111	NUM
fcis-30156	87	42	human	human	ADJ
fcis-30156	87	43	dynamic	dynamic	ADJ
fcis-30156	87	44	skeleton	skeleton	NOUN
fcis-30156	87	45	features	feature	NOUN
fcis-30156	87	46	.	.	PUNCT
fcis-30156	88	1	then	then	ADV
fcis-30156	88	2	,	,	PUNCT
fcis-30156	88	3	by	by	ADP
fcis-30156	88	4	incorporating	incorporate	VERB
fcis-30156	88	5	a	a	DET
fcis-30156	88	6	hierarchically	hierarchically	ADV
fcis-30156	88	7	decomposed	decompose	VERB
fcis-30156	88	8	graph	graph	NOUN
fcis-30156	88	9	convolutional	convolutional	ADJ
fcis-30156	88	10	network	network	NOUN
fcis-30156	88	11	,	,	PUNCT
fcis-30156	88	12	multi	multi	ADJ
fcis-30156	88	13	-	-	ADJ
fcis-30156	88	14	granularity	granularity	NOUN
fcis-30156	88	15	extraction	extraction	NOUN
fcis-30156	88	16	of	of	ADP
fcis-30156	88	17	human	human	ADJ
fcis-30156	88	18	dynamic	dynamic	ADJ
fcis-30156	88	19	skeleton	skeleton	NOUN
fcis-30156	88	20	features	feature	NOUN
fcis-30156	88	21	is	be	AUX
fcis-30156	88	22	achieved	achieve	VERB
fcis-30156	88	23	,	,	PUNCT
fcis-30156	88	24	effectively	effectively	ADV
fcis-30156	88	25	enhancing	enhance	VERB
fcis-30156	88	26	the	the	DET
fcis-30156	88	27	expressive	expressive	ADJ
fcis-30156	88	28	capability	capability	NOUN
fcis-30156	88	29	of	of	ADP
fcis-30156	88	30	the	the	DET
fcis-30156	88	31	final	final	ADJ
fcis-30156	88	32	human	human	ADJ
fcis-30156	88	33	dynamic	dynamic	ADJ
fcis-30156	88	34	skeleton	skeleton	NOUN
fcis-30156	88	35	features	feature	NOUN
fcis-30156	88	36	.	.	PUNCT
fcis-30156	89	1	experiments	experiment	NOUN
fcis-30156	89	2	conducted	conduct	VERB
fcis-30156	89	3	on	on	ADP
fcis-30156	89	4	the	the	DET
fcis-30156	89	5	shanghaitech	shanghaitech	NOUN
fcis-30156	89	6	and	and	CCONJ
fcis-30156	89	7	nturgb+d	nturgb+d	ADJ
fcis-30156	89	8	datasets	dataset	NOUN
fcis-30156	89	9	demonstrate	demonstrate	VERB
fcis-30156	89	10	that	that	SCONJ
fcis-30156	89	11	the	the	DET
fcis-30156	89	12	proposed	propose	VERB
fcis-30156	89	13	method	method	NOUN
fcis-30156	89	14	significantly	significantly	ADV
fcis-30156	89	15	improves	improve	VERB
fcis-30156	89	16	the	the	DET
fcis-30156	89	17	detection	detection	NOUN
fcis-30156	89	18	accuracy	accuracy	NOUN
fcis-30156	89	19	of	of	ADP
fcis-30156	89	20	abnormal	abnormal	ADJ
fcis-30156	89	21	behavior	behavior	NOUN
fcis-30156	89	22	recognition	recognition	NOUN
fcis-30156	89	23	methods	method	NOUN
fcis-30156	89	24	.	.	PUNCT
fcis-30156	90	1	references	reference	NOUN
fcis-30156	90	2	[	[	X
fcis-30156	90	3	1	1	NUM
fcis-30156	90	4	]	]	PUNCT
fcis-30156	90	5	verma	verma	PROPN
fcis-30156	90	6	,	,	PUNCT
fcis-30156	90	7	kamal	kamal	PROPN
fcis-30156	90	8	kant	kant	PROPN
fcis-30156	90	9	,	,	PUNCT
fcis-30156	90	10	brij	brij	PROPN
fcis-30156	90	11	mohan	mohan	PROPN
fcis-30156	90	12	singh	singh	PROPN
fcis-30156	90	13	,	,	PUNCT
fcis-30156	90	14	and	and	CCONJ
fcis-30156	90	15	amit	amit	PROPN
fcis-30156	90	16	dixit	dixit	PROPN
fcis-30156	90	17	.	.	PUNCT
fcis-30156	91	1	"	"	PUNCT
fcis-30156	91	2	a	a	DET
fcis-30156	91	3	review	review	NOUN
fcis-30156	91	4	of	of	ADP
fcis-30156	91	5	supervised	supervised	ADJ
fcis-30156	91	6	and	and	CCONJ
fcis-30156	91	7	unsupervised	unsupervised	ADJ
fcis-30156	91	8	machine	machine	NOUN
fcis-30156	91	9	learning	learn	VERB
fcis-30156	91	10	techniques	technique	NOUN
fcis-30156	91	11	for	for	ADP
fcis-30156	91	12	suspicious	suspicious	ADJ
fcis-30156	91	13	behavior	behavior	NOUN
fcis-30156	91	14	recognition	recognition	NOUN
fcis-30156	91	15	in	in	ADP
fcis-30156	91	16	intelligent	intelligent	ADJ
fcis-30156	91	17	surveillance	surveillance	NOUN
fcis-30156	91	18	system	system	NOUN
fcis-30156	91	19	.	.	PUNCT
fcis-30156	91	20	"	"	PUNCT
fcis-30156	92	1	international	international	ADJ
fcis-30156	92	2	journal	journal	NOUN
fcis-30156	92	3	of	of	ADP
fcis-30156	92	4	information	information	NOUN
fcis-30156	92	5	technology	technology	NOUN
fcis-30156	92	6	14.1	14.1	NUM
fcis-30156	92	7	(	(	PUNCT
fcis-30156	92	8	2022	2022	NUM
fcis-30156	92	9	):	):	PUNCT
fcis-30156	92	10	397	397	NUM
fcis-30156	92	11	-	-	SYM
fcis-30156	92	12	410	410	NUM
fcis-30156	92	13	.	.	PUNCT
fcis-30156	93	1	[	[	X
fcis-30156	93	2	2	2	NUM
fcis-30156	93	3	]	]	X
fcis-30156	93	4	pogadadanda	pogadadanda	ADJ
fcis-30156	93	5	,	,	PUNCT
fcis-30156	93	6	vikas	vikas	NOUN
fcis-30156	93	7	,	,	PUNCT
fcis-30156	93	8	et	et	PROPN
fcis-30156	93	9	al	al	PROPN
fcis-30156	93	10	.	.	PUNCT
fcis-30156	94	1	"	"	PUNCT
fcis-30156	94	2	abnormal	abnormal	ADJ
fcis-30156	94	3	activity	activity	NOUN
fcis-30156	94	4	recognition	recognition	NOUN
fcis-30156	94	5	on	on	ADP
fcis-30156	94	6	surveillance	surveillance	NOUN
fcis-30156	94	7	:	:	PUNCT
fcis-30156	94	8	a	a	DET
fcis-30156	94	9	review	review	NOUN
fcis-30156	94	10	.	.	PUNCT
fcis-30156	94	11	"	"	PUNCT
fcis-30156	95	1	2023	2023	NUM
fcis-30156	95	2	third	third	ADJ
fcis-30156	95	3	international	international	ADJ
fcis-30156	95	4	conference	conference	NOUN
fcis-30156	95	5	on	on	ADP
fcis-30156	95	6	artificial	artificial	ADJ
fcis-30156	95	7	intelligence	intelligence	NOUN
fcis-30156	95	8	and	and	CCONJ
fcis-30156	95	9	smart	smart	ADJ
fcis-30156	95	10	energy	energy	NOUN
fcis-30156	95	11	(	(	PUNCT
fcis-30156	95	12	icais	icais	PROPN
fcis-30156	95	13	)	)	PUNCT
fcis-30156	95	14	.	.	PUNCT
fcis-30156	96	1	ieee	ieee	NOUN
fcis-30156	96	2	,	,	PUNCT
fcis-30156	96	3	2023	2023	NUM
fcis-30156	96	4	.	.	PUNCT
fcis-30156	97	1	[	[	X
fcis-30156	97	2	3	3	NUM
fcis-30156	97	3	]	]	X
fcis-30156	97	4	qiu	qiu	PROPN
fcis-30156	97	5	,	,	PUNCT
fcis-30156	97	6	yuting	yuting	NUM
fcis-30156	97	7	,	,	PUNCT
fcis-30156	97	8	james	james	PROPN
fcis-30156	97	9	meng	meng	PROPN
fcis-30156	97	10	,	,	PUNCT
fcis-30156	97	11	and	and	CCONJ
fcis-30156	97	12	b.	b.	PROPN
fcis-30156	97	13	j.	j.	PROPN
fcis-30156	97	14	j.	j.	PROPN
fcis-30156	97	15	i.	i.	PROPN
fcis-30156	97	16	li	li	PROPN
fcis-30156	97	17	.	.	PUNCT
fcis-30156	98	1	"	"	PUNCT
fcis-30156	98	2	automated	automate	VERB
fcis-30156	98	3	falls	fall	NOUN
fcis-30156	98	4	detection	detection	NOUN
fcis-30156	98	5	using	use	VERB
fcis-30156	98	6	visual	visual	ADJ
fcis-30156	98	7	anomaly	anomaly	NOUN
fcis-30156	98	8	detection	detection	NOUN
fcis-30156	98	9	and	and	CCONJ
fcis-30156	98	10	pose	pose	NOUN
fcis-30156	98	11	-	-	PUNCT
fcis-30156	98	12	based	base	VERB
fcis-30156	98	13	approaches	approach	NOUN
fcis-30156	98	14	:	:	PUNCT
fcis-30156	98	15	experimental	experimental	ADJ
fcis-30156	98	16	review	review	NOUN
fcis-30156	98	17	and	and	CCONJ
fcis-30156	98	18	evaluation	evaluation	NOUN
fcis-30156	98	19	.	.	PUNCT
fcis-30156	98	20	"	"	PUNCT
fcis-30156	99	1	j	j	PROPN
fcis-30156	99	2	issn	issn	PROPN
fcis-30156	99	3	2766	2766	NUM
fcis-30156	99	4	(	(	PUNCT
fcis-30156	99	5	2024	2024	NUM
fcis-30156	99	6	):	):	PUNCT
fcis-30156	99	7	2276	2276	NUM
fcis-30156	99	8	.	.	PUNCT
fcis-30156	100	1	[	[	X
fcis-30156	100	2	4	4	NUM
fcis-30156	100	3	]	]	X
fcis-30156	100	4	duong	duong	PROPN
fcis-30156	100	5	,	,	PUNCT
fcis-30156	100	6	huu	huu	PROPN
fcis-30156	100	7	-	-	PUNCT
fcis-30156	100	8	thanh	thanh	ADJ
fcis-30156	100	9	,	,	PUNCT
fcis-30156	100	10	viet	viet	PROPN
fcis-30156	100	11	-	-	PUNCT
fcis-30156	100	12	tuan	tuan	PROPN
fcis-30156	100	13	le	le	X
fcis-30156	100	14	,	,	PUNCT
fcis-30156	100	15	and	and	CCONJ
fcis-30156	100	16	vinh	vinh	PROPN
fcis-30156	100	17	truong	truong	PROPN
fcis-30156	100	18	hoang	hoang	PROPN
fcis-30156	100	19	.	.	PUNCT
fcis-30156	101	1	"	"	PUNCT
fcis-30156	101	2	deep	deep	ADJ
fcis-30156	101	3	learning	learning	NOUN
fcis-30156	101	4	-	-	PUNCT
fcis-30156	101	5	based	base	VERB
fcis-30156	101	6	anomaly	anomaly	NOUN
fcis-30156	101	7	detection	detection	NOUN
fcis-30156	101	8	in	in	ADP
fcis-30156	101	9	video	video	NOUN
fcis-30156	101	10	surveillance	surveillance	NOUN
fcis-30156	101	11	:	:	PUNCT
fcis-30156	101	12	a	a	DET
fcis-30156	101	13	survey	survey	NOUN
fcis-30156	101	14	.	.	PUNCT
fcis-30156	101	15	"	"	PUNCT
fcis-30156	101	16	sensors	sensor	NOUN
fcis-30156	101	17	23.11	23.11	NUM
fcis-30156	101	18	(	(	PUNCT
fcis-30156	101	19	2023	2023	NUM
fcis-30156	101	20	):	):	PUNCT
fcis-30156	101	21	5024	5024	NUM
fcis-30156	101	22	.	.	PUNCT
fcis-30156	102	1	[	[	X
fcis-30156	102	2	5	5	NUM
fcis-30156	102	3	]	]	PUNCT
fcis-30156	102	4	sato	sato	NOUN
fcis-30156	102	5	,	,	PUNCT
fcis-30156	102	6	fumiaki	fumiaki	NOUN
fcis-30156	102	7	,	,	PUNCT
fcis-30156	102	8	ryo	ryo	PROPN
fcis-30156	102	9	hachiuma	hachiuma	PROPN
fcis-30156	102	10	,	,	PUNCT
fcis-30156	102	11	and	and	CCONJ
fcis-30156	102	12	taiki	taiki	PROPN
fcis-30156	102	13	sekii	sekii	PROPN
fcis-30156	102	14	.	.	PUNCT
fcis-30156	103	1	"	"	PUNCT
fcis-30156	103	2	promptguided	promptguide	VERB
fcis-30156	103	3	zero	zero	NUM
fcis-30156	103	4	-	-	PUNCT
fcis-30156	103	5	shot	shot	NOUN
fcis-30156	103	6	anomaly	anomaly	NOUN
fcis-30156	103	7	action	action	NOUN
fcis-30156	103	8	recognition	recognition	NOUN
fcis-30156	103	9	using	use	VERB
fcis-30156	103	10	pretrained	pretraine	VERB
fcis-30156	103	11	deep	deep	ADJ
fcis-30156	103	12	skeleton	skeleton	NOUN
fcis-30156	103	13	features	feature	NOUN
fcis-30156	103	14	.	.	PUNCT
fcis-30156	103	15	"	"	PUNCT
fcis-30156	104	1	proceedings	proceeding	NOUN
fcis-30156	104	2	of	of	ADP
fcis-30156	104	3	the	the	DET
fcis-30156	104	4	ieee	ieee	NOUN
fcis-30156	104	5	/	/	SYM
fcis-30156	104	6	cvf	cvf	NOUN
fcis-30156	104	7	conference	conference	NOUN
fcis-30156	104	8	on	on	ADP
fcis-30156	104	9	computer	computer	NOUN
fcis-30156	104	10	vision	vision	NOUN
fcis-30156	104	11	and	and	CCONJ
fcis-30156	104	12	pattern	pattern	NOUN
fcis-30156	104	13	recognition	recognition	NOUN
fcis-30156	104	14	.	.	PUNCT
fcis-30156	105	1	2023	2023	NUM
fcis-30156	105	2	.	.	PUNCT
fcis-30156	106	1	[	[	X
fcis-30156	106	2	6	6	NUM
fcis-30156	106	3	]	]	X
fcis-30156	106	4	cho	cho	PROPN
fcis-30156	106	5	,	,	PUNCT
fcis-30156	106	6	myeongah	myeongah	PROPN
fcis-30156	106	7	,	,	PUNCT
fcis-30156	106	8	et	et	PROPN
fcis-30156	106	9	al	al	PROPN
fcis-30156	106	10	.	.	PUNCT
fcis-30156	107	1	"	"	PUNCT
fcis-30156	107	2	unsupervised	unsupervised	ADJ
fcis-30156	107	3	video	video	NOUN
fcis-30156	107	4	anomaly	anomaly	NOUN
fcis-30156	107	5	detection	detection	NOUN
fcis-30156	107	6	via	via	ADP
fcis-30156	107	7	normalizing	normalizing	ADJ
fcis-30156	107	8	flows	flow	NOUN
fcis-30156	107	9	with	with	ADP
fcis-30156	107	10	implicit	implicit	ADJ
fcis-30156	107	11	latent	latent	NOUN
fcis-30156	107	12	features	feature	NOUN
fcis-30156	107	13	.	.	PUNCT
fcis-30156	107	14	"	"	PUNCT
fcis-30156	107	15	pattern	pattern	NOUN
fcis-30156	107	16	recognition	recognition	NOUN
fcis-30156	107	17	129	129	NUM
fcis-30156	107	18	(	(	PUNCT
fcis-30156	107	19	2022	2022	NUM
fcis-30156	107	20	):	):	PUNCT
fcis-30156	107	21	108703	108703	NUM
fcis-30156	107	22	.	.	PUNCT
fcis-30156	108	1	[	[	X
fcis-30156	108	2	7	7	NUM
fcis-30156	108	3	]	]	PUNCT
fcis-30156	108	4	coşar	coşar	ADJ
fcis-30156	108	5	,	,	PUNCT
fcis-30156	108	6	serhan	serhan	PROPN
fcis-30156	108	7	,	,	PUNCT
fcis-30156	108	8	et	et	PROPN
fcis-30156	108	9	al	al	PROPN
fcis-30156	108	10	.	.	PUNCT
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fcis-30156	112	1	ieee	ieee	PROPN
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fcis-30156	112	11	.	.	PUNCT
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