id	sid	tid	token	lemma	pos
fcis-12004	1	1	frontiers	frontier	NOUN
fcis-12004	1	2	in	in	ADP
fcis-12004	1	3	computing	computing	NOUN
fcis-12004	1	4	and	and	CCONJ
fcis-12004	1	5	intelligent	intelligent	ADJ
fcis-12004	1	6	systems	system	NOUN
fcis-12004	1	7	issn	issn	VERB
fcis-12004	1	8	:	:	PUNCT
fcis-12004	1	9	2832	2832	NUM
fcis-12004	1	10	-	-	SYM
fcis-12004	1	11	6024	6024	NUM
fcis-12004	1	12	|	|	NOUN
fcis-12004	1	13	vol	vol	NOUN
fcis-12004	1	14	.	.	PROPN
fcis-12004	2	1	5	5	NUM
fcis-12004	2	2	,	,	PUNCT
fcis-12004	2	3	no	no	INTJ
fcis-12004	2	4	.	.	NOUN
fcis-12004	2	5	1	1	NUM
fcis-12004	2	6	,	,	PUNCT
fcis-12004	2	7	2023	2023	NUM
fcis-12004	2	8	103	103	NUM
fcis-12004	2	9	research	research	NOUN
fcis-12004	2	10	on	on	ADP
fcis-12004	2	11	video	video	NOUN
fcis-12004	2	12	detection	detection	NOUN
fcis-12004	2	13	method	method	NOUN
fcis-12004	2	14	of	of	ADP
fcis-12004	2	15	mudslide	mudslide	NOUN
fcis-12004	2	16	based	base	VERB
fcis-12004	2	17	on	on	ADP
fcis-12004	2	18	inflated	inflated	ADJ
fcis-12004	2	19	3d	3d	NUM
fcis-12004	2	20	convolutional	convolutional	ADJ
fcis-12004	2	21	neural	neural	ADJ
fcis-12004	2	22	network	network	NOUN
fcis-12004	2	23	zefeng	zefeng	PROPN
fcis-12004	2	24	yu	yu	PROPN
fcis-12004	2	25	*	*	PROPN
fcis-12004	2	26	chengdu	chengdu	PROPN
fcis-12004	2	27	university	university	PROPN
fcis-12004	2	28	of	of	ADP
fcis-12004	2	29	technology	technology	PROPN
fcis-12004	2	30	,	,	PUNCT
fcis-12004	2	31	chengdu	chengdu	PROPN
fcis-12004	2	32	,	,	PUNCT
fcis-12004	2	33	china	china	PROPN
fcis-12004	2	34	*	*	PUNCT
fcis-12004	2	35	corresponding	correspond	VERB
fcis-12004	2	36	author	author	NOUN
fcis-12004	2	37	email	email	NOUN
fcis-12004	2	38	:	:	PUNCT
fcis-12004	2	39	yuzefeng2021@163.com	yuzefeng2021@163.com	X
fcis-12004	2	40	abstract	abstract	NOUN
fcis-12004	2	41	:	:	PUNCT
fcis-12004	3	1	mudslide	mudslide	PROPN
fcis-12004	3	2	is	be	AUX
fcis-12004	3	3	a	a	DET
fcis-12004	3	4	common	common	ADJ
fcis-12004	3	5	natural	natural	ADJ
fcis-12004	3	6	disaster	disaster	NOUN
fcis-12004	3	7	in	in	ADP
fcis-12004	3	8	mountainous	mountainous	ADJ
fcis-12004	3	9	areas	area	NOUN
fcis-12004	3	10	,	,	PUNCT
fcis-12004	3	11	causing	cause	VERB
fcis-12004	3	12	harm	harm	NOUN
fcis-12004	3	13	to	to	ADP
fcis-12004	3	14	roads	road	NOUN
fcis-12004	3	15	and	and	CCONJ
fcis-12004	3	16	railroads	railroad	NOUN
fcis-12004	3	17	and	and	CCONJ
fcis-12004	3	18	structures	structure	NOUN
fcis-12004	3	19	.	.	PUNCT
fcis-12004	4	1	to	to	PART
fcis-12004	4	2	address	address	VERB
fcis-12004	4	3	this	this	DET
fcis-12004	4	4	problem	problem	NOUN
fcis-12004	4	5	,	,	PUNCT
fcis-12004	4	6	this	this	DET
fcis-12004	4	7	paper	paper	NOUN
fcis-12004	4	8	adopts	adopt	VERB
fcis-12004	4	9	the	the	DET
fcis-12004	4	10	automatic	automatic	ADJ
fcis-12004	4	11	video	video	NOUN
fcis-12004	4	12	recognition	recognition	NOUN
fcis-12004	4	13	approach	approach	NOUN
fcis-12004	4	14	,	,	PUNCT
fcis-12004	4	15	which	which	PRON
fcis-12004	4	16	utilizes	utilize	VERB
fcis-12004	4	17	widely	widely	ADV
fcis-12004	4	18	installed	instal	VERB
fcis-12004	4	19	video	video	NOUN
fcis-12004	4	20	surveillance	surveillance	NOUN
fcis-12004	4	21	equipment	equipment	NOUN
fcis-12004	4	22	to	to	PART
fcis-12004	4	23	detect	detect	VERB
fcis-12004	4	24	the	the	DET
fcis-12004	4	25	changes	change	NOUN
fcis-12004	4	26	of	of	ADP
fcis-12004	4	27	mudslides	mudslide	NOUN
fcis-12004	4	28	,	,	PUNCT
fcis-12004	4	29	so	so	SCONJ
fcis-12004	4	30	as	as	SCONJ
fcis-12004	4	31	to	to	PART
fcis-12004	4	32	identify	identify	VERB
fcis-12004	4	33	the	the	DET
fcis-12004	4	34	mudslide	mudslide	NOUN
fcis-12004	4	35	diffuse	diffuse	NOUN
fcis-12004	4	36	flow	flow	NOUN
fcis-12004	4	37	disaster	disaster	NOUN
fcis-12004	4	38	in	in	ADP
fcis-12004	4	39	the	the	DET
fcis-12004	4	40	video	video	NOUN
fcis-12004	4	41	monitoring	monitoring	NOUN
fcis-12004	4	42	area	area	NOUN
fcis-12004	4	43	to	to	PART
fcis-12004	4	44	achieve	achieve	VERB
fcis-12004	4	45	early	early	ADJ
fcis-12004	4	46	warning	warning	NOUN
fcis-12004	4	47	.	.	PUNCT
fcis-12004	5	1	firstly	firstly	ADV
fcis-12004	5	2	,	,	PUNCT
fcis-12004	5	3	the	the	DET
fcis-12004	5	4	deep	deep	ADJ
fcis-12004	5	5	learning	learning	NOUN
fcis-12004	5	6	model	model	NOUN
fcis-12004	5	7	is	be	AUX
fcis-12004	5	8	trained	train	VERB
fcis-12004	5	9	with	with	ADP
fcis-12004	5	10	weakly	weakly	ADJ
fcis-12004	5	11	labeled	label	VERB
fcis-12004	5	12	mudslide	mudslide	ADJ
fcis-12004	5	13	video	video	NOUN
fcis-12004	5	14	files	file	NOUN
fcis-12004	5	15	,	,	PUNCT
fcis-12004	5	16	and	and	CCONJ
fcis-12004	5	17	the	the	DET
fcis-12004	5	18	spatio	spatio	ADJ
fcis-12004	5	19	-	-	PUNCT
fcis-12004	5	20	temporal	temporal	ADJ
fcis-12004	5	21	feature	feature	NOUN
fcis-12004	5	22	learning	learning	NOUN
fcis-12004	5	23	method	method	NOUN
fcis-12004	5	24	,	,	PUNCT
fcis-12004	5	25	i.e.	i.e.	X
fcis-12004	5	26	,	,	PUNCT
fcis-12004	5	27	inflated	inflate	VERB
fcis-12004	5	28	3d	3d	NUM
fcis-12004	5	29	convolutional	convolutional	ADJ
fcis-12004	5	30	network	network	NOUN
fcis-12004	5	31	,	,	PUNCT
fcis-12004	5	32	is	be	AUX
fcis-12004	5	33	combined	combine	VERB
fcis-12004	5	34	in	in	ADP
fcis-12004	5	35	the	the	DET
fcis-12004	5	36	model	model	NOUN
fcis-12004	5	37	,	,	PUNCT
fcis-12004	5	38	which	which	PRON
fcis-12004	5	39	results	result	VERB
fcis-12004	5	40	in	in	ADP
fcis-12004	5	41	a	a	DET
fcis-12004	5	42	higher	high	ADJ
fcis-12004	5	43	correctness	correctness	NOUN
fcis-12004	5	44	rate	rate	NOUN
fcis-12004	5	45	of	of	ADP
fcis-12004	5	46	training	training	NOUN
fcis-12004	5	47	and	and	CCONJ
fcis-12004	5	48	detection	detection	NOUN
fcis-12004	5	49	;	;	PUNCT
fcis-12004	5	50	secondly	secondly	ADV
fcis-12004	5	51	,	,	PUNCT
fcis-12004	5	52	the	the	DET
fcis-12004	5	53	model	model	NOUN
fcis-12004	5	54	is	be	AUX
fcis-12004	5	55	shown	show	VERB
fcis-12004	5	56	to	to	PART
fcis-12004	5	57	have	have	VERB
fcis-12004	5	58	a	a	DET
fcis-12004	5	59	recognition	recognition	NOUN
fcis-12004	5	60	accuracy	accuracy	NOUN
fcis-12004	5	61	of	of	ADP
fcis-12004	5	62	86	86	NUM
fcis-12004	5	63	%	%	NOUN
fcis-12004	5	64	through	through	ADP
fcis-12004	5	65	the	the	DET
fcis-12004	5	66	testing	testing	NOUN
fcis-12004	5	67	of	of	ADP
fcis-12004	5	68	the	the	DET
fcis-12004	5	69	relevant	relevant	ADJ
fcis-12004	5	70	datasets	dataset	NOUN
fcis-12004	5	71	,	,	PUNCT
fcis-12004	5	72	which	which	PRON
fcis-12004	5	73	can	can	AUX
fcis-12004	5	74	be	be	AUX
fcis-12004	5	75	used	use	VERB
fcis-12004	5	76	as	as	ADP
fcis-12004	5	77	an	an	DET
fcis-12004	5	78	effective	effective	ADJ
fcis-12004	5	79	complement	complement	NOUN
fcis-12004	5	80	to	to	ADP
fcis-12004	5	81	the	the	DET
fcis-12004	5	82	traditional	traditional	ADJ
fcis-12004	5	83	method	method	NOUN
fcis-12004	5	84	of	of	ADP
fcis-12004	5	85	mudslide	mudslide	ADJ
fcis-12004	5	86	early	early	ADJ
fcis-12004	5	87	warning	warning	NOUN
fcis-12004	5	88	.	.	PUNCT
fcis-12004	6	1	keywords	keyword	NOUN
fcis-12004	6	2	:	:	PUNCT
fcis-12004	6	3	debris	debris	NOUN
fcis-12004	6	4	flow	flow	NOUN
fcis-12004	6	5	transformation	transformation	NOUN
fcis-12004	6	6	;	;	PUNCT
fcis-12004	6	7	weak	weak	ADJ
fcis-12004	6	8	labeling	labeling	NOUN
fcis-12004	6	9	;	;	PUNCT
fcis-12004	6	10	convolutional	convolutional	ADJ
fcis-12004	6	11	networks	network	NOUN
fcis-12004	6	12	.	.	PUNCT
fcis-12004	7	1	1	1	X
fcis-12004	7	2	.	.	X
fcis-12004	7	3	introduction	introduction	NOUN
fcis-12004	7	4	a	a	DET
fcis-12004	7	5	mudslide	mudslide	NOUN
fcis-12004	7	6	is	be	AUX
fcis-12004	7	7	a	a	DET
fcis-12004	7	8	natural	natural	ADJ
fcis-12004	7	9	disaster	disaster	NOUN
fcis-12004	7	10	that	that	PRON
fcis-12004	7	11	occurs	occur	VERB
fcis-12004	7	12	mainly	mainly	ADV
fcis-12004	7	13	in	in	ADP
fcis-12004	7	14	mountainous	mountainous	ADJ
fcis-12004	7	15	areas	area	NOUN
fcis-12004	7	16	,	,	PUNCT
fcis-12004	7	17	on	on	ADP
fcis-12004	7	18	slopes	slope	NOUN
fcis-12004	7	19	or	or	CCONJ
fcis-12004	7	20	in	in	ADP
fcis-12004	7	21	areas	area	NOUN
fcis-12004	7	22	following	follow	VERB
fcis-12004	7	23	volcanic	volcanic	ADJ
fcis-12004	7	24	eruptions	eruption	NOUN
fcis-12004	7	25	.	.	PUNCT
fcis-12004	8	1	it	it	PRON
fcis-12004	8	2	consists	consist	VERB
fcis-12004	8	3	of	of	ADP
fcis-12004	8	4	a	a	DET
fcis-12004	8	5	fluid	fluid	ADJ
fcis-12004	8	6	flow	flow	NOUN
fcis-12004	8	7	of	of	ADP
fcis-12004	8	8	large	large	ADJ
fcis-12004	8	9	quantities	quantity	NOUN
fcis-12004	8	10	of	of	ADP
fcis-12004	8	11	rain	rain	NOUN
fcis-12004	8	12	,	,	PUNCT
fcis-12004	8	13	snowmelt	snowmelt	NOUN
fcis-12004	8	14	or	or	CCONJ
fcis-12004	8	15	melted	melt	VERB
fcis-12004	8	16	glacial	glacial	ADJ
fcis-12004	8	17	water	water	NOUN
fcis-12004	8	18	,	,	PUNCT
fcis-12004	8	19	as	as	ADV
fcis-12004	8	20	well	well	ADV
fcis-12004	8	21	as	as	ADP
fcis-12004	8	22	a	a	DET
fcis-12004	8	23	mixture	mixture	NOUN
fcis-12004	8	24	of	of	ADP
fcis-12004	8	25	sediment	sediment	NOUN
fcis-12004	8	26	and	and	CCONJ
fcis-12004	8	27	rocks	rock	NOUN
fcis-12004	8	28	,	,	PUNCT
fcis-12004	8	29	which	which	PRON
fcis-12004	8	30	creates	create	VERB
fcis-12004	8	31	a	a	DET
fcis-12004	8	32	destructive	destructive	ADJ
fcis-12004	8	33	and	and	CCONJ
fcis-12004	8	34	powerful	powerful	ADJ
fcis-12004	8	35	shock	shock	NOUN
fcis-12004	8	36	wave	wave	NOUN
fcis-12004	8	37	.	.	PUNCT
fcis-12004	9	1	mudslides	mudslide	NOUN
fcis-12004	9	2	occur	occur	VERB
fcis-12004	9	3	globally	globally	ADV
fcis-12004	9	4	,	,	PUNCT
fcis-12004	9	5	but	but	CCONJ
fcis-12004	9	6	especially	especially	ADV
fcis-12004	9	7	in	in	ADP
fcis-12004	9	8	mountainous	mountainous	ADJ
fcis-12004	9	9	areas	area	NOUN
fcis-12004	9	10	,	,	PUNCT
fcis-12004	9	11	where	where	SCONJ
fcis-12004	9	12	they	they	PRON
fcis-12004	9	13	have	have	VERB
fcis-12004	9	14	a	a	DET
fcis-12004	9	15	major	major	ADJ
fcis-12004	9	16	impact	impact	NOUN
fcis-12004	9	17	on	on	ADP
fcis-12004	9	18	people	people	NOUN
fcis-12004	9	19	,	,	PUNCT
fcis-12004	9	20	infrastructure	infrastructure	NOUN
fcis-12004	9	21	and	and	CCONJ
fcis-12004	9	22	ecosystems	ecosystem	NOUN
fcis-12004	9	23	[	[	X
fcis-12004	9	24	1	1	NUM
fcis-12004	9	25	]	]	PUNCT
fcis-12004	9	26	.	.	PUNCT
fcis-12004	10	1	mudslides	mudslide	NOUN
fcis-12004	10	2	occur	occur	VERB
fcis-12004	10	3	primarily	primarily	ADV
fcis-12004	10	4	when	when	SCONJ
fcis-12004	10	5	large	large	ADJ
fcis-12004	10	6	amounts	amount	NOUN
fcis-12004	10	7	of	of	ADP
fcis-12004	10	8	rainfall	rainfall	NOUN
fcis-12004	10	9	,	,	PUNCT
fcis-12004	10	10	snowmelt	snowmelt	PROPN
fcis-12004	10	11	,	,	PUNCT
fcis-12004	10	12	or	or	CCONJ
fcis-12004	10	13	glacial	glacial	ADJ
fcis-12004	10	14	meltwater	meltwater	NOUN
fcis-12004	10	15	cause	cause	VERB
fcis-12004	10	16	soil	soil	NOUN
fcis-12004	10	17	and	and	CCONJ
fcis-12004	10	18	rock	rock	NOUN
fcis-12004	10	19	on	on	ADP
fcis-12004	10	20	mountain	mountain	NOUN
fcis-12004	10	21	slopes	slope	NOUN
fcis-12004	10	22	to	to	PART
fcis-12004	10	23	become	become	VERB
fcis-12004	10	24	saturated	saturated	ADJ
fcis-12004	10	25	and	and	CCONJ
fcis-12004	10	26	lose	lose	VERB
fcis-12004	10	27	their	their	PRON
fcis-12004	10	28	support	support	NOUN
fcis-12004	10	29	.	.	PUNCT
fcis-12004	11	1	when	when	SCONJ
fcis-12004	11	2	the	the	DET
fcis-12004	11	3	amount	amount	NOUN
fcis-12004	11	4	of	of	ADP
fcis-12004	11	5	standing	stand	VERB
fcis-12004	11	6	water	water	NOUN
fcis-12004	11	7	exceeds	exceed	VERB
fcis-12004	11	8	the	the	DET
fcis-12004	11	9	waterholding	waterholde	VERB
fcis-12004	11	10	capacity	capacity	NOUN
fcis-12004	11	11	of	of	ADP
fcis-12004	11	12	the	the	DET
fcis-12004	11	13	soil	soil	NOUN
fcis-12004	11	14	,	,	PUNCT
fcis-12004	11	15	the	the	DET
fcis-12004	11	16	soil	soil	NOUN
fcis-12004	11	17	becomes	become	VERB
fcis-12004	11	18	loose	loose	ADJ
fcis-12004	11	19	and	and	CCONJ
fcis-12004	11	20	joins	join	VERB
fcis-12004	11	21	with	with	ADP
fcis-12004	11	22	rocks	rock	NOUN
fcis-12004	11	23	and	and	CCONJ
fcis-12004	11	24	other	other	ADJ
fcis-12004	11	25	debris	debris	NOUN
fcis-12004	11	26	to	to	PART
fcis-12004	11	27	form	form	VERB
fcis-12004	11	28	fluid	fluid	ADJ
fcis-12004	11	29	-	-	PUNCT
fcis-12004	11	30	like	like	ADJ
fcis-12004	11	31	mudslides	mudslide	NOUN
fcis-12004	11	32	.	.	PUNCT
fcis-12004	12	1	in	in	ADP
fcis-12004	12	2	addition	addition	NOUN
fcis-12004	12	3	,	,	PUNCT
fcis-12004	12	4	natural	natural	ADJ
fcis-12004	12	5	disasters	disaster	NOUN
fcis-12004	12	6	such	such	ADJ
fcis-12004	12	7	as	as	ADP
fcis-12004	12	8	earthquakes	earthquake	NOUN
fcis-12004	12	9	,	,	PUNCT
fcis-12004	12	10	volcanic	volcanic	ADJ
fcis-12004	12	11	eruptions	eruption	NOUN
fcis-12004	12	12	,	,	PUNCT
fcis-12004	12	13	or	or	CCONJ
fcis-12004	12	14	glacial	glacial	ADJ
fcis-12004	12	15	lake	lake	NOUN
fcis-12004	12	16	outbursts	outburst	NOUN
fcis-12004	12	17	can	can	AUX
fcis-12004	12	18	cause	cause	VERB
fcis-12004	12	19	mudslides	mudslide	NOUN
fcis-12004	12	20	.	.	PUNCT
fcis-12004	13	1	a	a	DET
fcis-12004	13	2	mudslide	mudslide	NOUN
fcis-12004	13	3	is	be	AUX
fcis-12004	13	4	a	a	DET
fcis-12004	13	5	devastating	devastating	ADJ
fcis-12004	13	6	natural	natural	ADJ
fcis-12004	13	7	disaster	disaster	NOUN
fcis-12004	13	8	and	and	CCONJ
fcis-12004	13	9	the	the	DET
fcis-12004	13	10	damage	damage	NOUN
fcis-12004	13	11	caused	cause	VERB
fcis-12004	13	12	is	be	AUX
fcis-12004	13	13	often	often	ADV
fcis-12004	13	14	enormous	enormous	ADJ
fcis-12004	13	15	[	[	X
fcis-12004	13	16	2	2	NUM
fcis-12004	13	17	]	]	PUNCT
fcis-12004	13	18	.	.	PUNCT
fcis-12004	14	1	it	it	PRON
fcis-12004	14	2	can	can	AUX
fcis-12004	14	3	destroy	destroy	VERB
fcis-12004	14	4	homes	home	NOUN
fcis-12004	14	5	,	,	PUNCT
fcis-12004	14	6	roads	road	NOUN
fcis-12004	14	7	and	and	CCONJ
fcis-12004	14	8	bridges	bridge	NOUN
fcis-12004	14	9	,	,	PUNCT
fcis-12004	14	10	leading	lead	VERB
fcis-12004	14	11	to	to	ADP
fcis-12004	14	12	loss	loss	NOUN
fcis-12004	14	13	of	of	ADP
fcis-12004	14	14	life	life	NOUN
fcis-12004	14	15	and	and	CCONJ
fcis-12004	14	16	property	property	NOUN
fcis-12004	14	17	.	.	PUNCT
fcis-12004	15	1	at	at	ADP
fcis-12004	15	2	the	the	DET
fcis-12004	15	3	same	same	ADJ
fcis-12004	15	4	time	time	NOUN
fcis-12004	15	5	,	,	PUNCT
fcis-12004	15	6	mudslides	mudslide	NOUN
fcis-12004	15	7	can	can	AUX
fcis-12004	15	8	block	block	VERB
fcis-12004	15	9	rivers	river	NOUN
fcis-12004	15	10	,	,	PUNCT
fcis-12004	15	11	lakes	lake	NOUN
fcis-12004	15	12	and	and	CCONJ
fcis-12004	15	13	estuaries	estuary	NOUN
fcis-12004	15	14	,	,	PUNCT
fcis-12004	15	15	creating	create	VERB
fcis-12004	15	16	mud	mud	NOUN
fcis-12004	15	17	and	and	CCONJ
fcis-12004	15	18	rock	rock	NOUN
fcis-12004	15	19	accumulations	accumulation	NOUN
fcis-12004	15	20	that	that	PRON
fcis-12004	15	21	can	can	AUX
fcis-12004	15	22	exacerbate	exacerbate	VERB
fcis-12004	15	23	the	the	DET
fcis-12004	15	24	risk	risk	NOUN
fcis-12004	15	25	of	of	ADP
fcis-12004	15	26	flooding	flooding	NOUN
fcis-12004	15	27	and	and	CCONJ
fcis-12004	15	28	inundation	inundation	NOUN
fcis-12004	15	29	.	.	PUNCT
fcis-12004	16	1	in	in	ADP
fcis-12004	16	2	addition	addition	NOUN
fcis-12004	16	3	,	,	PUNCT
fcis-12004	16	4	mudslides	mudslide	NOUN
fcis-12004	16	5	can	can	AUX
fcis-12004	16	6	damage	damage	VERB
fcis-12004	16	7	farmland	farmland	NOUN
fcis-12004	16	8	,	,	PUNCT
fcis-12004	16	9	forests	forest	NOUN
fcis-12004	16	10	,	,	PUNCT
fcis-12004	16	11	and	and	CCONJ
fcis-12004	16	12	wildlife	wildlife	NOUN
fcis-12004	16	13	habitat	habitat	NOUN
fcis-12004	16	14	,	,	PUNCT
fcis-12004	16	15	causing	cause	VERB
fcis-12004	16	16	long	long	ADJ
fcis-12004	16	17	-	-	PUNCT
fcis-12004	16	18	term	term	NOUN
fcis-12004	16	19	impacts	impact	NOUN
fcis-12004	16	20	on	on	ADP
fcis-12004	16	21	ecosystems	ecosystem	NOUN
fcis-12004	16	22	.	.	PUNCT
fcis-12004	17	1	in	in	ADP
fcis-12004	17	2	order	order	NOUN
fcis-12004	17	3	to	to	PART
fcis-12004	17	4	mitigate	mitigate	VERB
fcis-12004	17	5	the	the	DET
fcis-12004	17	6	impacts	impact	NOUN
fcis-12004	17	7	of	of	ADP
fcis-12004	17	8	mudslides	mudslide	NOUN
fcis-12004	17	9	,	,	PUNCT
fcis-12004	17	10	many	many	ADJ
fcis-12004	17	11	regions	region	NOUN
fcis-12004	17	12	have	have	AUX
fcis-12004	17	13	adopted	adopt	VERB
fcis-12004	17	14	a	a	DET
fcis-12004	17	15	range	range	NOUN
fcis-12004	17	16	of	of	ADP
fcis-12004	17	17	preventive	preventive	ADJ
fcis-12004	17	18	measures	measure	NOUN
fcis-12004	17	19	.	.	PUNCT
fcis-12004	18	1	these	these	DET
fcis-12004	18	2	measures	measure	NOUN
fcis-12004	18	3	include	include	VERB
fcis-12004	18	4	establishing	establish	VERB
fcis-12004	18	5	mudslide	mudslide	ADJ
fcis-12004	18	6	monitoring	monitor	VERB
fcis-12004	18	7	systems	system	NOUN
fcis-12004	18	8	to	to	PART
fcis-12004	18	9	monitor	monitor	VERB
fcis-12004	18	10	the	the	DET
fcis-12004	18	11	occurrence	occurrence	NOUN
fcis-12004	18	12	and	and	CCONJ
fcis-12004	18	13	evolution	evolution	NOUN
fcis-12004	18	14	of	of	ADP
fcis-12004	18	15	geologic	geologic	ADJ
fcis-12004	18	16	hazards	hazard	NOUN
fcis-12004	18	17	in	in	ADP
fcis-12004	18	18	real	real	ADJ
fcis-12004	18	19	time	time	NOUN
fcis-12004	18	20	and	and	CCONJ
fcis-12004	18	21	send	send	VERB
fcis-12004	18	22	out	out	ADP
fcis-12004	18	23	early	early	ADJ
fcis-12004	18	24	warning	warning	NOUN
fcis-12004	18	25	messages	message	NOUN
fcis-12004	18	26	;	;	PUNCT
fcis-12004	18	27	reducing	reduce	VERB
fcis-12004	18	28	the	the	DET
fcis-12004	18	29	impact	impact	NOUN
fcis-12004	18	30	of	of	ADP
fcis-12004	18	31	mudslide	mudslide	ADJ
fcis-12004	18	32	impacts	impact	NOUN
fcis-12004	18	33	through	through	ADP
fcis-12004	18	34	engineering	engineering	NOUN
fcis-12004	18	35	measures	measure	NOUN
fcis-12004	18	36	such	such	ADJ
fcis-12004	18	37	as	as	ADP
fcis-12004	18	38	the	the	DET
fcis-12004	18	39	construction	construction	NOUN
fcis-12004	18	40	of	of	ADP
fcis-12004	18	41	protective	protective	ADJ
fcis-12004	18	42	dikes	dike	NOUN
fcis-12004	18	43	,	,	PUNCT
fcis-12004	18	44	embankments	embankment	NOUN
fcis-12004	18	45	,	,	PUNCT
fcis-12004	18	46	and	and	CCONJ
fcis-12004	18	47	berms	berm	NOUN
fcis-12004	18	48	;	;	PUNCT
fcis-12004	18	49	reinforcing	reinforce	VERB
fcis-12004	18	50	slopes	slope	NOUN
fcis-12004	18	51	to	to	PART
fcis-12004	18	52	reduce	reduce	VERB
fcis-12004	18	53	the	the	DET
fcis-12004	18	54	formation	formation	NOUN
fcis-12004	18	55	of	of	ADP
fcis-12004	18	56	mudslides	mudslide	NOUN
fcis-12004	18	57	through	through	ADP
fcis-12004	18	58	the	the	DET
fcis-12004	18	59	restoration	restoration	NOUN
fcis-12004	18	60	of	of	ADP
fcis-12004	18	61	vegetation	vegetation	NOUN
fcis-12004	18	62	and	and	CCONJ
fcis-12004	18	63	ecological	ecological	ADJ
fcis-12004	18	64	remediation	remediation	NOUN
fcis-12004	18	65	;	;	PUNCT
fcis-12004	18	66	avoiding	avoid	VERB
fcis-12004	18	67	the	the	DET
fcis-12004	18	68	construction	construction	NOUN
fcis-12004	18	69	of	of	ADP
fcis-12004	18	70	residential	residential	ADJ
fcis-12004	18	71	areas	area	NOUN
fcis-12004	18	72	or	or	CCONJ
fcis-12004	18	73	important	important	ADJ
fcis-12004	18	74	infrastructures	infrastructure	NOUN
fcis-12004	18	75	in	in	ADP
fcis-12004	18	76	areas	area	NOUN
fcis-12004	18	77	with	with	ADP
fcis-12004	18	78	high	high	ADJ
fcis-12004	18	79	risk	risk	NOUN
fcis-12004	18	80	of	of	ADP
fcis-12004	18	81	mudslides	mudslide	NOUN
fcis-12004	18	82	;	;	PUNCT
fcis-12004	18	83	and	and	CCONJ
fcis-12004	18	84	strengthening	strengthen	VERB
fcis-12004	18	85	the	the	DET
fcis-12004	18	86	public	public	NOUN
fcis-12004	18	87	's	's	PART
fcis-12004	18	88	mudslide	mudslide	ADJ
fcis-12004	18	89	risk	risk	NOUN
fcis-12004	18	90	awareness	awareness	NOUN
fcis-12004	18	91	and	and	CCONJ
fcis-12004	18	92	safety	safety	NOUN
fcis-12004	18	93	education	education	NOUN
fcis-12004	18	94	to	to	PART
fcis-12004	18	95	improve	improve	VERB
fcis-12004	18	96	the	the	DET
fcis-12004	18	97	ability	ability	NOUN
fcis-12004	18	98	to	to	PART
fcis-12004	18	99	cope	cope	VERB
fcis-12004	18	100	with	with	ADP
fcis-12004	18	101	disasters	disaster	NOUN
fcis-12004	18	102	.	.	PUNCT
fcis-12004	19	1	currently	currently	ADV
fcis-12004	19	2	,	,	PUNCT
fcis-12004	19	3	research	research	NOUN
fcis-12004	19	4	on	on	ADP
fcis-12004	19	5	mudslides	mudslide	NOUN
fcis-12004	19	6	and	and	CCONJ
fcis-12004	19	7	disaster	disaster	NOUN
fcis-12004	19	8	prevention	prevention	NOUN
fcis-12004	19	9	is	be	AUX
fcis-12004	19	10	being	be	AUX
fcis-12004	19	11	carried	carry	VERB
fcis-12004	19	12	out	out	ADP
fcis-12004	19	13	in	in	ADP
fcis-12004	19	14	many	many	ADJ
fcis-12004	19	15	scientific	scientific	ADJ
fcis-12004	19	16	research	research	NOUN
fcis-12004	19	17	institutions	institution	NOUN
fcis-12004	19	18	and	and	CCONJ
fcis-12004	19	19	local	local	ADJ
fcis-12004	19	20	governments	government	NOUN
fcis-12004	19	21	.	.	PUNCT
fcis-12004	20	1	the	the	DET
fcis-12004	20	2	research	research	NOUN
fcis-12004	20	3	aspects	aspect	NOUN
fcis-12004	20	4	involve	involve	VERB
fcis-12004	20	5	the	the	DET
fcis-12004	20	6	formation	formation	NOUN
fcis-12004	20	7	mechanism	mechanism	NOUN
fcis-12004	20	8	of	of	ADP
fcis-12004	20	9	mudslide	mudslide	ADJ
fcis-12004	20	10	,	,	PUNCT
fcis-12004	20	11	improvement	improvement	NOUN
fcis-12004	20	12	of	of	ADP
fcis-12004	20	13	monitoring	monitor	VERB
fcis-12004	20	14	technology	technology	NOUN
fcis-12004	20	15	,	,	PUNCT
fcis-12004	20	16	and	and	CCONJ
fcis-12004	20	17	evaluation	evaluation	NOUN
fcis-12004	20	18	of	of	ADP
fcis-12004	20	19	the	the	DET
fcis-12004	20	20	effectiveness	effectiveness	NOUN
fcis-12004	20	21	of	of	ADP
fcis-12004	20	22	prevention	prevention	NOUN
fcis-12004	20	23	and	and	CCONJ
fcis-12004	20	24	control	control	NOUN
fcis-12004	20	25	measures	measure	NOUN
fcis-12004	20	26	.	.	PUNCT
fcis-12004	21	1	computer	computer	NOUN
fcis-12004	21	2	technology	technology	NOUN
fcis-12004	21	3	also	also	ADV
fcis-12004	21	4	plays	play	VERB
fcis-12004	21	5	an	an	DET
fcis-12004	21	6	important	important	ADJ
fcis-12004	21	7	role	role	NOUN
fcis-12004	21	8	in	in	ADP
fcis-12004	21	9	mudslide	mudslide	ADJ
fcis-12004	21	10	research	research	NOUN
fcis-12004	21	11	,	,	PUNCT
fcis-12004	21	12	including	include	VERB
fcis-12004	21	13	mudslide	mudslide	ADJ
fcis-12004	21	14	simulation	simulation	NOUN
fcis-12004	21	15	and	and	CCONJ
fcis-12004	21	16	prediction	prediction	NOUN
fcis-12004	21	17	,	,	PUNCT
fcis-12004	21	18	mudslide	mudslide	ADJ
fcis-12004	21	19	monitoring	monitoring	NOUN
fcis-12004	21	20	and	and	CCONJ
fcis-12004	21	21	early	early	ADJ
fcis-12004	21	22	warning	warning	NOUN
fcis-12004	21	23	systems	system	NOUN
fcis-12004	21	24	,	,	PUNCT
fcis-12004	21	25	land	land	NOUN
fcis-12004	21	26	planning	planning	NOUN
fcis-12004	21	27	and	and	CCONJ
fcis-12004	21	28	disaster	disaster	NOUN
fcis-12004	21	29	risk	risk	NOUN
fcis-12004	21	30	assessment	assessment	NOUN
fcis-12004	21	31	,	,	PUNCT
fcis-12004	21	32	and	and	CCONJ
fcis-12004	21	33	data	datum	NOUN
fcis-12004	21	34	analysis	analysis	NOUN
fcis-12004	21	35	and	and	CCONJ
fcis-12004	21	36	early	early	ADJ
fcis-12004	21	37	warning	warning	NOUN
fcis-12004	21	38	decision	decision	NOUN
fcis-12004	21	39	support	support	NOUN
fcis-12004	21	40	.	.	PUNCT
fcis-12004	22	1	through	through	ADP
fcis-12004	22	2	global	global	ADJ
fcis-12004	22	3	cooperation	cooperation	NOUN
fcis-12004	22	4	and	and	CCONJ
fcis-12004	22	5	scientific	scientific	ADJ
fcis-12004	22	6	and	and	CCONJ
fcis-12004	22	7	technological	technological	ADJ
fcis-12004	22	8	progress	progress	NOUN
fcis-12004	22	9	,	,	PUNCT
fcis-12004	22	10	it	it	PRON
fcis-12004	22	11	is	be	AUX
fcis-12004	22	12	expected	expect	VERB
fcis-12004	22	13	that	that	SCONJ
fcis-12004	22	14	the	the	DET
fcis-12004	22	15	capacity	capacity	NOUN
fcis-12004	22	16	for	for	ADP
fcis-12004	22	17	early	early	ADJ
fcis-12004	22	18	warning	warning	NOUN
fcis-12004	22	19	and	and	CCONJ
fcis-12004	22	20	prevention	prevention	NOUN
fcis-12004	22	21	of	of	ADP
fcis-12004	22	22	mudslides	mudslide	NOUN
fcis-12004	22	23	will	will	AUX
fcis-12004	22	24	continue	continue	VERB
fcis-12004	22	25	to	to	PART
fcis-12004	22	26	improve	improve	VERB
fcis-12004	22	27	,	,	PUNCT
fcis-12004	22	28	with	with	ADP
fcis-12004	22	29	the	the	DET
fcis-12004	22	30	prospect	prospect	NOUN
fcis-12004	22	31	of	of	ADP
fcis-12004	22	32	reducing	reduce	VERB
fcis-12004	22	33	the	the	DET
fcis-12004	22	34	damage	damage	NOUN
fcis-12004	22	35	caused	cause	VERB
fcis-12004	22	36	by	by	ADP
fcis-12004	22	37	the	the	DET
fcis-12004	22	38	disaster	disaster	NOUN
fcis-12004	22	39	.	.	PUNCT
fcis-12004	23	1	2	2	X
fcis-12004	23	2	.	.	X
fcis-12004	23	3	video	video	NOUN
fcis-12004	23	4	detection	detection	NOUN
fcis-12004	23	5	principle	principle	NOUN
fcis-12004	23	6	2.1	2.1	NUM
fcis-12004	23	7	.	.	PUNCT
fcis-12004	23	8	anomaly	anomaly	NOUN
fcis-12004	23	9	detection	detection	NOUN
fcis-12004	23	10	in	in	ADP
fcis-12004	23	11	computer	computer	NOUN
fcis-12004	23	12	vision	vision	NOUN
fcis-12004	23	13	,	,	PUNCT
fcis-12004	23	14	anomaly	anomaly	NOUN
fcis-12004	23	15	detection	detection	NOUN
fcis-12004	23	16	is	be	AUX
fcis-12004	23	17	a	a	DET
fcis-12004	23	18	key	key	ADJ
fcis-12004	23	19	data	datum	NOUN
fcis-12004	23	20	analysis	analysis	NOUN
fcis-12004	23	21	task	task	NOUN
fcis-12004	23	22	and	and	CCONJ
fcis-12004	23	23	one	one	NUM
fcis-12004	23	24	of	of	ADP
fcis-12004	23	25	the	the	DET
fcis-12004	23	26	most	most	ADV
fcis-12004	23	27	challenging	challenging	ADJ
fcis-12004	23	28	and	and	CCONJ
fcis-12004	23	29	longstanding	longstanding	ADJ
fcis-12004	23	30	problems	problem	NOUN
fcis-12004	23	31	.	.	PUNCT
fcis-12004	24	1	in	in	ADP
fcis-12004	24	2	order	order	NOUN
fcis-12004	24	3	to	to	PART
fcis-12004	24	4	ensure	ensure	VERB
fcis-12004	24	5	that	that	SCONJ
fcis-12004	24	6	the	the	DET
fcis-12004	24	7	model	model	NOUN
fcis-12004	24	8	used	use	VERB
fcis-12004	24	9	in	in	ADP
fcis-12004	24	10	this	this	DET
fcis-12004	24	11	paper	paper	NOUN
fcis-12004	24	12	can	can	AUX
fcis-12004	24	13	correctly	correctly	ADV
fcis-12004	24	14	identify	identify	VERB
fcis-12004	24	15	abnormal	abnormal	ADJ
fcis-12004	24	16	behaviors	behavior	NOUN
fcis-12004	24	17	,	,	PUNCT
fcis-12004	24	18	a	a	DET
fcis-12004	24	19	large	large	ADJ
fcis-12004	24	20	amount	amount	NOUN
fcis-12004	24	21	of	of	ADP
fcis-12004	24	22	data	datum	NOUN
fcis-12004	24	23	needs	need	VERB
fcis-12004	24	24	to	to	PART
fcis-12004	24	25	be	be	AUX
fcis-12004	24	26	collected	collect	VERB
fcis-12004	24	27	for	for	ADP
fcis-12004	24	28	training	training	NOUN
fcis-12004	24	29	[	[	X
fcis-12004	24	30	3	3	NUM
fcis-12004	24	31	]	]	PUNCT
fcis-12004	24	32	.	.	PUNCT
fcis-12004	25	1	but	but	CCONJ
fcis-12004	25	2	collecting	collect	VERB
fcis-12004	25	3	all	all	DET
fcis-12004	25	4	the	the	DET
fcis-12004	25	5	normal	normal	ADJ
fcis-12004	25	6	behavior	behavior	NOUN
fcis-12004	25	7	data	datum	NOUN
fcis-12004	25	8	is	be	AUX
fcis-12004	25	9	almost	almost	ADV
fcis-12004	25	10	impossible	impossible	ADJ
fcis-12004	25	11	.	.	PUNCT
fcis-12004	26	1	at	at	ADP
fcis-12004	26	2	the	the	DET
fcis-12004	26	3	same	same	ADJ
fcis-12004	26	4	time	time	NOUN
fcis-12004	26	5	,	,	PUNCT
fcis-12004	26	6	the	the	DET
fcis-12004	26	7	boundary	boundary	NOUN
fcis-12004	26	8	between	between	ADP
fcis-12004	26	9	normal	normal	ADJ
fcis-12004	26	10	and	and	CCONJ
fcis-12004	26	11	abnormal	abnormal	ADJ
fcis-12004	26	12	is	be	AUX
fcis-12004	26	13	blurred	blur	VERB
fcis-12004	26	14	,	,	PUNCT
fcis-12004	26	15	and	and	CCONJ
fcis-12004	26	16	the	the	DET
fcis-12004	26	17	determination	determination	NOUN
fcis-12004	26	18	of	of	ADP
fcis-12004	26	19	abnormality	abnormality	NOUN
fcis-12004	26	20	requires	require	VERB
fcis-12004	26	21	a	a	DET
fcis-12004	26	22	reasonable	reasonable	ADJ
fcis-12004	26	23	grasp	grasp	NOUN
fcis-12004	26	24	of	of	ADP
fcis-12004	26	25	the	the	DET
fcis-12004	26	26	scale	scale	NOUN
fcis-12004	26	27	of	of	ADP
fcis-12004	26	28	normal	normal	ADJ
fcis-12004	26	29	.	.	PUNCT
fcis-12004	27	1	in	in	ADP
fcis-12004	27	2	order	order	NOUN
fcis-12004	27	3	to	to	PART
fcis-12004	27	4	solve	solve	VERB
fcis-12004	27	5	the	the	DET
fcis-12004	27	6	above	above	ADJ
fcis-12004	27	7	problems	problem	NOUN
fcis-12004	27	8	,	,	PUNCT
fcis-12004	27	9	this	this	DET
fcis-12004	27	10	paper	paper	NOUN
fcis-12004	27	11	adds	add	VERB
fcis-12004	27	12	some	some	DET
fcis-12004	27	13	abnormal	abnormal	ADJ
fcis-12004	27	14	information	information	NOUN
fcis-12004	27	15	for	for	ADP
fcis-12004	27	16	the	the	DET
fcis-12004	27	17	model	model	NOUN
fcis-12004	27	18	to	to	PART
fcis-12004	27	19	make	make	VERB
fcis-12004	27	20	reference	reference	NOUN
fcis-12004	27	21	,	,	PUNCT
fcis-12004	27	22	i.e.	i.e.	X
fcis-12004	27	23	,	,	PUNCT
fcis-12004	27	24	not	not	PART
fcis-12004	27	25	only	only	ADV
fcis-12004	27	26	the	the	DET
fcis-12004	27	27	normal	normal	ADJ
fcis-12004	27	28	behavior	behavior	NOUN
fcis-12004	27	29	is	be	AUX
fcis-12004	27	30	considered	consider	VERB
fcis-12004	27	31	,	,	PUNCT
fcis-12004	27	32	but	but	CCONJ
fcis-12004	27	33	also	also	ADV
fcis-12004	27	34	the	the	DET
fcis-12004	27	35	abnormal	abnormal	ADJ
fcis-12004	27	36	behavior	behavior	NOUN
fcis-12004	27	37	is	be	AUX
fcis-12004	27	38	taken	take	VERB
fcis-12004	27	39	into	into	ADP
fcis-12004	27	40	account	account	NOUN
fcis-12004	27	41	for	for	ADP
fcis-12004	27	42	the	the	DET
fcis-12004	27	43	abnormality	abnormality	NOUN
fcis-12004	27	44	detection	detection	NOUN
fcis-12004	27	45	,	,	PUNCT
fcis-12004	27	46	and	and	CCONJ
fcis-12004	27	47	only	only	ADV
fcis-12004	27	48	the	the	DET
fcis-12004	27	49	weakly	weakly	ADJ
fcis-12004	27	50	labeled	label	VERB
fcis-12004	27	51	training	training	NOUN
fcis-12004	27	52	data	datum	NOUN
fcis-12004	27	53	is	be	AUX
fcis-12004	27	54	used	use	VERB
fcis-12004	27	55	,	,	PUNCT
fcis-12004	27	56	so	so	SCONJ
fcis-12004	27	57	as	as	SCONJ
fcis-12004	27	58	to	to	PART
fcis-12004	27	59	improve	improve	VERB
fcis-12004	27	60	the	the	DET
fcis-12004	27	61	model	model	NOUN
fcis-12004	27	62	's	's	PART
fcis-12004	27	63	understanding	understanding	NOUN
fcis-12004	27	64	of	of	ADP
fcis-12004	27	65	the	the	DET
fcis-12004	27	66	abnormal	abnormal	ADJ
fcis-12004	27	67	behavior	behavior	NOUN
fcis-12004	27	68	.	.	PUNCT
fcis-12004	28	1	and	and	CCONJ
fcis-12004	28	2	anomaly	anomaly	NOUN
fcis-12004	28	3	detection	detection	NOUN
fcis-12004	28	4	for	for	ADP
fcis-12004	28	5	video	video	NOUN
fcis-12004	28	6	surveillance	surveillance	NOUN
fcis-12004	28	7	applications	application	NOUN
fcis-12004	28	8	,	,	PUNCT
fcis-12004	28	9	in	in	ADP
fcis-12004	28	10	this	this	DET
fcis-12004	28	11	paper	paper	NOUN
fcis-12004	28	12	is	be	AUX
fcis-12004	28	13	used	use	VERB
fcis-12004	28	14	for	for	ADP
fcis-12004	28	15	the	the	DET
fcis-12004	28	16	monitoring	monitoring	NOUN
fcis-12004	28	17	of	of	ADP
fcis-12004	28	18	mudslide	mudslide	ADJ
fcis-12004	28	19	impact	impact	NOUN
fcis-12004	28	20	.	.	PUNCT
fcis-12004	29	1	in	in	ADP
fcis-12004	29	2	the	the	DET
fcis-12004	29	3	testing	testing	NOUN
fcis-12004	29	4	process	process	NOUN
fcis-12004	29	5	,	,	PUNCT
fcis-12004	29	6	patterns	pattern	NOUN
fcis-12004	29	7	with	with	ADP
fcis-12004	29	8	large	large	ADJ
fcis-12004	29	9	reconstruction	reconstruction	NOUN
fcis-12004	29	10	errors	error	NOUN
fcis-12004	29	11	are	be	AUX
fcis-12004	29	12	considered	consider	VERB
fcis-12004	29	13	as	as	ADP
fcis-12004	29	14	anomalous	anomalous	ADJ
fcis-12004	29	15	behaviors	behavior	NOUN
fcis-12004	29	16	.	.	PUNCT
fcis-12004	30	1	and	and	CCONJ
fcis-12004	30	2	in	in	ADP
fcis-12004	30	3	this	this	DET
fcis-12004	30	4	paper	paper	NOUN
fcis-12004	30	5	,	,	PUNCT
fcis-12004	30	6	the	the	DET
fcis-12004	30	7	action	action	NOUN
fcis-12004	30	8	of	of	ADP
fcis-12004	30	9	mudslide	mudslide	NOUN
fcis-12004	30	10	is	be	AUX
fcis-12004	30	11	considered	consider	VERB
fcis-12004	30	12	as	as	ADP
fcis-12004	30	13	the	the	DET
fcis-12004	30	14	target	target	NOUN
fcis-12004	30	15	of	of	ADP
fcis-12004	30	16	anomaly	anomaly	NOUN
fcis-12004	30	17	detection	detection	NOUN
fcis-12004	30	18	.	.	PUNCT
fcis-12004	31	1	unlike	unlike	ADP
fcis-12004	31	2	the	the	DET
fcis-12004	31	3	generic	generic	ADJ
fcis-12004	31	4	detection	detection	NOUN
fcis-12004	31	5	of	of	ADP
fcis-12004	31	6	still	still	ADV
fcis-12004	31	7	images	image	NOUN
fcis-12004	31	8	,	,	PUNCT
fcis-12004	31	9	video	video	NOUN
fcis-12004	31	10	detection	detection	NOUN
fcis-12004	31	11	usually	usually	ADV
fcis-12004	31	12	utilizes	utilize	VERB
fcis-12004	31	13	the	the	DET
fcis-12004	31	14	context	context	NOUN
fcis-12004	31	15	between	between	ADP
fcis-12004	31	16	consecutive	consecutive	ADJ
fcis-12004	31	17	104	104	NUM
fcis-12004	31	18	frames	frame	NOUN
fcis-12004	31	19	of	of	ADP
fcis-12004	31	20	the	the	DET
fcis-12004	31	21	video	video	NOUN
fcis-12004	31	22	to	to	PART
fcis-12004	31	23	localize	localize	VERB
fcis-12004	31	24	the	the	DET
fcis-12004	31	25	region	region	NOUN
fcis-12004	31	26	of	of	ADP
fcis-12004	31	27	interest	interest	NOUN
fcis-12004	31	28	.	.	PUNCT
fcis-12004	32	1	in	in	ADP
fcis-12004	32	2	this	this	DET
fcis-12004	32	3	paper	paper	NOUN
fcis-12004	32	4	,	,	PUNCT
fcis-12004	32	5	the	the	DET
fcis-12004	32	6	model	model	NOUN
fcis-12004	32	7	is	be	AUX
fcis-12004	32	8	trained	train	VERB
fcis-12004	32	9	under	under	ADP
fcis-12004	32	10	weak	weak	ADJ
fcis-12004	32	11	supervision	supervision	NOUN
fcis-12004	32	12	,	,	PUNCT
fcis-12004	32	13	and	and	CCONJ
fcis-12004	32	14	thus	thus	ADV
fcis-12004	32	15	only	only	ADV
fcis-12004	32	16	cares	care	VERB
fcis-12004	32	17	about	about	ADP
fcis-12004	32	18	the	the	DET
fcis-12004	32	19	presence	presence	NOUN
fcis-12004	32	20	of	of	ADP
fcis-12004	32	21	anomalous	anomalous	ADJ
fcis-12004	32	22	events	event	NOUN
fcis-12004	32	23	,	,	PUNCT
fcis-12004	32	24	not	not	PART
fcis-12004	32	25	about	about	ADP
fcis-12004	32	26	which	which	PRON
fcis-12004	32	27	frames	frame	NOUN
fcis-12004	32	28	of	of	ADP
fcis-12004	32	29	the	the	DET
fcis-12004	32	30	video	video	NOUN
fcis-12004	32	31	the	the	DET
fcis-12004	32	32	specific	specific	ADJ
fcis-12004	32	33	anomaly	anomaly	NOUN
fcis-12004	32	34	occurs	occur	VERB
fcis-12004	32	35	within	within	ADP
fcis-12004	32	36	.	.	PUNCT
fcis-12004	33	1	traditional	traditional	ADJ
fcis-12004	33	2	action	action	NOUN
fcis-12004	33	3	recognition	recognition	NOUN
fcis-12004	33	4	methods	method	NOUN
fcis-12004	33	5	can	can	AUX
fcis-12004	33	6	not	not	PART
fcis-12004	33	7	be	be	AUX
fcis-12004	33	8	used	use	VERB
fcis-12004	33	9	for	for	ADP
fcis-12004	33	10	anomaly	anomaly	NOUN
fcis-12004	33	11	detection	detection	NOUN
fcis-12004	33	12	in	in	ADP
fcis-12004	33	13	realistic	realistic	ADJ
fcis-12004	33	14	surveillance	surveillance	NOUN
fcis-12004	33	15	videos	video	NOUN
fcis-12004	33	16	.	.	PUNCT
fcis-12004	34	1	this	this	PRON
fcis-12004	34	2	is	be	AUX
fcis-12004	34	3	because	because	SCONJ
fcis-12004	34	4	the	the	DET
fcis-12004	34	5	dataset	dataset	NOUN
fcis-12004	34	6	contains	contain	VERB
fcis-12004	34	7	long	long	ADJ
fcis-12004	34	8	periods	period	NOUN
fcis-12004	34	9	of	of	ADP
fcis-12004	34	10	untrimmed	untrimmed	ADJ
fcis-12004	34	11	video	video	NOUN
fcis-12004	34	12	,	,	PUNCT
fcis-12004	34	13	whereas	whereas	SCONJ
fcis-12004	34	14	anomalies	anomaly	NOUN
fcis-12004	34	15	tend	tend	VERB
fcis-12004	34	16	to	to	PART
fcis-12004	34	17	be	be	AUX
fcis-12004	34	18	random	random	ADJ
fcis-12004	34	19	and	and	CCONJ
fcis-12004	34	20	mostly	mostly	ADV
fcis-12004	34	21	occur	occur	VERB
fcis-12004	34	22	within	within	ADP
fcis-12004	34	23	a	a	DET
fcis-12004	34	24	short	short	ADJ
fcis-12004	34	25	period	period	NOUN
fcis-12004	34	26	of	of	ADP
fcis-12004	34	27	time	time	NOUN
fcis-12004	34	28	.	.	PUNCT
fcis-12004	35	1	as	as	ADP
fcis-12004	35	2	a	a	DET
fcis-12004	35	3	result	result	NOUN
fcis-12004	35	4	,	,	PUNCT
fcis-12004	35	5	the	the	DET
fcis-12004	35	6	features	feature	NOUN
fcis-12004	35	7	extracted	extract	VERB
fcis-12004	35	8	from	from	ADP
fcis-12004	35	9	these	these	DET
fcis-12004	35	10	untrimmed	untrimmed	ADJ
fcis-12004	35	11	training	training	NOUN
fcis-12004	35	12	videos	video	NOUN
fcis-12004	35	13	are	be	AUX
fcis-12004	35	14	not	not	PART
fcis-12004	35	15	sufficiently	sufficiently	ADV
fcis-12004	35	16	discriminative	discriminative	NOUN
fcis-12004	35	17	for	for	ADP
fcis-12004	35	18	anomalous	anomalous	ADJ
fcis-12004	35	19	events	event	NOUN
fcis-12004	35	20	.	.	PUNCT
fcis-12004	36	1	therefore	therefore	ADV
fcis-12004	36	2	,	,	PUNCT
fcis-12004	36	3	training	training	NOUN
fcis-12004	36	4	of	of	ADP
fcis-12004	36	5	abnormal	abnormal	ADJ
fcis-12004	36	6	and	and	CCONJ
fcis-12004	36	7	normal	normal	ADJ
fcis-12004	36	8	videos	video	NOUN
fcis-12004	36	9	is	be	AUX
fcis-12004	36	10	indispensable	indispensable	ADJ
fcis-12004	36	11	.	.	PUNCT
fcis-12004	37	1	in	in	ADP
fcis-12004	37	2	this	this	DET
fcis-12004	37	3	regard	regard	NOUN
fcis-12004	37	4	,	,	PUNCT
fcis-12004	37	5	in	in	ADP
fcis-12004	37	6	this	this	DET
fcis-12004	37	7	paper	paper	NOUN
fcis-12004	37	8	,	,	PUNCT
fcis-12004	37	9	the	the	DET
fcis-12004	37	10	videos	video	NOUN
fcis-12004	37	11	in	in	ADP
fcis-12004	37	12	the	the	DET
fcis-12004	37	13	dataset	dataset	NOUN
fcis-12004	37	14	are	be	AUX
fcis-12004	37	15	all	all	ADV
fcis-12004	37	16	categorized	categorize	VERB
fcis-12004	37	17	into	into	ADP
fcis-12004	37	18	short	short	ADJ
fcis-12004	37	19	videos	video	NOUN
fcis-12004	37	20	and	and	CCONJ
fcis-12004	37	21	the	the	DET
fcis-12004	37	22	different	different	ADJ
fcis-12004	37	23	short	short	ADJ
fcis-12004	37	24	videos	video	NOUN
fcis-12004	37	25	are	be	AUX
fcis-12004	37	26	put	put	VERB
fcis-12004	37	27	into	into	ADP
fcis-12004	37	28	positive	positive	ADJ
fcis-12004	37	29	and	and	CCONJ
fcis-12004	37	30	negative	negative	ADJ
fcis-12004	37	31	example	example	NOUN
fcis-12004	37	32	packages	package	NOUN
fcis-12004	37	33	for	for	ADP
fcis-12004	37	34	subsequent	subsequent	ADJ
fcis-12004	37	35	operations	operation	NOUN
fcis-12004	37	36	.	.	PUNCT
fcis-12004	38	1	2.2	2.2	NUM
fcis-12004	38	2	.	.	PUNCT
fcis-12004	39	1	i3d	i3d	NOUN
fcis-12004	39	2	in	in	ADP
fcis-12004	39	3	the	the	DET
fcis-12004	39	4	field	field	NOUN
fcis-12004	39	5	of	of	ADP
fcis-12004	39	6	image	image	NOUN
fcis-12004	39	7	processing	processing	NOUN
fcis-12004	39	8	,	,	PUNCT
fcis-12004	39	9	all	all	DET
fcis-12004	39	10	the	the	DET
fcis-12004	39	11	images	image	NOUN
fcis-12004	39	12	that	that	PRON
fcis-12004	39	13	are	be	AUX
fcis-12004	39	14	convolved	convolve	VERB
fcis-12004	39	15	are	be	AUX
fcis-12004	39	16	static	static	ADJ
fcis-12004	39	17	images	image	NOUN
fcis-12004	39	18	,	,	PUNCT
fcis-12004	39	19	so	so	SCONJ
fcis-12004	39	20	the	the	DET
fcis-12004	39	21	use	use	NOUN
fcis-12004	39	22	of	of	ADP
fcis-12004	39	23	two	two	NUM
fcis-12004	39	24	-	-	PUNCT
fcis-12004	39	25	dimensional	dimensional	ADJ
fcis-12004	39	26	convolutional	convolutional	ADJ
fcis-12004	39	27	neural	neural	ADJ
fcis-12004	39	28	network	network	NOUN
fcis-12004	39	29	(	(	PUNCT
fcis-12004	39	30	2d	2d	NUM
fcis-12004	39	31	cnn	cnn	PROPN
fcis-12004	39	32	)	)	PUNCT
fcis-12004	39	33	is	be	AUX
fcis-12004	39	34	sufficient	sufficient	ADJ
fcis-12004	39	35	.	.	PUNCT
fcis-12004	40	1	in	in	ADP
fcis-12004	40	2	the	the	DET
fcis-12004	40	3	field	field	NOUN
fcis-12004	40	4	of	of	ADP
fcis-12004	40	5	video	video	NOUN
fcis-12004	40	6	understanding	understanding	NOUN
fcis-12004	40	7	,	,	PUNCT
fcis-12004	40	8	on	on	ADP
fcis-12004	40	9	the	the	DET
fcis-12004	40	10	other	other	ADJ
fcis-12004	40	11	hand	hand	NOUN
fcis-12004	40	12	,	,	PUNCT
fcis-12004	40	13	in	in	ADP
fcis-12004	40	14	order	order	NOUN
fcis-12004	40	15	to	to	PART
fcis-12004	40	16	retain	retain	VERB
fcis-12004	40	17	the	the	DET
fcis-12004	40	18	temporal	temporal	ADJ
fcis-12004	40	19	information	information	NOUN
fcis-12004	40	20	at	at	ADP
fcis-12004	40	21	the	the	DET
fcis-12004	40	22	same	same	ADJ
fcis-12004	40	23	time	time	NOUN
fcis-12004	40	24	,	,	PUNCT
fcis-12004	40	25	it	it	PRON
fcis-12004	40	26	is	be	AUX
fcis-12004	40	27	necessary	necessary	ADJ
fcis-12004	40	28	to	to	PART
fcis-12004	40	29	learn	learn	VERB
fcis-12004	40	30	the	the	DET
fcis-12004	40	31	spatio	spatio	PROPN
fcis-12004	40	32	-	-	PUNCT
fcis-12004	40	33	temporal	temporal	ADJ
fcis-12004	40	34	features	feature	NOUN
fcis-12004	40	35	at	at	ADP
fcis-12004	40	36	the	the	DET
fcis-12004	40	37	same	same	ADJ
fcis-12004	40	38	time	time	NOUN
fcis-12004	40	39	,	,	PUNCT
fcis-12004	40	40	and	and	CCONJ
fcis-12004	40	41	if	if	SCONJ
fcis-12004	40	42	a	a	DET
fcis-12004	40	43	2d	2d	NUM
fcis-12004	40	44	cnn	cnn	PROPN
fcis-12004	40	45	is	be	AUX
fcis-12004	40	46	used	use	VERB
fcis-12004	40	47	to	to	PART
fcis-12004	40	48	process	process	VERB
fcis-12004	40	49	the	the	DET
fcis-12004	40	50	video	video	NOUN
fcis-12004	40	51	,	,	PUNCT
fcis-12004	40	52	then	then	ADV
fcis-12004	40	53	it	it	PRON
fcis-12004	40	54	will	will	AUX
fcis-12004	40	55	not	not	PART
fcis-12004	40	56	be	be	AUX
fcis-12004	40	57	able	able	ADJ
fcis-12004	40	58	to	to	PART
fcis-12004	40	59	take	take	VERB
fcis-12004	40	60	into	into	ADP
fcis-12004	40	61	account	account	NOUN
fcis-12004	40	62	the	the	DET
fcis-12004	40	63	motion	motion	NOUN
fcis-12004	40	64	information	information	NOUN
fcis-12004	40	65	encoded	encode	VERB
fcis-12004	40	66	between	between	ADP
fcis-12004	40	67	multiple	multiple	ADJ
fcis-12004	40	68	consecutive	consecutive	ADJ
fcis-12004	40	69	frames	frame	NOUN
fcis-12004	40	70	.	.	PUNCT
fcis-12004	41	1	for	for	ADP
fcis-12004	41	2	2d	2d	NUM
fcis-12004	41	3	convolutional	convolutional	ADJ
fcis-12004	41	4	networks	network	NOUN
fcis-12004	41	5	,	,	PUNCT
fcis-12004	41	6	the	the	DET
fcis-12004	41	7	input	input	NOUN
fcis-12004	41	8	is	be	AUX
fcis-12004	41	9	an	an	DET
fcis-12004	41	10	image	image	NOUN
fcis-12004	41	11	and	and	CCONJ
fcis-12004	41	12	the	the	DET
fcis-12004	41	13	output	output	NOUN
fcis-12004	41	14	will	will	AUX
fcis-12004	41	15	only	only	ADV
fcis-12004	41	16	produce	produce	VERB
fcis-12004	41	17	an	an	DET
fcis-12004	41	18	image	image	NOUN
fcis-12004	41	19	,	,	PUNCT
fcis-12004	41	20	and	and	CCONJ
fcis-12004	41	21	the	the	DET
fcis-12004	41	22	input	input	NOUN
fcis-12004	41	23	is	be	AUX
fcis-12004	41	24	a	a	DET
fcis-12004	41	25	video	video	NOUN
fcis-12004	41	26	and	and	CCONJ
fcis-12004	41	27	the	the	DET
fcis-12004	41	28	output	output	NOUN
fcis-12004	41	29	will	will	AUX
fcis-12004	41	30	still	still	ADV
fcis-12004	41	31	be	be	AUX
fcis-12004	41	32	an	an	DET
fcis-12004	41	33	image	image	NOUN
fcis-12004	41	34	,	,	PUNCT
fcis-12004	41	35	this	this	PRON
fcis-12004	41	36	is	be	AUX
fcis-12004	41	37	because	because	SCONJ
fcis-12004	41	38	2d	2d	NUM
fcis-12004	41	39	convolutional	convolutional	ADJ
fcis-12004	41	40	networks	network	NOUN
fcis-12004	41	41	can	can	AUX
fcis-12004	41	42	only	only	ADV
fcis-12004	41	43	learn	learn	VERB
fcis-12004	41	44	spatial	spatial	ADJ
fcis-12004	41	45	features	feature	NOUN
fcis-12004	41	46	.	.	PUNCT
fcis-12004	42	1	in	in	ADP
fcis-12004	42	2	the	the	DET
fcis-12004	42	3	previous	previous	ADJ
fcis-12004	42	4	use	use	NOUN
fcis-12004	42	5	of	of	ADP
fcis-12004	42	6	2d	2d	NUM
fcis-12004	42	7	cnn	cnn	PROPN
fcis-12004	42	8	to	to	PART
fcis-12004	42	9	process	process	NOUN
fcis-12004	42	10	video	video	NOUN
fcis-12004	42	11	,	,	PUNCT
fcis-12004	42	12	the	the	DET
fcis-12004	42	13	features	feature	NOUN
fcis-12004	42	14	are	be	AUX
fcis-12004	42	15	extracted	extract	VERB
fcis-12004	42	16	from	from	ADP
fcis-12004	42	17	each	each	DET
fcis-12004	42	18	key	key	ADJ
fcis-12004	42	19	frame	frame	NOUN
fcis-12004	42	20	,	,	PUNCT
fcis-12004	42	21	and	and	CCONJ
fcis-12004	42	22	then	then	ADV
fcis-12004	42	23	an	an	DET
fcis-12004	42	24	algorithm	algorithm	NOUN
fcis-12004	42	25	is	be	AUX
fcis-12004	42	26	used	use	VERB
fcis-12004	42	27	to	to	PART
fcis-12004	42	28	combine	combine	VERB
fcis-12004	42	29	the	the	DET
fcis-12004	42	30	features	feature	NOUN
fcis-12004	42	31	of	of	ADP
fcis-12004	42	32	each	each	DET
fcis-12004	42	33	key	key	ADJ
fcis-12004	42	34	frame	frame	NOUN
fcis-12004	42	35	together	together	ADV
fcis-12004	42	36	.	.	PUNCT
fcis-12004	43	1	this	this	DET
fcis-12004	43	2	operation	operation	NOUN
fcis-12004	43	3	creates	create	VERB
fcis-12004	43	4	a	a	DET
fcis-12004	43	5	problem	problem	NOUN
fcis-12004	43	6	,	,	PUNCT
fcis-12004	43	7	that	that	ADV
fcis-12004	43	8	is	is	ADV
fcis-12004	43	9	,	,	PUNCT
fcis-12004	43	10	when	when	SCONJ
fcis-12004	43	11	using	use	VERB
fcis-12004	43	12	2d	2d	PROPN
fcis-12004	43	13	cnn	cnn	PROPN
fcis-12004	43	14	to	to	PART
fcis-12004	43	15	process	process	NOUN
fcis-12004	43	16	video	video	NOUN
fcis-12004	43	17	is	be	AUX
fcis-12004	43	18	to	to	PART
fcis-12004	43	19	treat	treat	VERB
fcis-12004	43	20	each	each	DET
fcis-12004	43	21	frame	frame	NOUN
fcis-12004	43	22	as	as	ADP
fcis-12004	43	23	a	a	DET
fcis-12004	43	24	static	static	ADJ
fcis-12004	43	25	picture	picture	NOUN
fcis-12004	43	26	,	,	PUNCT
fcis-12004	43	27	ignoring	ignore	VERB
fcis-12004	43	28	the	the	DET
fcis-12004	43	29	time	time	NOUN
fcis-12004	43	30	dimension	dimension	NOUN
fcis-12004	43	31	of	of	ADP
fcis-12004	43	32	the	the	DET
fcis-12004	43	33	motion	motion	NOUN
fcis-12004	43	34	information	information	NOUN
fcis-12004	43	35	.	.	PUNCT
fcis-12004	44	1	3d	3d	NUM
fcis-12004	44	2	convolution	convolution	NOUN
fcis-12004	44	3	and	and	CCONJ
fcis-12004	44	4	3d	3d	NUM
fcis-12004	44	5	pooling	pooling	NOUN
fcis-12004	44	6	,	,	PUNCT
fcis-12004	44	7	on	on	ADP
fcis-12004	44	8	the	the	DET
fcis-12004	44	9	other	other	ADJ
fcis-12004	44	10	hand	hand	NOUN
fcis-12004	44	11	,	,	PUNCT
fcis-12004	44	12	can	can	AUX
fcis-12004	44	13	model	model	VERB
fcis-12004	44	14	temporal	temporal	ADJ
fcis-12004	44	15	information	information	NOUN
fcis-12004	44	16	,	,	PUNCT
fcis-12004	44	17	i.e.	i.e.	X
fcis-12004	44	18	,	,	PUNCT
fcis-12004	44	19	3d	3d	ADJ
fcis-12004	44	20	convolutional	convolutional	ADJ
fcis-12004	44	21	network	network	NOUN
fcis-12004	44	22	input	input	NOUN
fcis-12004	44	23	video	video	NOUN
fcis-12004	44	24	will	will	AUX
fcis-12004	44	25	output	output	VERB
fcis-12004	44	26	another	another	DET
fcis-12004	44	27	video	video	NOUN
fcis-12004	44	28	,	,	PUNCT
fcis-12004	44	29	preserving	preserve	VERB
fcis-12004	44	30	the	the	DET
fcis-12004	44	31	input	input	NOUN
fcis-12004	44	32	temporal	temporal	ADJ
fcis-12004	44	33	information	information	NOUN
fcis-12004	44	34	[	[	X
fcis-12004	44	35	4	4	NUM
fcis-12004	44	36	]	]	PUNCT
fcis-12004	44	37	.	.	PUNCT
fcis-12004	45	1	fig	fig	PROPN
fcis-12004	45	2	1	1	NUM
fcis-12004	45	3	.	.	PUNCT
fcis-12004	45	4	schema	schema	NOUN
fcis-12004	45	5	of	of	ADP
fcis-12004	45	6	i3d	i3d	NOUN
fcis-12004	45	7	meanwhile	meanwhile	ADV
fcis-12004	45	8	,	,	PUNCT
fcis-12004	45	9	in	in	ADP
fcis-12004	45	10	order	order	NOUN
fcis-12004	45	11	to	to	PART
fcis-12004	45	12	ensure	ensure	VERB
fcis-12004	45	13	that	that	SCONJ
fcis-12004	45	14	the	the	DET
fcis-12004	45	15	network	network	NOUN
fcis-12004	45	16	can	can	AUX
fcis-12004	45	17	extract	extract	VERB
fcis-12004	45	18	features	feature	NOUN
fcis-12004	45	19	at	at	ADP
fcis-12004	45	20	multiple	multiple	ADJ
fcis-12004	45	21	scales	scale	NOUN
fcis-12004	45	22	,	,	PUNCT
fcis-12004	45	23	so	so	SCONJ
fcis-12004	45	24	as	as	SCONJ
fcis-12004	45	25	to	to	PART
fcis-12004	45	26	understand	understand	VERB
fcis-12004	45	27	the	the	DET
fcis-12004	45	28	video	video	NOUN
fcis-12004	45	29	content	content	NOUN
fcis-12004	45	30	more	more	ADV
fcis-12004	45	31	comprehensively	comprehensively	ADV
fcis-12004	45	32	,	,	PUNCT
fcis-12004	45	33	this	this	DET
fcis-12004	45	34	paper	paper	NOUN
fcis-12004	45	35	adopts	adopt	VERB
fcis-12004	45	36	the	the	DET
fcis-12004	45	37	inception	inception	ADJ
fcis-12004	45	38	structure	structure	NOUN
fcis-12004	45	39	.	.	PUNCT
fcis-12004	46	1	therefore	therefore	ADV
fcis-12004	46	2	,	,	PUNCT
fcis-12004	46	3	therefore	therefore	ADV
fcis-12004	46	4	,	,	PUNCT
fcis-12004	46	5	this	this	DET
fcis-12004	46	6	paper	paper	NOUN
fcis-12004	46	7	adopts	adopt	VERB
fcis-12004	46	8	inflated	inflate	VERB
fcis-12004	46	9	3d	3d	NUM
fcis-12004	46	10	convolutional	convolutional	ADJ
fcis-12004	46	11	neural	neural	ADJ
fcis-12004	46	12	network	network	NOUN
fcis-12004	46	13	(	(	PUNCT
fcis-12004	46	14	i3d	i3d	NUM
fcis-12004	46	15	)	)	PUNCT
fcis-12004	46	16	for	for	ADP
fcis-12004	46	17	the	the	DET
fcis-12004	46	18	characteristics	characteristic	NOUN
fcis-12004	46	19	of	of	ADP
fcis-12004	46	20	mudslide	mudslide	ADJ
fcis-12004	46	21	recognition	recognition	NOUN
fcis-12004	46	22	as	as	ADV
fcis-12004	46	23	well	well	ADV
fcis-12004	46	24	as	as	ADP
fcis-12004	46	25	the	the	DET
fcis-12004	46	26	consideration	consideration	NOUN
fcis-12004	46	27	of	of	ADP
fcis-12004	46	28	recognition	recognition	NOUN
fcis-12004	46	29	success	success	NOUN
fcis-12004	46	30	rate	rate	NOUN
fcis-12004	46	31	,	,	PUNCT
fcis-12004	46	32	as	as	SCONJ
fcis-12004	46	33	shown	show	VERB
fcis-12004	46	34	in	in	ADP
fcis-12004	46	35	fig	fig	NOUN
fcis-12004	46	36	.	.	PUNCT
fcis-12004	47	1	1	1	X
fcis-12004	47	2	.	.	X
fcis-12004	47	3	in	in	ADP
fcis-12004	47	4	addition	addition	NOUN
fcis-12004	47	5	,	,	PUNCT
fcis-12004	47	6	the	the	DET
fcis-12004	47	7	overfitting	overfitting	NOUN
fcis-12004	47	8	is	be	AUX
fcis-12004	47	9	reduced	reduce	VERB
fcis-12004	47	10	by	by	ADP
fcis-12004	47	11	the	the	DET
fcis-12004	47	12	deconvolutional	deconvolutional	ADJ
fcis-12004	47	13	layer	layer	NOUN
fcis-12004	47	14	introduced	introduce	VERB
fcis-12004	47	15	by	by	ADP
fcis-12004	47	16	i3d	i3d	NOUN
fcis-12004	47	17	to	to	PART
fcis-12004	47	18	improve	improve	VERB
fcis-12004	47	19	the	the	DET
fcis-12004	47	20	generalization	generalization	NOUN
fcis-12004	47	21	ability	ability	NOUN
fcis-12004	47	22	of	of	ADP
fcis-12004	47	23	the	the	DET
fcis-12004	47	24	model	model	NOUN
fcis-12004	47	25	,	,	PUNCT
fcis-12004	47	26	and	and	CCONJ
fcis-12004	47	27	the	the	DET
fcis-12004	47	28	richness	richness	NOUN
fcis-12004	47	29	of	of	ADP
fcis-12004	47	30	the	the	DET
fcis-12004	47	31	feature	feature	NOUN
fcis-12004	47	32	representation	representation	NOUN
fcis-12004	47	33	is	be	AUX
fcis-12004	47	34	enhanced	enhance	VERB
fcis-12004	47	35	by	by	ADP
fcis-12004	47	36	fusing	fuse	VERB
fcis-12004	47	37	different	different	ADJ
fcis-12004	47	38	levels	level	NOUN
fcis-12004	47	39	of	of	ADP
fcis-12004	47	40	features	feature	NOUN
fcis-12004	47	41	through	through	ADP
fcis-12004	47	42	feature	feature	NOUN
fcis-12004	47	43	splicing	splicing	NOUN
fcis-12004	47	44	.	.	PUNCT
fcis-12004	48	1	i3d	i3d	PROPN
fcis-12004	48	2	is	be	AUX
fcis-12004	48	3	an	an	DET
fcis-12004	48	4	innovative	innovative	ADJ
fcis-12004	48	5	architecture	architecture	NOUN
fcis-12004	48	6	in	in	ADP
fcis-12004	48	7	the	the	DET
fcis-12004	48	8	field	field	NOUN
fcis-12004	48	9	of	of	ADP
fcis-12004	48	10	deep	deep	ADJ
fcis-12004	48	11	learning	learning	NOUN
fcis-12004	48	12	,	,	PUNCT
fcis-12004	48	13	designed	design	VERB
fcis-12004	48	14	specifically	specifically	ADV
fcis-12004	48	15	for	for	ADP
fcis-12004	48	16	video	video	NOUN
fcis-12004	48	17	data	datum	NOUN
fcis-12004	48	18	processing	processing	NOUN
fcis-12004	48	19	.	.	PUNCT
fcis-12004	49	1	its	its	PRON
fcis-12004	49	2	core	core	NOUN
fcis-12004	49	3	innovation	innovation	NOUN
fcis-12004	49	4	is	be	AUX
fcis-12004	49	5	to	to	PART
fcis-12004	49	6	extend	extend	VERB
fcis-12004	49	7	the	the	DET
fcis-12004	49	8	2d	2d	NUM
fcis-12004	49	9	convolutional	convolutional	ADJ
fcis-12004	49	10	weights	weight	NOUN
fcis-12004	49	11	,	,	PUNCT
fcis-12004	49	12	which	which	PRON
fcis-12004	49	13	have	have	AUX
fcis-12004	49	14	been	be	AUX
fcis-12004	49	15	subjected	subject	VERB
fcis-12004	49	16	to	to	ADP
fcis-12004	49	17	an	an	DET
fcis-12004	49	18	inflation	inflation	NOUN
fcis-12004	49	19	operation	operation	NOUN
fcis-12004	49	20	,	,	PUNCT
fcis-12004	49	21	into	into	ADP
fcis-12004	49	22	3d	3d	NUM
fcis-12004	49	23	convolutional	convolutional	ADJ
fcis-12004	49	24	weights	weight	NOUN
fcis-12004	49	25	,	,	PUNCT
fcis-12004	49	26	thus	thus	ADV
fcis-12004	49	27	enabling	enable	VERB
fcis-12004	49	28	the	the	DET
fcis-12004	49	29	network	network	NOUN
fcis-12004	49	30	to	to	PART
fcis-12004	49	31	capture	capture	VERB
fcis-12004	49	32	both	both	CCONJ
fcis-12004	49	33	spatial	spatial	ADJ
fcis-12004	49	34	and	and	CCONJ
fcis-12004	49	35	temporal	temporal	ADJ
fcis-12004	49	36	dimensional	dimensional	ADJ
fcis-12004	49	37	information	information	NOUN
fcis-12004	49	38	of	of	ADP
fcis-12004	49	39	video	video	NOUN
fcis-12004	49	40	data.i3d	data.i3d	PROPN
fcis-12004	49	41	is	be	AUX
fcis-12004	49	42	mainly	mainly	ADV
fcis-12004	49	43	used	use	VERB
fcis-12004	49	44	for	for	ADP
fcis-12004	49	45	tasks	task	NOUN
fcis-12004	49	46	such	such	ADJ
fcis-12004	49	47	as	as	ADP
fcis-12004	49	48	video	video	NOUN
fcis-12004	49	49	classification	classification	NOUN
fcis-12004	49	50	and	and	CCONJ
fcis-12004	49	51	action	action	NOUN
fcis-12004	49	52	recognition	recognition	NOUN
fcis-12004	49	53	,	,	PUNCT
fcis-12004	49	54	and	and	CCONJ
fcis-12004	49	55	its	its	PRON
fcis-12004	49	56	construction	construction	NOUN
fcis-12004	49	57	and	and	CCONJ
fcis-12004	49	58	working	working	NOUN
fcis-12004	49	59	process	process	NOUN
fcis-12004	49	60	can	can	AUX
fcis-12004	49	61	be	be	AUX
fcis-12004	49	62	divided	divide	VERB
fcis-12004	49	63	into	into	ADP
fcis-12004	49	64	several	several	ADJ
fcis-12004	49	65	key	key	ADJ
fcis-12004	49	66	steps	step	NOUN
fcis-12004	49	67	.	.	PUNCT
fcis-12004	50	1	first	first	ADV
fcis-12004	50	2	,	,	PUNCT
fcis-12004	50	3	i3d	i3d	NOUN
fcis-12004	50	4	selects	select	VERB
fcis-12004	50	5	a	a	DET
fcis-12004	50	6	convolutional	convolutional	ADJ
fcis-12004	50	7	neural	neural	ADJ
fcis-12004	50	8	network	network	NOUN
fcis-12004	50	9	pretrained	pretraine	VERB
fcis-12004	50	10	on	on	ADP
fcis-12004	50	11	2d	2d	NUM
fcis-12004	50	12	image	image	NOUN
fcis-12004	50	13	data	datum	NOUN
fcis-12004	50	14	as	as	ADP
fcis-12004	50	15	its	its	PRON
fcis-12004	50	16	infrastructure	infrastructure	NOUN
fcis-12004	50	17	.	.	PUNCT
fcis-12004	51	1	this	this	DET
fcis-12004	51	2	base	base	NOUN
fcis-12004	51	3	network	network	NOUN
fcis-12004	51	4	performs	perform	VERB
fcis-12004	51	5	well	well	ADV
fcis-12004	51	6	in	in	ADP
fcis-12004	51	7	the	the	DET
fcis-12004	51	8	domain	domain	NOUN
fcis-12004	51	9	of	of	ADP
fcis-12004	51	10	still	still	ADV
fcis-12004	51	11	images	image	NOUN
fcis-12004	51	12	,	,	PUNCT
fcis-12004	51	13	but	but	CCONJ
fcis-12004	51	14	lacks	lack	VERB
fcis-12004	51	15	the	the	DET
fcis-12004	51	16	ability	ability	NOUN
fcis-12004	51	17	to	to	PART
fcis-12004	51	18	process	process	VERB
fcis-12004	51	19	in	in	ADP
fcis-12004	51	20	the	the	DET
fcis-12004	51	21	temporal	temporal	ADJ
fcis-12004	51	22	dimension	dimension	NOUN
fcis-12004	51	23	.	.	PUNCT
fcis-12004	52	1	then	then	ADV
fcis-12004	52	2	,	,	PUNCT
fcis-12004	52	3	through	through	ADP
fcis-12004	52	4	a	a	DET
fcis-12004	52	5	weight	weight	NOUN
fcis-12004	52	6	inflation	inflation	NOUN
fcis-12004	52	7	operation	operation	NOUN
fcis-12004	52	8	,	,	PUNCT
fcis-12004	52	9	i3d	i3d	PRON
fcis-12004	52	10	expands	expand	VERB
fcis-12004	52	11	these	these	DET
fcis-12004	52	12	2d	2d	NUM
fcis-12004	52	13	convolutional	convolutional	ADJ
fcis-12004	52	14	weights	weight	NOUN
fcis-12004	52	15	into	into	ADP
fcis-12004	52	16	3d	3d	NUM
fcis-12004	52	17	convolutional	convolutional	ADJ
fcis-12004	52	18	weights	weight	NOUN
fcis-12004	52	19	suitable	suitable	ADJ
fcis-12004	52	20	for	for	ADP
fcis-12004	52	21	the	the	DET
fcis-12004	52	22	time	time	NOUN
fcis-12004	52	23	dimension	dimension	NOUN
fcis-12004	52	24	.	.	PUNCT
fcis-12004	53	1	this	this	DET
fcis-12004	53	2	inflation	inflation	NOUN
fcis-12004	53	3	operation	operation	NOUN
fcis-12004	53	4	transforms	transform	VERB
fcis-12004	53	5	the	the	DET
fcis-12004	53	6	2d	2d	NUM
fcis-12004	53	7	weights	weight	NOUN
fcis-12004	53	8	by	by	ADP
fcis-12004	53	9	adding	add	VERB
fcis-12004	53	10	an	an	DET
fcis-12004	53	11	additional	additional	ADJ
fcis-12004	53	12	temporal	temporal	ADJ
fcis-12004	53	13	dimension	dimension	NOUN
fcis-12004	53	14	,	,	PUNCT
fcis-12004	53	15	allowing	allow	VERB
fcis-12004	53	16	the	the	DET
fcis-12004	53	17	network	network	NOUN
fcis-12004	53	18	to	to	PART
fcis-12004	53	19	perform	perform	VERB
fcis-12004	53	20	convolutional	convolutional	ADJ
fcis-12004	53	21	operations	operation	NOUN
fcis-12004	53	22	in	in	ADP
fcis-12004	53	23	time	time	NOUN
fcis-12004	53	24	.	.	PUNCT
fcis-12004	54	1	this	this	PRON
fcis-12004	54	2	allows	allow	VERB
fcis-12004	54	3	i3d	i3d	NOUN
fcis-12004	54	4	to	to	PART
fcis-12004	54	5	better	well	ADV
fcis-12004	54	6	capture	capture	VERB
fcis-12004	54	7	temporal	temporal	ADJ
fcis-12004	54	8	information	information	NOUN
fcis-12004	54	9	and	and	CCONJ
fcis-12004	54	10	dynamic	dynamic	ADJ
fcis-12004	54	11	changes	change	NOUN
fcis-12004	54	12	in	in	ADP
fcis-12004	54	13	the	the	DET
fcis-12004	54	14	video	video	NOUN
fcis-12004	54	15	data	datum	NOUN
fcis-12004	54	16	.	.	PUNCT
fcis-12004	55	1	next	next	ADV
fcis-12004	55	2	,	,	PUNCT
fcis-12004	55	3	the	the	DET
fcis-12004	55	4	input	input	NOUN
fcis-12004	55	5	is	be	AUX
fcis-12004	55	6	a	a	DET
fcis-12004	55	7	video	video	NOUN
fcis-12004	55	8	clip	clip	NOUN
fcis-12004	55	9	consisting	consist	VERB
fcis-12004	55	10	of	of	ADP
fcis-12004	55	11	a	a	DET
fcis-12004	55	12	series	series	NOUN
fcis-12004	55	13	of	of	ADP
fcis-12004	55	14	consecutive	consecutive	ADJ
fcis-12004	55	15	video	video	NOUN
fcis-12004	55	16	frames	frame	NOUN
fcis-12004	55	17	that	that	PRON
fcis-12004	55	18	are	be	AUX
fcis-12004	55	19	closely	closely	ADV
fcis-12004	55	20	connected	connect	VERB
fcis-12004	55	21	in	in	ADP
fcis-12004	55	22	time	time	NOUN
fcis-12004	55	23	and	and	CCONJ
fcis-12004	55	24	represent	represent	VERB
fcis-12004	55	25	video	video	NOUN
fcis-12004	55	26	content	content	NOUN
fcis-12004	55	27	over	over	ADP
fcis-12004	55	28	a	a	DET
fcis-12004	55	29	period	period	NOUN
fcis-12004	55	30	of	of	ADP
fcis-12004	55	31	time	time	NOUN
fcis-12004	55	32	.	.	PUNCT
fcis-12004	56	1	these	these	DET
fcis-12004	56	2	frames	frame	NOUN
fcis-12004	56	3	are	be	AUX
fcis-12004	56	4	preprocessed	preprocesse	VERB
fcis-12004	56	5	and	and	CCONJ
fcis-12004	56	6	normalized	normalize	VERB
fcis-12004	56	7	to	to	PART
fcis-12004	56	8	serve	serve	VERB
fcis-12004	56	9	as	as	ADP
fcis-12004	56	10	inputs	input	NOUN
fcis-12004	56	11	to	to	ADP
fcis-12004	56	12	i3d	i3d	PROPN
fcis-12004	57	1	[	[	X
fcis-12004	57	2	5	5	NUM
fcis-12004	57	3	]	]	PUNCT
fcis-12004	57	4	.	.	PUNCT
fcis-12004	58	1	in	in	ADP
fcis-12004	58	2	the	the	DET
fcis-12004	58	3	core	core	ADJ
fcis-12004	58	4	part	part	NOUN
fcis-12004	58	5	of	of	ADP
fcis-12004	58	6	the	the	DET
fcis-12004	58	7	network	network	NOUN
fcis-12004	58	8	,	,	PUNCT
fcis-12004	58	9	i.e.	i.e.	X
fcis-12004	58	10	,	,	PUNCT
fcis-12004	58	11	the	the	DET
fcis-12004	58	12	3d	3d	ADJ
fcis-12004	58	13	convolutional	convolutional	ADJ
fcis-12004	58	14	layer	layer	NOUN
fcis-12004	58	15	,	,	PUNCT
fcis-12004	58	16	i3d	i3d	PRON
fcis-12004	58	17	uses	use	VERB
fcis-12004	58	18	an	an	DET
fcis-12004	58	19	inflated	inflated	ADJ
fcis-12004	58	20	3d	3d	NUM
fcis-12004	58	21	convolutional	convolutional	ADJ
fcis-12004	58	22	kernel	kernel	NOUN
fcis-12004	58	23	to	to	PART
fcis-12004	58	24	perform	perform	VERB
fcis-12004	58	25	a	a	DET
fcis-12004	58	26	convolutional	convolutional	ADJ
fcis-12004	58	27	operation	operation	NOUN
fcis-12004	58	28	on	on	ADP
fcis-12004	58	29	the	the	DET
fcis-12004	58	30	sequence	sequence	NOUN
fcis-12004	58	31	of	of	ADP
fcis-12004	58	32	input	input	NOUN
fcis-12004	58	33	video	video	NOUN
fcis-12004	58	34	frames	frame	NOUN
fcis-12004	58	35	.	.	PUNCT
fcis-12004	59	1	this	this	PRON
fcis-12004	59	2	allows	allow	VERB
fcis-12004	59	3	the	the	DET
fcis-12004	59	4	network	network	NOUN
fcis-12004	59	5	to	to	PART
fcis-12004	59	6	capture	capture	VERB
fcis-12004	59	7	the	the	DET
fcis-12004	59	8	motion	motion	NOUN
fcis-12004	59	9	and	and	CCONJ
fcis-12004	59	10	dynamics	dynamic	NOUN
fcis-12004	59	11	information	information	NOUN
fcis-12004	59	12	of	of	ADP
fcis-12004	59	13	the	the	DET
fcis-12004	59	14	video	video	NOUN
fcis-12004	59	15	in	in	ADP
fcis-12004	59	16	the	the	DET
fcis-12004	59	17	time	time	NOUN
fcis-12004	59	18	dimension	dimension	NOUN
fcis-12004	59	19	,	,	PUNCT
fcis-12004	59	20	giving	give	VERB
fcis-12004	59	21	it	it	PRON
fcis-12004	59	22	an	an	DET
fcis-12004	59	23	advantage	advantage	NOUN
fcis-12004	59	24	when	when	SCONJ
fcis-12004	59	25	dealing	deal	VERB
fcis-12004	59	26	with	with	ADP
fcis-12004	59	27	spatio	spatio	PROPN
fcis-12004	59	28	-	-	PUNCT
fcis-12004	59	29	temporal	temporal	ADJ
fcis-12004	59	30	features	feature	NOUN
fcis-12004	59	31	such	such	ADJ
fcis-12004	59	32	as	as	ADP
fcis-12004	59	33	actions	action	NOUN
fcis-12004	59	34	and	and	CCONJ
fcis-12004	59	35	poses	pose	NOUN
fcis-12004	59	36	.	.	PUNCT
fcis-12004	60	1	pooling	pool	VERB
fcis-12004	60	2	and	and	CCONJ
fcis-12004	60	3	fullyconnected	fullyconnecte	VERB
fcis-12004	60	4	layers	layer	NOUN
fcis-12004	60	5	are	be	AUX
fcis-12004	60	6	used	use	VERB
fcis-12004	60	7	to	to	PART
fcis-12004	60	8	further	far	ADV
fcis-12004	60	9	compress	compress	VERB
fcis-12004	60	10	the	the	DET
fcis-12004	60	11	dimensionality	dimensionality	NOUN
fcis-12004	60	12	of	of	ADP
fcis-12004	60	13	the	the	DET
fcis-12004	60	14	feature	feature	NOUN
fcis-12004	60	15	maps	map	NOUN
fcis-12004	60	16	and	and	CCONJ
fcis-12004	60	17	generate	generate	VERB
fcis-12004	60	18	higher	high	ADJ
fcis-12004	60	19	-	-	PUNCT
fcis-12004	60	20	level	level	NOUN
fcis-12004	60	21	feature	feature	NOUN
fcis-12004	60	22	representations	representation	NOUN
fcis-12004	60	23	,	,	PUNCT
fcis-12004	60	24	which	which	PRON
fcis-12004	60	25	help	help	VERB
fcis-12004	60	26	the	the	DET
fcis-12004	60	27	network	network	NOUN
fcis-12004	60	28	better	well	ADV
fcis-12004	60	29	understand	understand	VERB
fcis-12004	60	30	the	the	DET
fcis-12004	60	31	abstract	abstract	ADJ
fcis-12004	60	32	features	feature	NOUN
fcis-12004	60	33	of	of	ADP
fcis-12004	60	34	the	the	DET
fcis-12004	60	35	video	video	NOUN
fcis-12004	60	36	content	content	NOUN
fcis-12004	60	37	.	.	PUNCT
fcis-12004	61	1	finally	finally	ADV
fcis-12004	61	2	,	,	PUNCT
fcis-12004	61	3	i3d	i3d	PROPN
fcis-12004	61	4	adds	add	VERB
fcis-12004	61	5	a	a	DET
fcis-12004	61	6	classifier	classifier	NOUN
fcis-12004	61	7	layer	layer	NOUN
fcis-12004	61	8	on	on	ADP
fcis-12004	61	9	top	top	NOUN
fcis-12004	61	10	to	to	PART
fcis-12004	61	11	map	map	VERB
fcis-12004	61	12	the	the	DET
fcis-12004	61	13	network	network	NOUN
fcis-12004	61	14	output	output	NOUN
fcis-12004	61	15	to	to	ADP
fcis-12004	61	16	different	different	ADJ
fcis-12004	61	17	categories	category	NOUN
fcis-12004	61	18	or	or	CCONJ
fcis-12004	61	19	action	action	NOUN
fcis-12004	61	20	labels	label	NOUN
fcis-12004	61	21	.	.	PUNCT
fcis-12004	62	1	for	for	ADP
fcis-12004	62	2	the	the	DET
fcis-12004	62	3	binary	binary	ADJ
fcis-12004	62	4	classification	classification	NOUN
fcis-12004	62	5	problem	problem	NOUN
fcis-12004	62	6	,	,	PUNCT
fcis-12004	62	7	this	this	DET
fcis-12004	62	8	paper	paper	NOUN
fcis-12004	62	9	uses	use	VERB
fcis-12004	62	10	a	a	DET
fcis-12004	62	11	sigmoid	sigmoid	NOUN
fcis-12004	62	12	activation	activation	NOUN
fcis-12004	62	13	function	function	VERB
fcis-12004	62	14	to	to	PART
fcis-12004	62	15	obtain	obtain	VERB
fcis-12004	62	16	the	the	DET
fcis-12004	62	17	probability	probability	NOUN
fcis-12004	62	18	that	that	SCONJ
fcis-12004	62	19	the	the	DET
fcis-12004	62	20	representation	representation	NOUN
fcis-12004	62	21	belongs	belong	VERB
fcis-12004	62	22	to	to	ADP
fcis-12004	62	23	a	a	DET
fcis-12004	62	24	positive	positive	ADJ
fcis-12004	62	25	class	class	NOUN
fcis-12004	62	26	,	,	PUNCT
fcis-12004	62	27	as	as	SCONJ
fcis-12004	62	28	shown	show	VERB
fcis-12004	62	29	in	in	ADP
fcis-12004	62	30	figure	figure	NOUN
fcis-12004	62	31	2	2	NUM
fcis-12004	62	32	.	.	PUNCT
fcis-12004	63	1	this	this	PRON
fcis-12004	63	2	allows	allow	VERB
fcis-12004	63	3	i3d	i3d	NOUN
fcis-12004	63	4	to	to	PART
fcis-12004	63	5	provide	provide	VERB
fcis-12004	63	6	accurate	accurate	ADJ
fcis-12004	63	7	classification	classification	NOUN
fcis-12004	63	8	predictions	prediction	NOUN
fcis-12004	63	9	for	for	ADP
fcis-12004	63	10	the	the	DET
fcis-12004	63	11	input	input	NOUN
fcis-12004	63	12	video	video	NOUN
fcis-12004	63	13	clips	clip	NOUN
fcis-12004	63	14	.	.	PUNCT
fcis-12004	64	1	fig	fig	NOUN
fcis-12004	64	2	2	2	NUM
fcis-12004	64	3	.	.	NOUN
fcis-12004	64	4	sigmoid	sigmoid	NOUN
fcis-12004	64	5	activation	activation	NOUN
fcis-12004	64	6	function	function	NOUN
fcis-12004	64	7	example	example	NOUN
fcis-12004	64	8	the	the	DET
fcis-12004	64	9	strength	strength	NOUN
fcis-12004	64	10	of	of	ADP
fcis-12004	64	11	i3d	i3d	PROPN
fcis-12004	64	12	lies	lie	VERB
fcis-12004	64	13	in	in	ADP
fcis-12004	64	14	its	its	PRON
fcis-12004	64	15	capability	capability	NOUN
fcis-12004	64	16	and	and	CCONJ
fcis-12004	64	17	flexibility	flexibility	NOUN
fcis-12004	64	18	.	.	PUNCT
fcis-12004	65	1	by	by	ADP
fcis-12004	65	2	extending	extend	VERB
fcis-12004	65	3	2d	2d	NUM
fcis-12004	65	4	weights	weight	NOUN
fcis-12004	65	5	to	to	ADP
fcis-12004	65	6	3d	3d	NUM
fcis-12004	65	7	weights	weight	NOUN
fcis-12004	65	8	,	,	PUNCT
fcis-12004	65	9	i3d	i3d	PRON
fcis-12004	65	10	is	be	AUX
fcis-12004	65	11	able	able	ADJ
fcis-12004	65	12	to	to	PART
fcis-12004	65	13	process	process	VERB
fcis-12004	65	14	both	both	CCONJ
fcis-12004	65	15	spatial	spatial	ADJ
fcis-12004	65	16	and	and	CCONJ
fcis-12004	65	17	temporal	temporal	ADJ
fcis-12004	65	18	information	information	NOUN
fcis-12004	65	19	of	of	ADP
fcis-12004	65	20	a	a	DET
fcis-12004	65	21	video	video	NOUN
fcis-12004	65	22	in	in	ADP
fcis-12004	65	23	a	a	DET
fcis-12004	65	24	single	single	ADJ
fcis-12004	65	25	network	network	NOUN
fcis-12004	65	26	without	without	ADP
fcis-12004	65	27	additional	additional	ADJ
fcis-12004	65	28	processing	processing	NOUN
fcis-12004	65	29	steps	step	NOUN
fcis-12004	65	30	.	.	PUNCT
fcis-12004	66	1	this	this	PRON
fcis-12004	66	2	allows	allow	VERB
fcis-12004	66	3	it	it	PRON
fcis-12004	66	4	to	to	PART
fcis-12004	66	5	better	well	ADV
fcis-12004	66	6	capture	capture	VERB
fcis-12004	66	7	motion	motion	NOUN
fcis-12004	66	8	and	and	CCONJ
fcis-12004	66	9	dynamic	dynamic	ADJ
fcis-12004	66	10	patterns	pattern	NOUN
fcis-12004	66	11	in	in	ADP
fcis-12004	66	12	videos	video	NOUN
fcis-12004	66	13	,	,	PUNCT
fcis-12004	66	14	providing	provide	VERB
fcis-12004	66	15	excellent	excellent	ADJ
fcis-12004	66	16	performance	performance	NOUN
fcis-12004	66	17	for	for	ADP
fcis-12004	66	18	tasks	task	NOUN
fcis-12004	66	19	such	such	ADJ
fcis-12004	66	20	as	as	ADP
fcis-12004	66	21	video	video	NOUN
fcis-12004	66	22	classification	classification	NOUN
fcis-12004	66	23	and	and	CCONJ
fcis-12004	66	24	action	action	NOUN
fcis-12004	66	25	recognition	recognition	NOUN
fcis-12004	66	26	.	.	PUNCT
fcis-12004	67	1	in	in	ADP
fcis-12004	67	2	the	the	DET
fcis-12004	67	3	training	training	NOUN
fcis-12004	67	4	phase	phase	NOUN
fcis-12004	67	5	,	,	PUNCT
fcis-12004	67	6	i3d	i3d	PRON
fcis-12004	67	7	can	can	AUX
fcis-12004	67	8	start	start	VERB
fcis-12004	67	9	with	with	ADP
fcis-12004	67	10	pre	pre	ADJ
fcis-12004	67	11	-	-	ADJ
fcis-12004	67	12	trained	train	VERB
fcis-12004	67	13	weights	weight	NOUN
fcis-12004	67	14	from	from	ADP
fcis-12004	67	15	the	the	DET
fcis-12004	67	16	image	image	NOUN
fcis-12004	67	17	domain	domain	NOUN
fcis-12004	67	18	105	105	NUM
fcis-12004	67	19	and	and	CCONJ
fcis-12004	67	20	take	take	VERB
fcis-12004	67	21	advantage	advantage	NOUN
fcis-12004	67	22	of	of	ADP
fcis-12004	67	23	migration	migration	NOUN
fcis-12004	67	24	learning	learn	VERB
fcis-12004	67	25	to	to	PART
fcis-12004	67	26	accelerate	accelerate	VERB
fcis-12004	67	27	model	model	NOUN
fcis-12004	67	28	convergence	convergence	NOUN
fcis-12004	67	29	and	and	CCONJ
fcis-12004	67	30	improve	improve	VERB
fcis-12004	67	31	performance	performance	NOUN
fcis-12004	67	32	.	.	PUNCT
fcis-12004	68	1	2.3	2.3	NUM
fcis-12004	68	2	.	.	PUNCT
fcis-12004	69	1	multi	multi	ADJ
fcis-12004	69	2	-	-	NOUN
fcis-12004	69	3	example	example	ADJ
fcis-12004	69	4	learning	learning	NOUN
fcis-12004	69	5	in	in	ADP
fcis-12004	69	6	terms	term	NOUN
fcis-12004	69	7	of	of	ADP
fcis-12004	69	8	the	the	DET
fcis-12004	69	9	ambiguity	ambiguity	NOUN
fcis-12004	69	10	of	of	ADP
fcis-12004	69	11	the	the	DET
fcis-12004	69	12	training	training	NOUN
fcis-12004	69	13	data	datum	NOUN
fcis-12004	69	14	,	,	PUNCT
fcis-12004	69	15	there	there	PRON
fcis-12004	69	16	are	be	VERB
fcis-12004	69	17	three	three	NUM
fcis-12004	69	18	broad	broad	ADJ
fcis-12004	69	19	learning	learning	NOUN
fcis-12004	69	20	frameworks	framework	NOUN
fcis-12004	69	21	:	:	PUNCT
fcis-12004	69	22	supervised	supervised	ADJ
fcis-12004	69	23	learning	learning	NOUN
fcis-12004	69	24	,	,	PUNCT
fcis-12004	69	25	unsupervised	unsupervised	ADJ
fcis-12004	69	26	learning	learning	NOUN
fcis-12004	69	27	,	,	PUNCT
fcis-12004	69	28	and	and	CCONJ
fcis-12004	69	29	reinforcement	reinforcement	NOUN
fcis-12004	69	30	learning	learning	NOUN
fcis-12004	69	31	.	.	PUNCT
fcis-12004	70	1	supervised	supervised	ADJ
fcis-12004	70	2	learning	learning	NOUN
fcis-12004	70	3	has	have	VERB
fcis-12004	70	4	sample	sample	NOUN
fcis-12004	70	5	examples	example	NOUN
fcis-12004	70	6	labeled	label	VERB
fcis-12004	70	7	;	;	PUNCT
fcis-12004	70	8	unsupervised	unsupervised	ADJ
fcis-12004	70	9	learning	learning	NOUN
fcis-12004	70	10	does	do	VERB
fcis-12004	70	11	not	not	PART
fcis-12004	70	12	.	.	PUNCT
fcis-12004	71	1	and	and	CCONJ
fcis-12004	71	2	multi	multi	ADJ
fcis-12004	71	3	-	-	ADJ
fcis-12004	71	4	sample	sample	ADJ
fcis-12004	71	5	learning	learning	NOUN
fcis-12004	71	6	can	can	AUX
fcis-12004	71	7	be	be	AUX
fcis-12004	71	8	considered	consider	VERB
fcis-12004	71	9	as	as	ADP
fcis-12004	71	10	a	a	DET
fcis-12004	71	11	new	new	ADJ
fcis-12004	71	12	kind	kind	NOUN
fcis-12004	71	13	of	of	ADP
fcis-12004	71	14	product	product	NOUN
fcis-12004	71	15	that	that	PRON
fcis-12004	71	16	is	be	AUX
fcis-12004	71	17	different	different	ADJ
fcis-12004	71	18	from	from	ADP
fcis-12004	71	19	the	the	DET
fcis-12004	71	20	three	three	NUM
fcis-12004	71	21	major	major	ADJ
fcis-12004	71	22	learning	learning	NOUN
fcis-12004	71	23	frameworks	framework	NOUN
fcis-12004	71	24	,	,	PUNCT
fcis-12004	71	25	and	and	CCONJ
fcis-12004	71	26	the	the	DET
fcis-12004	71	27	mudslide	mudslide	ADJ
fcis-12004	71	28	detection	detection	NOUN
fcis-12004	71	29	studied	study	VERB
fcis-12004	71	30	in	in	ADP
fcis-12004	71	31	this	this	DET
fcis-12004	71	32	paper	paper	NOUN
fcis-12004	71	33	precisely	precisely	ADV
fcis-12004	71	34	utilizes	utilize	VERB
fcis-12004	71	35	the	the	DET
fcis-12004	71	36	method	method	NOUN
fcis-12004	71	37	of	of	ADP
fcis-12004	71	38	multi	multi	ADJ
fcis-12004	71	39	-	-	ADJ
fcis-12004	71	40	sample	sample	ADJ
fcis-12004	71	41	learning	learning	NOUN
fcis-12004	72	1	[	[	X
fcis-12004	72	2	6	6	NUM
fcis-12004	72	3	]	]	PUNCT
fcis-12004	72	4	.	.	PUNCT
fcis-12004	73	1	the	the	DET
fcis-12004	73	2	usual	usual	ADJ
fcis-12004	73	3	deep	deep	ADJ
fcis-12004	73	4	learning	learning	NOUN
fcis-12004	73	5	training	training	NOUN
fcis-12004	73	6	is	be	AUX
fcis-12004	73	7	a	a	DET
fcis-12004	73	8	sample	sample	NOUN
fcis-12004	73	9	corresponds	correspond	VERB
fcis-12004	73	10	to	to	ADP
fcis-12004	73	11	a	a	DET
fcis-12004	73	12	label	label	NOUN
fcis-12004	73	13	,	,	PUNCT
fcis-12004	73	14	while	while	SCONJ
fcis-12004	73	15	in	in	ADP
fcis-12004	73	16	multiple	multiple	ADJ
fcis-12004	73	17	examples	example	NOUN
fcis-12004	73	18	learning	learn	VERB
fcis-12004	73	19	,	,	PUNCT
fcis-12004	73	20	the	the	DET
fcis-12004	73	21	concept	concept	NOUN
fcis-12004	73	22	of	of	ADP
fcis-12004	73	23	packet	packet	NOUN
fcis-12004	73	24	appears	appear	VERB
fcis-12004	73	25	,	,	PUNCT
fcis-12004	73	26	one	one	NUM
fcis-12004	73	27	corresponds	correspond	VERB
fcis-12004	73	28	to	to	ADP
fcis-12004	73	29	a	a	DET
fcis-12004	73	30	label	label	NOUN
fcis-12004	73	31	,	,	PUNCT
fcis-12004	73	32	a	a	DET
fcis-12004	73	33	positive	positive	ADJ
fcis-12004	73	34	packet	packet	NOUN
fcis-12004	73	35	needs	need	VERB
fcis-12004	73	36	at	at	ADV
fcis-12004	73	37	least	least	ADV
fcis-12004	73	38	one	one	NUM
fcis-12004	73	39	positive	positive	ADJ
fcis-12004	73	40	sample	sample	NOUN
fcis-12004	73	41	,	,	PUNCT
fcis-12004	73	42	a	a	DET
fcis-12004	73	43	negative	negative	ADJ
fcis-12004	73	44	packet	packet	NOUN
fcis-12004	73	45	can	can	AUX
fcis-12004	73	46	only	only	ADV
fcis-12004	73	47	be	be	AUX
fcis-12004	73	48	all	all	PRON
fcis-12004	73	49	negative	negative	ADJ
fcis-12004	73	50	samples	sample	NOUN
fcis-12004	73	51	,	,	PUNCT
fcis-12004	73	52	and	and	CCONJ
fcis-12004	73	53	a	a	DET
fcis-12004	73	54	packet	packet	NOUN
fcis-12004	73	55	,	,	PUNCT
fcis-12004	73	56	containing	contain	VERB
fcis-12004	73	57	multiple	multiple	ADJ
fcis-12004	73	58	samples	sample	NOUN
fcis-12004	73	59	.	.	PUNCT
fcis-12004	74	1	in	in	ADP
fcis-12004	74	2	this	this	DET
fcis-12004	74	3	paper	paper	NOUN
fcis-12004	74	4	,	,	PUNCT
fcis-12004	74	5	the	the	DET
fcis-12004	74	6	original	original	ADJ
fcis-12004	74	7	dataset	dataset	NOUN
fcis-12004	74	8	is	be	AUX
fcis-12004	74	9	preprocessed	preprocesse	VERB
fcis-12004	74	10	,	,	PUNCT
fcis-12004	74	11	the	the	DET
fcis-12004	74	12	video	video	NOUN
fcis-12004	74	13	is	be	AUX
fcis-12004	74	14	divided	divide	VERB
fcis-12004	74	15	into	into	ADP
fcis-12004	74	16	several	several	ADJ
fcis-12004	74	17	short	short	ADJ
fcis-12004	74	18	frames	frame	NOUN
fcis-12004	74	19	,	,	PUNCT
fcis-12004	74	20	and	and	CCONJ
fcis-12004	74	21	according	accord	VERB
fcis-12004	74	22	to	to	ADP
fcis-12004	74	23	whether	whether	SCONJ
fcis-12004	74	24	there	there	PRON
fcis-12004	74	25	is	be	VERB
fcis-12004	74	26	anomaly	anomaly	NOUN
fcis-12004	74	27	collection	collection	NOUN
fcis-12004	74	28	into	into	ADP
fcis-12004	74	29	packets	packet	NOUN
fcis-12004	74	30	and	and	CCONJ
fcis-12004	74	31	labeling	labeling	NOUN
fcis-12004	74	32	,	,	PUNCT
fcis-12004	74	33	respectively	respectively	ADV
fcis-12004	74	34	.	.	PUNCT
fcis-12004	75	1	the	the	DET
fcis-12004	75	2	purpose	purpose	NOUN
fcis-12004	75	3	of	of	ADP
fcis-12004	75	4	multi	multi	ADJ
fcis-12004	75	5	-	-	NOUN
fcis-12004	75	6	example	example	ADJ
fcis-12004	75	7	learning	learning	NOUN
fcis-12004	75	8	is	be	AUX
fcis-12004	75	9	to	to	PART
fcis-12004	75	10	build	build	VERB
fcis-12004	75	11	a	a	DET
fcis-12004	75	12	multiexample	multiexample	NOUN
fcis-12004	75	13	classifier	classifier	NOUN
fcis-12004	75	14	using	use	VERB
fcis-12004	75	15	multi	multi	ADJ
fcis-12004	75	16	-	-	ADJ
fcis-12004	75	17	example	example	NOUN
fcis-12004	75	18	packages	package	NOUN
fcis-12004	75	19	with	with	ADP
fcis-12004	75	20	labels	label	NOUN
fcis-12004	75	21	,	,	PUNCT
fcis-12004	75	22	and	and	CCONJ
fcis-12004	75	23	apply	apply	VERB
fcis-12004	75	24	the	the	DET
fcis-12004	75	25	classifier	classifier	NOUN
fcis-12004	75	26	to	to	ADP
fcis-12004	75	27	the	the	DET
fcis-12004	75	28	prediction	prediction	NOUN
fcis-12004	75	29	of	of	ADP
fcis-12004	75	30	other	other	ADJ
fcis-12004	75	31	unknown	unknown	ADJ
fcis-12004	75	32	multi	multi	ADJ
fcis-12004	75	33	-	-	ADJ
fcis-12004	75	34	example	example	NOUN
fcis-12004	75	35	packages	package	NOUN
fcis-12004	75	36	.	.	PUNCT
fcis-12004	76	1	in	in	ADP
fcis-12004	76	2	this	this	DET
fcis-12004	76	3	case	case	NOUN
fcis-12004	76	4	,	,	PUNCT
fcis-12004	76	5	there	there	PRON
fcis-12004	76	6	are	be	VERB
fcis-12004	76	7	two	two	NUM
fcis-12004	76	8	types	type	NOUN
fcis-12004	76	9	of	of	ADP
fcis-12004	76	10	prediction	prediction	NOUN
fcis-12004	76	11	classes	class	NOUN
fcis-12004	76	12	,	,	PUNCT
fcis-12004	76	13	one	one	NUM
fcis-12004	76	14	is	be	AUX
fcis-12004	76	15	package	package	NOUN
fcis-12004	76	16	prediction	prediction	NOUN
fcis-12004	76	17	and	and	CCONJ
fcis-12004	76	18	the	the	DET
fcis-12004	76	19	other	other	ADJ
fcis-12004	76	20	is	be	AUX
fcis-12004	76	21	example	example	NOUN
fcis-12004	76	22	prediction	prediction	NOUN
fcis-12004	76	23	.	.	PUNCT
fcis-12004	77	1	the	the	DET
fcis-12004	77	2	biggest	big	ADJ
fcis-12004	77	3	difference	difference	NOUN
fcis-12004	77	4	between	between	ADP
fcis-12004	77	5	the	the	DET
fcis-12004	77	6	two	two	NUM
fcis-12004	77	7	prediction	prediction	NOUN
fcis-12004	77	8	classes	class	NOUN
fcis-12004	77	9	is	be	AUX
fcis-12004	77	10	the	the	DET
fcis-12004	77	11	cost	cost	NOUN
fcis-12004	77	12	to	to	ADP
fcis-12004	77	13	the	the	DET
fcis-12004	77	14	model	model	NOUN
fcis-12004	77	15	after	after	ADP
fcis-12004	77	16	an	an	DET
fcis-12004	77	17	example	example	NOUN
fcis-12004	77	18	prediction	prediction	NOUN
fcis-12004	77	19	error	error	NOUN
fcis-12004	77	20	.	.	PUNCT
fcis-12004	78	1	for	for	ADP
fcis-12004	78	2	packet	packet	NOUN
fcis-12004	78	3	classification	classification	NOUN
fcis-12004	78	4	,	,	PUNCT
fcis-12004	78	5	if	if	SCONJ
fcis-12004	78	6	a	a	DET
fcis-12004	78	7	needed	need	VERB
fcis-12004	78	8	feature	feature	NOUN
fcis-12004	78	9	is	be	AUX
fcis-12004	78	10	found	find	VERB
fcis-12004	78	11	in	in	ADP
fcis-12004	78	12	a	a	DET
fcis-12004	78	13	packet	packet	NOUN
fcis-12004	78	14	,	,	PUNCT
fcis-12004	78	15	then	then	ADV
fcis-12004	78	16	it	it	PRON
fcis-12004	78	17	is	be	AUX
fcis-12004	78	18	classified	classify	VERB
fcis-12004	78	19	as	as	ADP
fcis-12004	78	20	a	a	DET
fcis-12004	78	21	positive	positive	ADJ
fcis-12004	78	22	example	example	NOUN
fcis-12004	78	23	packet	packet	NOUN
fcis-12004	78	24	,	,	PUNCT
fcis-12004	78	25	and	and	CCONJ
fcis-12004	78	26	the	the	DET
fcis-12004	78	27	features	feature	NOUN
fcis-12004	78	28	of	of	ADP
fcis-12004	78	29	the	the	DET
fcis-12004	78	30	other	other	ADJ
fcis-12004	78	31	examples	example	NOUN
fcis-12004	78	32	in	in	ADP
fcis-12004	78	33	the	the	DET
fcis-12004	78	34	packet	packet	NOUN
fcis-12004	78	35	are	be	AUX
fcis-12004	78	36	not	not	PART
fcis-12004	78	37	concerned	concern	VERB
fcis-12004	78	38	.	.	PUNCT
fcis-12004	79	1	therefore	therefore	ADV
fcis-12004	79	2	,	,	PUNCT
fcis-12004	79	3	at	at	ADP
fcis-12004	79	4	this	this	DET
fcis-12004	79	5	point	point	NOUN
fcis-12004	79	6	,	,	PUNCT
fcis-12004	79	7	fp	fp	X
fcis-12004	79	8	(	(	PUNCT
fcis-12004	79	9	judged	judge	VERB
fcis-12004	79	10	as	as	ADP
fcis-12004	79	11	a	a	DET
fcis-12004	79	12	positive	positive	ADJ
fcis-12004	79	13	sample	sample	NOUN
fcis-12004	79	14	,	,	PUNCT
fcis-12004	79	15	which	which	PRON
fcis-12004	79	16	is	be	AUX
fcis-12004	79	17	actually	actually	ADV
fcis-12004	79	18	a	a	DET
fcis-12004	79	19	negative	negative	ADJ
fcis-12004	79	20	sample	sample	NOUN
fcis-12004	79	21	)	)	PUNCT
fcis-12004	79	22	and	and	CCONJ
fcis-12004	79	23	fn	fn	INTJ
fcis-12004	79	24	(	(	PUNCT
fcis-12004	79	25	judged	judge	VERB
fcis-12004	79	26	as	as	ADP
fcis-12004	79	27	a	a	DET
fcis-12004	79	28	negative	negative	ADJ
fcis-12004	79	29	sample	sample	NOUN
fcis-12004	79	30	,	,	PUNCT
fcis-12004	79	31	which	which	PRON
fcis-12004	79	32	is	be	AUX
fcis-12004	79	33	actually	actually	ADV
fcis-12004	79	34	a	a	DET
fcis-12004	79	35	positive	positive	ADJ
fcis-12004	79	36	sample	sample	NOUN
fcis-12004	79	37	)	)	PUNCT
fcis-12004	79	38	have	have	VERB
fcis-12004	79	39	no	no	DET
fcis-12004	79	40	effect	effect	NOUN
fcis-12004	79	41	on	on	ADP
fcis-12004	79	42	the	the	DET
fcis-12004	79	43	accuracy	accuracy	NOUN
fcis-12004	79	44	of	of	ADP
fcis-12004	79	45	packet	packet	NOUN
fcis-12004	79	46	classification	classification	NOUN
fcis-12004	79	47	,	,	PUNCT
fcis-12004	79	48	but	but	CCONJ
fcis-12004	79	49	increase	increase	VERB
fcis-12004	79	50	the	the	DET
fcis-12004	79	51	example	example	NOUN
fcis-12004	79	52	classification	classification	NOUN
fcis-12004	79	53	error	error	NOUN
fcis-12004	79	54	rate	rate	NOUN
fcis-12004	79	55	.	.	PUNCT
fcis-12004	80	1	and	and	CCONJ
fcis-12004	80	2	when	when	SCONJ
fcis-12004	80	3	considering	consider	VERB
fcis-12004	80	4	negative	negative	ADJ
fcis-12004	80	5	packages	package	NOUN
fcis-12004	80	6	,	,	PUNCT
fcis-12004	80	7	an	an	DET
fcis-12004	80	8	fp	fp	NOUN
fcis-12004	80	9	will	will	AUX
fcis-12004	80	10	allow	allow	VERB
fcis-12004	80	11	a	a	DET
fcis-12004	80	12	package	package	NOUN
fcis-12004	80	13	to	to	PART
fcis-12004	80	14	be	be	AUX
fcis-12004	80	15	misclassified	misclassifie	VERB
fcis-12004	80	16	(	(	PUNCT
fcis-12004	80	17	as	as	ADV
fcis-12004	80	18	long	long	ADV
fcis-12004	80	19	as	as	SCONJ
fcis-12004	80	20	there	there	PRON
fcis-12004	80	21	is	be	VERB
fcis-12004	80	22	a	a	DET
fcis-12004	80	23	feature	feature	NOUN
fcis-12004	80	24	in	in	ADP
fcis-12004	80	25	the	the	DET
fcis-12004	80	26	negative	negative	ADJ
fcis-12004	80	27	package	package	NOUN
fcis-12004	80	28	that	that	PRON
fcis-12004	80	29	is	be	AUX
fcis-12004	80	30	needed	need	VERB
fcis-12004	80	31	for	for	ADP
fcis-12004	80	32	a	a	DET
fcis-12004	80	33	positive	positive	ADJ
fcis-12004	80	34	example	example	NOUN
fcis-12004	80	35	,	,	PUNCT
fcis-12004	80	36	the	the	DET
fcis-12004	80	37	package	package	NOUN
fcis-12004	80	38	will	will	AUX
fcis-12004	80	39	be	be	AUX
fcis-12004	80	40	classified	classify	VERB
fcis-12004	80	41	as	as	ADP
fcis-12004	80	42	a	a	DET
fcis-12004	80	43	positive	positive	ADJ
fcis-12004	80	44	package	package	NOUN
fcis-12004	80	45	)	)	PUNCT
fcis-12004	81	1	[	[	X
fcis-12004	81	2	7	7	NUM
fcis-12004	81	3	]	]	PUNCT
fcis-12004	81	4	.	.	PUNCT
fcis-12004	82	1	in	in	ADP
fcis-12004	82	2	this	this	DET
fcis-12004	82	3	paper	paper	NOUN
fcis-12004	82	4	,	,	PUNCT
fcis-12004	82	5	the	the	DET
fcis-12004	82	6	short	short	ADJ
fcis-12004	82	7	videos	video	NOUN
fcis-12004	82	8	that	that	PRON
fcis-12004	82	9	have	have	AUX
fcis-12004	82	10	been	be	AUX
fcis-12004	82	11	segmented	segment	VERB
fcis-12004	82	12	are	be	AUX
fcis-12004	82	13	assembled	assemble	VERB
fcis-12004	82	14	into	into	ADP
fcis-12004	82	15	packets	packet	NOUN
fcis-12004	82	16	and	and	CCONJ
fcis-12004	82	17	labeled	label	VERB
fcis-12004	82	18	,	,	PUNCT
fcis-12004	82	19	and	and	CCONJ
fcis-12004	82	20	then	then	ADV
fcis-12004	82	21	fed	feed	VERB
fcis-12004	82	22	into	into	ADP
fcis-12004	82	23	the	the	DET
fcis-12004	82	24	training	training	NOUN
fcis-12004	82	25	model	model	NOUN
fcis-12004	82	26	,	,	PUNCT
fcis-12004	82	27	as	as	SCONJ
fcis-12004	82	28	shown	show	VERB
fcis-12004	82	29	in	in	ADP
fcis-12004	82	30	fig	fig	NOUN
fcis-12004	82	31	.	.	PUNCT
fcis-12004	83	1	3	3	X
fcis-12004	83	2	.	.	X
fcis-12004	83	3	a	a	DET
fcis-12004	83	4	multi	multi	ADJ
fcis-12004	83	5	-	-	ADJ
fcis-12004	83	6	example	example	ADJ
fcis-12004	83	7	classifier	classifier	NOUN
fcis-12004	83	8	is	be	AUX
fcis-12004	83	9	built	build	VERB
fcis-12004	83	10	by	by	ADP
fcis-12004	83	11	learning	learn	VERB
fcis-12004	83	12	multi	multi	ADJ
fcis-12004	83	13	-	-	ADJ
fcis-12004	83	14	example	example	NOUN
fcis-12004	83	15	packages	package	NOUN
fcis-12004	83	16	with	with	ADP
fcis-12004	83	17	classification	classification	NOUN
fcis-12004	83	18	labels	label	NOUN
fcis-12004	83	19	,	,	PUNCT
fcis-12004	83	20	and	and	CCONJ
fcis-12004	83	21	the	the	DET
fcis-12004	83	22	classifier	classifier	NOUN
fcis-12004	83	23	is	be	AUX
fcis-12004	83	24	applied	apply	VERB
fcis-12004	83	25	to	to	ADP
fcis-12004	83	26	the	the	DET
fcis-12004	83	27	prediction	prediction	NOUN
fcis-12004	83	28	of	of	ADP
fcis-12004	83	29	unknown	unknown	ADJ
fcis-12004	83	30	multi	multi	ADJ
fcis-12004	83	31	-	-	ADJ
fcis-12004	83	32	example	example	NOUN
fcis-12004	83	33	packages	package	NOUN
fcis-12004	83	34	.	.	PUNCT
fcis-12004	84	1	using	use	VERB
fcis-12004	84	2	multiple	multiple	ADJ
fcis-12004	84	3	example	example	NOUN
fcis-12004	84	4	packages	package	NOUN
fcis-12004	84	5	for	for	ADP
fcis-12004	84	6	training	training	NOUN
fcis-12004	84	7	can	can	AUX
fcis-12004	84	8	be	be	AUX
fcis-12004	84	9	more	more	ADV
fcis-12004	84	10	convenient	convenient	ADJ
fcis-12004	84	11	and	and	CCONJ
fcis-12004	84	12	simpler	simple	ADJ
fcis-12004	84	13	.	.	PUNCT
fcis-12004	85	1	fig	fig	NOUN
fcis-12004	85	2	3	3	NUM
fcis-12004	85	3	.	.	PUNCT
fcis-12004	86	1	multi	multi	ADJ
fcis-12004	86	2	-	-	NOUN
fcis-12004	86	3	example	example	ADJ
fcis-12004	86	4	learning	learn	VERB
fcis-12004	86	5	3	3	NUM
fcis-12004	86	6	.	.	PUNCT
fcis-12004	86	7	convolutional	convolutional	ADJ
fcis-12004	86	8	network	network	NOUN
fcis-12004	86	9	-	-	PUNCT
fcis-12004	86	10	based	base	VERB
fcis-12004	86	11	detection	detection	NOUN
fcis-12004	86	12	methods	method	VERB
fcis-12004	86	13	the	the	DET
fcis-12004	86	14	cause	cause	NOUN
fcis-12004	86	15	of	of	ADP
fcis-12004	86	16	mudslide	mudslide	ADJ
fcis-12004	86	17	disaster	disaster	NOUN
fcis-12004	86	18	is	be	AUX
fcis-12004	86	19	a	a	DET
fcis-12004	86	20	solid	solid	ADJ
fcis-12004	86	21	-	-	PUNCT
fcis-12004	86	22	liquid	liquid	ADJ
fcis-12004	86	23	two	two	NUM
fcis-12004	86	24	-	-	PUNCT
fcis-12004	86	25	phase	phase	NOUN
fcis-12004	86	26	fluid	fluid	NOUN
fcis-12004	86	27	with	with	ADP
fcis-12004	86	28	a	a	DET
fcis-12004	86	29	large	large	ADJ
fcis-12004	86	30	number	number	NOUN
fcis-12004	86	31	of	of	ADP
fcis-12004	86	32	solid	solid	ADJ
fcis-12004	86	33	materials	material	NOUN
fcis-12004	86	34	such	such	ADJ
fcis-12004	86	35	as	as	ADP
fcis-12004	86	36	mud	mud	NOUN
fcis-12004	86	37	,	,	PUNCT
fcis-12004	86	38	sand	sand	NOUN
fcis-12004	86	39	and	and	CCONJ
fcis-12004	86	40	stones	stone	NOUN
fcis-12004	86	41	,	,	PUNCT
fcis-12004	86	42	etc	etc	X
fcis-12004	86	43	.	.	X
fcis-12004	86	44	,	,	PUNCT
fcis-12004	86	45	formed	form	VERB
fcis-12004	86	46	due	due	ADP
fcis-12004	86	47	to	to	ADP
fcis-12004	86	48	a	a	DET
fcis-12004	86	49	large	large	ADJ
fcis-12004	86	50	amount	amount	NOUN
fcis-12004	86	51	of	of	ADP
fcis-12004	86	52	precipitation	precipitation	NOUN
fcis-12004	86	53	for	for	ADP
fcis-12004	86	54	a	a	DET
fcis-12004	86	55	short	short	ADJ
fcis-12004	86	56	period	period	NOUN
fcis-12004	86	57	of	of	ADP
fcis-12004	86	58	time	time	NOUN
fcis-12004	86	59	(	(	PUNCT
fcis-12004	86	60	heavy	heavy	ADJ
fcis-12004	86	61	rainfall	rainfall	NOUN
fcis-12004	86	62	,	,	PUNCT
fcis-12004	86	63	snowmelt	snowmelt	PROPN
fcis-12004	86	64	)	)	PUNCT
fcis-12004	86	65	,	,	PUNCT
fcis-12004	86	66	which	which	PRON
fcis-12004	86	67	is	be	AUX
fcis-12004	86	68	in	in	ADP
fcis-12004	86	69	the	the	DET
fcis-12004	86	70	state	state	NOUN
fcis-12004	86	71	of	of	ADP
fcis-12004	86	72	viscous	viscous	ADJ
fcis-12004	86	73	laminar	laminar	ADJ
fcis-12004	86	74	flow	flow	NOUN
fcis-12004	86	75	or	or	CCONJ
fcis-12004	86	76	dilute	dilute	VERB
fcis-12004	86	77	turbulence	turbulence	NOUN
fcis-12004	86	78	,	,	PUNCT
fcis-12004	86	79	and	and	CCONJ
fcis-12004	86	80	it	it	PRON
fcis-12004	86	81	is	be	AUX
fcis-12004	86	82	a	a	DET
fcis-12004	86	83	mixture	mixture	NOUN
fcis-12004	86	84	of	of	ADP
fcis-12004	86	85	particulate	particulate	NOUN
fcis-12004	86	86	flow	flow	NOUN
fcis-12004	86	87	of	of	ADP
fcis-12004	86	88	high	high	ADJ
fcis-12004	86	89	concentration	concentration	NOUN
fcis-12004	86	90	of	of	ADP
fcis-12004	86	91	solids	solid	NOUN
fcis-12004	86	92	and	and	CCONJ
fcis-12004	86	93	liquids	liquid	NOUN
fcis-12004	86	94	[	[	X
fcis-12004	86	95	8	8	NUM
fcis-12004	86	96	]	]	PUNCT
fcis-12004	86	97	.	.	PUNCT
fcis-12004	87	1	therefore	therefore	ADV
fcis-12004	87	2	,	,	PUNCT
fcis-12004	87	3	it	it	PRON
fcis-12004	87	4	is	be	AUX
fcis-12004	87	5	only	only	ADV
fcis-12004	87	6	necessary	necessary	ADJ
fcis-12004	87	7	to	to	PART
fcis-12004	87	8	look	look	VERB
fcis-12004	87	9	for	for	ADP
fcis-12004	87	10	anomalies	anomaly	NOUN
fcis-12004	87	11	in	in	ADP
fcis-12004	87	12	the	the	DET
fcis-12004	87	13	surface	surface	NOUN
fcis-12004	87	14	in	in	ADP
fcis-12004	87	15	the	the	DET
fcis-12004	87	16	dataset	dataset	NOUN
fcis-12004	87	17	i.e.	i.e.	X
fcis-12004	87	18	,	,	PUNCT
fcis-12004	87	19	the	the	DET
fcis-12004	87	20	presence	presence	NOUN
fcis-12004	87	21	of	of	ADP
fcis-12004	87	22	mudflow	mudflow	NOUN
fcis-12004	87	23	is	be	AUX
fcis-12004	87	24	clearly	clearly	ADV
fcis-12004	87	25	observed	observe	VERB
fcis-12004	87	26	in	in	ADP
fcis-12004	87	27	the	the	DET
fcis-12004	87	28	video	video	NOUN
fcis-12004	87	29	.	.	PUNCT
fcis-12004	88	1	this	this	DET
fcis-12004	88	2	section	section	NOUN
fcis-12004	88	3	explains	explain	VERB
fcis-12004	88	4	the	the	DET
fcis-12004	88	5	methodology	methodology	NOUN
fcis-12004	88	6	for	for	ADP
fcis-12004	88	7	detection	detection	NOUN
fcis-12004	88	8	of	of	ADP
fcis-12004	88	9	debris	debris	NOUN
fcis-12004	88	10	flow	flow	NOUN
fcis-12004	88	11	in	in	ADP
fcis-12004	88	12	the	the	DET
fcis-12004	88	13	video	video	NOUN
fcis-12004	88	14	.	.	PUNCT
fcis-12004	89	1	after	after	ADP
fcis-12004	89	2	obtaining	obtain	VERB
fcis-12004	89	3	the	the	DET
fcis-12004	89	4	monitored	monitor	VERB
fcis-12004	89	5	video	video	NOUN
fcis-12004	89	6	,	,	PUNCT
fcis-12004	89	7	the	the	DET
fcis-12004	89	8	image	image	NOUN
fcis-12004	89	9	frames	frame	NOUN
fcis-12004	89	10	are	be	AUX
fcis-12004	89	11	extracted	extract	VERB
fcis-12004	89	12	from	from	ADP
fcis-12004	89	13	the	the	DET
fcis-12004	89	14	video	video	NOUN
fcis-12004	89	15	file	file	NOUN
fcis-12004	89	16	,	,	PUNCT
fcis-12004	89	17	which	which	PRON
fcis-12004	89	18	can	can	AUX
fcis-12004	89	19	be	be	AUX
fcis-12004	89	20	done	do	VERB
fcis-12004	89	21	in	in	ADP
fcis-12004	89	22	various	various	ADJ
fcis-12004	89	23	ways	way	NOUN
fcis-12004	89	24	depending	depend	VERB
fcis-12004	89	25	on	on	ADP
fcis-12004	89	26	the	the	DET
fcis-12004	89	27	requirements	requirement	NOUN
fcis-12004	89	28	of	of	ADP
fcis-12004	89	29	the	the	DET
fcis-12004	89	30	model	model	NOUN
fcis-12004	89	31	.	.	PUNCT
fcis-12004	90	1	in	in	ADP
fcis-12004	90	2	this	this	DET
fcis-12004	90	3	paper	paper	NOUN
fcis-12004	90	4	,	,	PUNCT
fcis-12004	90	5	the	the	DET
fcis-12004	90	6	video	video	NOUN
fcis-12004	90	7	processing	processing	NOUN
fcis-12004	90	8	library	library	NOUN
fcis-12004	90	9	is	be	AUX
fcis-12004	90	10	used	use	VERB
fcis-12004	90	11	for	for	ADP
fcis-12004	90	12	this	this	DET
fcis-12004	90	13	purpose	purpose	NOUN
fcis-12004	90	14	.	.	PUNCT
fcis-12004	91	1	there	there	PRON
fcis-12004	91	2	is	be	VERB
fcis-12004	91	3	a	a	DET
fcis-12004	91	4	library	library	NOUN
fcis-12004	91	5	(	(	PUNCT
fcis-12004	91	6	opencv	opencv	PROPN
fcis-12004	91	7	)	)	PUNCT
fcis-12004	91	8	in	in	ADP
fcis-12004	91	9	python	python	NOUN
fcis-12004	91	10	which	which	PRON
fcis-12004	91	11	is	be	AUX
fcis-12004	91	12	specialized	specialize	VERB
fcis-12004	91	13	for	for	ADP
fcis-12004	91	14	video	video	NOUN
fcis-12004	91	15	processing	processing	NOUN
fcis-12004	91	16	.	.	PUNCT
fcis-12004	92	1	in	in	ADP
fcis-12004	92	2	this	this	DET
fcis-12004	92	3	paper	paper	NOUN
fcis-12004	92	4	,	,	PUNCT
fcis-12004	92	5	this	this	DET
fcis-12004	92	6	library	library	NOUN
fcis-12004	92	7	is	be	AUX
fcis-12004	92	8	used	use	VERB
fcis-12004	92	9	to	to	PART
fcis-12004	92	10	process	process	VERB
fcis-12004	92	11	the	the	DET
fcis-12004	92	12	video	video	NOUN
fcis-12004	92	13	file	file	NOUN
fcis-12004	92	14	by	by	ADP
fcis-12004	92	15	reading	read	VERB
fcis-12004	92	16	the	the	DET
fcis-12004	92	17	video	video	NOUN
fcis-12004	92	18	frame	frame	NOUN
fcis-12004	92	19	by	by	ADP
fcis-12004	92	20	frame	frame	NOUN
fcis-12004	92	21	and	and	CCONJ
fcis-12004	92	22	setting	set	VERB
fcis-12004	92	23	the	the	DET
fcis-12004	92	24	relevant	relevant	ADJ
fcis-12004	92	25	parameters	parameter	NOUN
fcis-12004	92	26	,	,	PUNCT
fcis-12004	92	27	after	after	ADP
fcis-12004	92	28	which	which	PRON
fcis-12004	92	29	each	each	DET
fcis-12004	92	30	frame	frame	NOUN
fcis-12004	92	31	is	be	AUX
fcis-12004	92	32	saved	save	VERB
fcis-12004	92	33	as	as	ADP
fcis-12004	92	34	an	an	DET
fcis-12004	92	35	image	image	NOUN
fcis-12004	92	36	,	,	PUNCT
fcis-12004	92	37	which	which	PRON
fcis-12004	92	38	can	can	AUX
fcis-12004	92	39	be	be	AUX
fcis-12004	92	40	fed	feed	VERB
fcis-12004	92	41	as	as	ADP
fcis-12004	92	42	an	an	DET
fcis-12004	92	43	input	input	NOUN
fcis-12004	92	44	to	to	ADP
fcis-12004	92	45	the	the	DET
fcis-12004	92	46	deep	deep	ADJ
fcis-12004	92	47	learning	learning	NOUN
fcis-12004	92	48	model	model	NOUN
fcis-12004	92	49	i3d	i3d	PROPN
fcis-12004	92	50	in	in	ADP
fcis-12004	92	51	this	this	DET
fcis-12004	92	52	paper	paper	NOUN
fcis-12004	92	53	for	for	ADP
fcis-12004	92	54	further	further	ADJ
fcis-12004	92	55	processing	processing	NOUN
fcis-12004	92	56	and	and	CCONJ
fcis-12004	92	57	analysis	analysis	NOUN
fcis-12004	92	58	.	.	PUNCT
fcis-12004	93	1	3.1	3.1	NUM
fcis-12004	93	2	.	.	PUNCT
fcis-12004	93	3	data	datum	NOUN
fcis-12004	93	4	processing	processing	NOUN
fcis-12004	93	5	when	when	SCONJ
fcis-12004	93	6	using	use	VERB
fcis-12004	93	7	a	a	DET
fcis-12004	93	8	video	video	NOUN
fcis-12004	93	9	processing	process	VERB
fcis-12004	93	10	library	library	NOUN
fcis-12004	93	11	to	to	PART
fcis-12004	93	12	process	process	VERB
fcis-12004	93	13	a	a	DET
fcis-12004	93	14	video	video	NOUN
fcis-12004	93	15	,	,	PUNCT
fcis-12004	93	16	it	it	PRON
fcis-12004	93	17	is	be	AUX
fcis-12004	93	18	important	important	ADJ
fcis-12004	93	19	to	to	PART
fcis-12004	93	20	set	set	VERB
fcis-12004	93	21	relevant	relevant	ADJ
fcis-12004	93	22	parameters	parameter	NOUN
fcis-12004	93	23	,	,	PUNCT
fcis-12004	93	24	such	such	ADJ
fcis-12004	93	25	as	as	ADP
fcis-12004	93	26	the	the	DET
fcis-12004	93	27	number	number	NOUN
fcis-12004	93	28	and	and	CCONJ
fcis-12004	93	29	frequency	frequency	NOUN
fcis-12004	93	30	of	of	ADP
fcis-12004	93	31	extracted	extract	VERB
fcis-12004	93	32	video	video	NOUN
fcis-12004	93	33	frame	frame	NOUN
fcis-12004	93	34	sequences	sequence	NOUN
fcis-12004	93	35	.	.	PUNCT
fcis-12004	94	1	among	among	ADP
fcis-12004	94	2	them	they	PRON
fcis-12004	94	3	,	,	PUNCT
fcis-12004	94	4	the	the	DET
fcis-12004	94	5	number	number	NOUN
fcis-12004	94	6	of	of	ADP
fcis-12004	94	7	frames	frame	NOUN
fcis-12004	94	8	should	should	AUX
fcis-12004	94	9	be	be	AUX
fcis-12004	94	10	sufficient	sufficient	ADJ
fcis-12004	94	11	to	to	PART
fcis-12004	94	12	capture	capture	VERB
fcis-12004	94	13	the	the	DET
fcis-12004	94	14	different	different	ADJ
fcis-12004	94	15	stages	stage	NOUN
fcis-12004	94	16	of	of	ADP
fcis-12004	94	17	the	the	DET
fcis-12004	94	18	action	action	NOUN
fcis-12004	94	19	,	,	PUNCT
fcis-12004	94	20	but	but	CCONJ
fcis-12004	94	21	not	not	PART
fcis-12004	94	22	too	too	ADV
fcis-12004	94	23	many	many	ADJ
fcis-12004	94	24	to	to	PART
fcis-12004	94	25	avoid	avoid	VERB
fcis-12004	94	26	excessive	excessive	ADJ
fcis-12004	94	27	computational	computational	ADJ
fcis-12004	94	28	overhead	overhead	NOUN
fcis-12004	94	29	.	.	PUNCT
fcis-12004	95	1	therefore	therefore	ADV
fcis-12004	95	2	,	,	PUNCT
fcis-12004	95	3	in	in	ADP
fcis-12004	95	4	this	this	DET
fcis-12004	95	5	paper	paper	NOUN
fcis-12004	95	6	,	,	PUNCT
fcis-12004	95	7	fifteen	fifteen	NUM
fcis-12004	95	8	is	be	AUX
fcis-12004	95	9	taken	take	VERB
fcis-12004	95	10	as	as	ADP
fcis-12004	95	11	the	the	DET
fcis-12004	95	12	number	number	NOUN
fcis-12004	95	13	of	of	ADP
fcis-12004	95	14	frames	frame	NOUN
fcis-12004	95	15	to	to	PART
fcis-12004	95	16	be	be	AUX
fcis-12004	95	17	extracted	extract	VERB
fcis-12004	95	18	,	,	PUNCT
fcis-12004	95	19	and	and	CCONJ
fcis-12004	95	20	also	also	ADV
fcis-12004	95	21	to	to	PART
fcis-12004	95	22	ensure	ensure	VERB
fcis-12004	95	23	the	the	DET
fcis-12004	95	24	accuracy	accuracy	NOUN
fcis-12004	95	25	of	of	ADP
fcis-12004	95	26	the	the	DET
fcis-12004	95	27	processing	processing	NOUN
fcis-12004	95	28	,	,	PUNCT
fcis-12004	95	29	this	this	DET
fcis-12004	95	30	paper	paper	NOUN
fcis-12004	95	31	chooses	choose	VERB
fcis-12004	95	32	to	to	PART
fcis-12004	95	33	extract	extract	VERB
fcis-12004	95	34	frames	frame	NOUN
fcis-12004	95	35	uniformly	uniformly	ADV
fcis-12004	95	36	from	from	ADP
fcis-12004	95	37	the	the	DET
fcis-12004	95	38	beginning	beginning	NOUN
fcis-12004	95	39	,	,	PUNCT
fcis-12004	95	40	middle	middle	ADJ
fcis-12004	95	41	,	,	PUNCT
fcis-12004	95	42	and	and	CCONJ
fcis-12004	95	43	end	end	VERB
fcis-12004	95	44	parts	part	NOUN
fcis-12004	95	45	of	of	ADP
fcis-12004	95	46	the	the	DET
fcis-12004	95	47	video	video	NOUN
fcis-12004	95	48	in	in	ADP
fcis-12004	95	49	order	order	NOUN
fcis-12004	95	50	to	to	PART
fcis-12004	95	51	obtain	obtain	VERB
fcis-12004	95	52	more	more	ADV
fcis-12004	95	53	comprehensive	comprehensive	ADJ
fcis-12004	95	54	information	information	NOUN
fcis-12004	95	55	.	.	PUNCT
fcis-12004	96	1	secondly	secondly	ADV
fcis-12004	96	2	,	,	PUNCT
fcis-12004	96	3	the	the	DET
fcis-12004	96	4	frequency	frequency	NOUN
fcis-12004	96	5	of	of	ADP
fcis-12004	96	6	extracted	extract	VERB
fcis-12004	96	7	frames	frame	NOUN
fcis-12004	96	8	generally	generally	ADV
fcis-12004	96	9	depends	depend	VERB
fcis-12004	96	10	on	on	ADP
fcis-12004	96	11	the	the	DET
fcis-12004	96	12	frame	frame	NOUN
fcis-12004	96	13	rate	rate	NOUN
fcis-12004	96	14	of	of	ADP
fcis-12004	96	15	the	the	DET
fcis-12004	96	16	video	video	NOUN
fcis-12004	96	17	and	and	CCONJ
fcis-12004	96	18	the	the	DET
fcis-12004	96	19	time	time	NOUN
fcis-12004	96	20	-	-	PUNCT
fcis-12004	96	21	dynamic	dynamic	ADJ
fcis-12004	96	22	information	information	NOUN
fcis-12004	96	23	that	that	PRON
fcis-12004	96	24	wants	want	VERB
fcis-12004	96	25	to	to	PART
fcis-12004	96	26	be	be	AUX
fcis-12004	96	27	captured	capture	VERB
fcis-12004	96	28	.	.	PUNCT
fcis-12004	97	1	to	to	PART
fcis-12004	97	2	characterize	characterize	VERB
fcis-12004	97	3	the	the	DET
fcis-12004	97	4	data	datum	NOUN
fcis-12004	97	5	at	at	ADP
fcis-12004	97	6	the	the	DET
fcis-12004	97	7	time	time	NOUN
fcis-12004	97	8	of	of	ADP
fcis-12004	97	9	the	the	DET
fcis-12004	97	10	mudslide	mudslide	ADJ
fcis-12004	97	11	disaster	disaster	NOUN
fcis-12004	97	12	,	,	PUNCT
fcis-12004	97	13	this	this	DET
fcis-12004	97	14	paper	paper	NOUN
fcis-12004	97	15	decides	decide	VERB
fcis-12004	97	16	to	to	PART
fcis-12004	97	17	use	use	VERB
fcis-12004	97	18	the	the	DET
fcis-12004	97	19	key	key	ADJ
fcis-12004	97	20	frame	frame	NOUN
fcis-12004	97	21	extraction	extraction	NOUN
fcis-12004	97	22	method	method	NOUN
fcis-12004	97	23	.	.	PUNCT
fcis-12004	98	1	an	an	DET
fcis-12004	98	2	algorithm	algorithm	NOUN
fcis-12004	98	3	(	(	PUNCT
fcis-12004	98	4	optical	optical	ADJ
fcis-12004	98	5	flow	flow	NOUN
fcis-12004	98	6	method	method	NOUN
fcis-12004	98	7	is	be	AUX
fcis-12004	98	8	used	use	VERB
fcis-12004	98	9	in	in	ADP
fcis-12004	98	10	this	this	DET
fcis-12004	98	11	paper	paper	NOUN
fcis-12004	98	12	)	)	PUNCT
fcis-12004	98	13	is	be	AUX
fcis-12004	98	14	used	use	VERB
fcis-12004	98	15	to	to	PART
fcis-12004	98	16	detect	detect	VERB
fcis-12004	98	17	key	key	ADJ
fcis-12004	98	18	frames	frame	NOUN
fcis-12004	98	19	in	in	ADP
fcis-12004	98	20	the	the	DET
fcis-12004	98	21	video	video	NOUN
fcis-12004	98	22	to	to	PART
fcis-12004	98	23	capture	capture	VERB
fcis-12004	98	24	significant	significant	ADJ
fcis-12004	98	25	changes	change	NOUN
fcis-12004	98	26	.	.	PUNCT
fcis-12004	99	1	3.2	3.2	NUM
fcis-12004	99	2	.	.	PUNCT
fcis-12004	100	1	detection	detection	NOUN
fcis-12004	100	2	principle	principle	NOUN
fcis-12004	100	3	in	in	ADP
fcis-12004	100	4	this	this	DET
fcis-12004	100	5	paper	paper	NOUN
fcis-12004	100	6	,	,	PUNCT
fcis-12004	100	7	we	we	PRON
fcis-12004	100	8	will	will	AUX
fcis-12004	100	9	train	train	VERB
fcis-12004	100	10	the	the	DET
fcis-12004	100	11	model	model	NOUN
fcis-12004	100	12	under	under	ADP
fcis-12004	100	13	weak	weak	ADJ
fcis-12004	100	14	supervision	supervision	NOUN
fcis-12004	100	15	,	,	PUNCT
fcis-12004	100	16	i.e.	i.e.	X
fcis-12004	100	17	,	,	PUNCT
fcis-12004	100	18	in	in	ADP
fcis-12004	100	19	a	a	DET
fcis-12004	100	20	video	video	NOUN
fcis-12004	100	21	,	,	PUNCT
fcis-12004	100	22	we	we	PRON
fcis-12004	100	23	are	be	AUX
fcis-12004	100	24	only	only	ADV
fcis-12004	100	25	concerned	concerned	ADJ
fcis-12004	100	26	with	with	ADP
fcis-12004	100	27	the	the	DET
fcis-12004	100	28	presence	presence	NOUN
fcis-12004	100	29	or	or	CCONJ
fcis-12004	100	30	absence	absence	NOUN
fcis-12004	100	31	of	of	ADP
fcis-12004	100	32	anomalous	anomalous	ADJ
fcis-12004	100	33	events	event	NOUN
fcis-12004	100	34	,	,	PUNCT
fcis-12004	100	35	without	without	ADP
fcis-12004	100	36	caring	care	VERB
fcis-12004	100	37	about	about	ADP
fcis-12004	100	38	the	the	DET
fcis-12004	100	39	specific	specific	ADJ
fcis-12004	100	40	type	type	NOUN
fcis-12004	100	41	of	of	ADP
fcis-12004	100	42	anomaly	anomaly	NOUN
fcis-12004	100	43	and	and	CCONJ
fcis-12004	100	44	the	the	DET
fcis-12004	100	45	frames	frame	NOUN
fcis-12004	100	46	in	in	ADP
fcis-12004	100	47	which	which	PRON
fcis-12004	100	48	the	the	DET
fcis-12004	100	49	anomaly	anomaly	NOUN
fcis-12004	100	50	occurs	occur	VERB
fcis-12004	100	51	.	.	PUNCT
fcis-12004	101	1	also	also	ADV
fcis-12004	101	2	,	,	PUNCT
fcis-12004	101	3	because	because	SCONJ
fcis-12004	101	4	the	the	DET
fcis-12004	101	5	video	video	NOUN
fcis-12004	101	6	captured	capture	VERB
fcis-12004	101	7	by	by	ADP
fcis-12004	101	8	the	the	DET
fcis-12004	101	9	camera	camera	NOUN
fcis-12004	101	10	has	have	VERB
fcis-12004	101	11	a	a	DET
fcis-12004	101	12	certain	certain	ADJ
fcis-12004	101	13	degree	degree	NOUN
fcis-12004	101	14	of	of	ADP
fcis-12004	101	15	noise	noise	NOUN
fcis-12004	101	16	as	as	ADV
fcis-12004	101	17	well	well	ADV
fcis-12004	101	18	as	as	ADP
fcis-12004	101	19	light	light	NOUN
fcis-12004	101	20	and	and	CCONJ
fcis-12004	101	21	shadow	shadow	NOUN
fcis-12004	101	22	variations	variation	NOUN
fcis-12004	101	23	that	that	PRON
fcis-12004	101	24	can	can	AUX
fcis-12004	101	25	cause	cause	VERB
fcis-12004	101	26	a	a	DET
fcis-12004	101	27	certain	certain	ADJ
fcis-12004	101	28	amount	amount	NOUN
fcis-12004	101	29	of	of	ADP
fcis-12004	101	30	error	error	NOUN
fcis-12004	101	31	in	in	ADP
fcis-12004	101	32	the	the	DET
fcis-12004	101	33	results	result	NOUN
fcis-12004	101	34	of	of	ADP
fcis-12004	101	35	the	the	DET
fcis-12004	101	36	data	datum	NOUN
fcis-12004	101	37	,	,	PUNCT
fcis-12004	101	38	this	this	DET
fcis-12004	101	39	paper	paper	NOUN
fcis-12004	101	40	uses	use	VERB
fcis-12004	101	41	the	the	DET
fcis-12004	101	42	i3d	i3d	NOUN
fcis-12004	101	43	training	training	NOUN
fcis-12004	101	44	model	model	NOUN
fcis-12004	101	45	to	to	PART
fcis-12004	101	46	extract	extract	VERB
fcis-12004	101	47	video	video	NOUN
fcis-12004	101	48	features	feature	NOUN
fcis-12004	101	49	and	and	CCONJ
fcis-12004	101	50	the	the	DET
fcis-12004	101	51	extracted	extract	VERB
fcis-12004	101	52	features	feature	NOUN
fcis-12004	101	53	are	be	AUX
fcis-12004	101	54	used	use	VERB
fcis-12004	101	55	to	to	PART
fcis-12004	101	56	calculate	calculate	VERB
fcis-12004	101	57	the	the	DET
fcis-12004	101	58	anomaly	anomaly	NOUN
fcis-12004	101	59	score	score	NOUN
fcis-12004	101	60	using	use	VERB
fcis-12004	101	61	the	the	DET
fcis-12004	101	62	sigmoid	sigmoid	NOUN
fcis-12004	101	63	activation	activation	NOUN
fcis-12004	101	64	function	function	NOUN
fcis-12004	101	65	,	,	PUNCT
fcis-12004	101	66	and	and	CCONJ
fcis-12004	101	67	based	base	VERB
fcis-12004	101	68	on	on	ADP
fcis-12004	101	69	the	the	DET
fcis-12004	101	70	anomaly	anomaly	NOUN
fcis-12004	101	71	score	score	NOUN
fcis-12004	101	72	,	,	PUNCT
fcis-12004	101	73	it	it	PRON
fcis-12004	101	74	is	be	AUX
fcis-12004	101	75	predicted	predict	VERB
fcis-12004	101	76	whether	whether	SCONJ
fcis-12004	101	77	or	or	CCONJ
fcis-12004	101	78	not	not	PART
fcis-12004	101	79	an	an	DET
fcis-12004	101	80	anomalous	anomalous	ADJ
fcis-12004	101	81	event	event	NOUN
fcis-12004	101	82	occurs	occur	VERB
fcis-12004	101	83	.	.	PUNCT
fcis-12004	102	1	3.3	3.3	NUM
fcis-12004	102	2	.	.	PUNCT
fcis-12004	102	3	model	model	NOUN
fcis-12004	102	4	building	build	VERB
fcis-12004	102	5	each	each	DET
fcis-12004	102	6	training	training	NOUN
fcis-12004	102	7	video	video	NOUN
fcis-12004	102	8	is	be	AUX
fcis-12004	102	9	divided	divide	VERB
fcis-12004	102	10	into	into	ADP
fcis-12004	102	11	the	the	DET
fcis-12004	102	12	same	same	ADJ
fcis-12004	102	13	number	number	NOUN
fcis-12004	102	14	of	of	ADP
fcis-12004	102	15	clips	clip	NOUN
fcis-12004	102	16	to	to	PART
fcis-12004	102	17	form	form	VERB
fcis-12004	102	18	positive	positive	ADJ
fcis-12004	102	19	and	and	CCONJ
fcis-12004	102	20	negative	negative	ADJ
fcis-12004	102	21	example	example	NOUN
fcis-12004	102	22	packages	package	NOUN
fcis-12004	102	23	for	for	ADP
fcis-12004	102	24	training	training	NOUN
fcis-12004	102	25	.	.	PUNCT
fcis-12004	103	1	then	then	ADV
fcis-12004	103	2	pick	pick	VERB
fcis-12004	103	3	a	a	DET
fcis-12004	103	4	best	well	ADV
fcis-12004	103	5	-	-	PUNCT
fcis-12004	103	6	scoring	score	VERB
fcis-12004	103	7	clip	clip	NOUN
fcis-12004	103	8	from	from	ADP
fcis-12004	103	9	the	the	DET
fcis-12004	103	10	positive	positive	ADJ
fcis-12004	103	11	example	example	NOUN
fcis-12004	103	12	packet	packet	NOUN
fcis-12004	103	13	for	for	ADP
fcis-12004	103	14	training	train	VERB
fcis-12004	103	15	the	the	DET
fcis-12004	103	16	parameters	parameter	NOUN
fcis-12004	103	17	of	of	ADP
fcis-12004	103	18	i3d	i3d	PROPN
fcis-12004	103	19	,	,	PUNCT
fcis-12004	103	20	and	and	CCONJ
fcis-12004	103	21	similarly	similarly	ADV
fcis-12004	103	22	,	,	PUNCT
fcis-12004	103	23	choose	choose	VERB
fcis-12004	103	24	a	a	DET
fcis-12004	103	25	best	well	ADV
fcis-12004	103	26	-	-	PUNCT
fcis-12004	103	27	scoring	score	VERB
fcis-12004	103	28	clip	clip	NOUN
fcis-12004	103	29	from	from	ADP
fcis-12004	103	30	the	the	DET
fcis-12004	103	31	negative	negative	ADJ
fcis-12004	103	32	example	example	NOUN
fcis-12004	103	33	packet	packet	NOUN
fcis-12004	103	34	for	for	ADP
fcis-12004	103	35	training	train	VERB
fcis-12004	103	36	i3d.in	i3d.in	PROPN
fcis-12004	103	37	this	this	DET
fcis-12004	103	38	paper	paper	NOUN
fcis-12004	103	39	,	,	PUNCT
fcis-12004	103	40	we	we	PRON
fcis-12004	103	41	use	use	VERB
fcis-12004	103	42	the	the	DET
fcis-12004	103	43	cross	cross	ADJ
fcis-12004	103	44	-	-	ADJ
fcis-12004	103	45	entropy	entropy	ADJ
fcis-12004	103	46	loss	loss	NOUN
fcis-12004	103	47	function	function	NOUN
fcis-12004	103	48	.	.	PUNCT
fcis-12004	104	1	106	106	NUM
fcis-12004	104	2	)	)	PUNCT
fcis-12004	104	3	)	)	PUNCT
fcis-12004	105	1	1log()1()log((c	1log()1()log((c	NUM
fcis-12004	105	2			ADJ
fcis-12004	105	3			PROPN
fcis-12004	105	4	yyyy	yyyy	NOUN
fcis-12004	105	5	the	the	DET
fcis-12004	105	6	cross	cross	ADJ
fcis-12004	105	7	-	-	ADJ
fcis-12004	105	8	entropy	entropy	ADJ
fcis-12004	105	9	loss	loss	NOUN
fcis-12004	105	10	function	function	NOUN
fcis-12004	105	11	serves	serve	VERB
fcis-12004	105	12	to	to	PART
fcis-12004	105	13	measure	measure	VERB
fcis-12004	105	14	the	the	DET
fcis-12004	105	15	difference	difference	NOUN
fcis-12004	105	16	between	between	ADP
fcis-12004	105	17	the	the	DET
fcis-12004	105	18	model	model	NOUN
fcis-12004	105	19	's	's	PART
fcis-12004	105	20	output	output	NOUN
fcis-12004	105	21	probability	probability	NOUN
fcis-12004	105	22	distribution	distribution	NOUN
fcis-12004	105	23	and	and	CCONJ
fcis-12004	105	24	the	the	DET
fcis-12004	105	25	actual	actual	ADJ
fcis-12004	105	26	target	target	NOUN
fcis-12004	105	27	distribution	distribution	NOUN
fcis-12004	105	28	[	[	X
fcis-12004	105	29	9	9	NUM
fcis-12004	105	30	]	]	PUNCT
fcis-12004	105	31	.	.	PUNCT
fcis-12004	106	1	the	the	PRON
fcis-12004	106	2	smaller	small	ADJ
fcis-12004	106	3	this	this	DET
fcis-12004	106	4	difference	difference	NOUN
fcis-12004	106	5	is	be	AUX
fcis-12004	106	6	,	,	PUNCT
fcis-12004	106	7	the	the	PRON
fcis-12004	106	8	lower	low	ADJ
fcis-12004	106	9	the	the	DET
fcis-12004	106	10	loss	loss	NOUN
fcis-12004	106	11	value	value	NOUN
fcis-12004	106	12	is	be	AUX
fcis-12004	106	13	,	,	PUNCT
fcis-12004	106	14	indicating	indicate	VERB
fcis-12004	106	15	the	the	DET
fcis-12004	106	16	more	more	ADV
fcis-12004	106	17	accurate	accurate	ADJ
fcis-12004	106	18	the	the	DET
fcis-12004	106	19	model	model	NOUN
fcis-12004	106	20	's	's	PART
fcis-12004	106	21	prediction	prediction	NOUN
fcis-12004	106	22	is	be	AUX
fcis-12004	106	23	.	.	PUNCT
fcis-12004	107	1	at	at	ADP
fcis-12004	107	2	the	the	DET
fcis-12004	107	3	same	same	ADJ
fcis-12004	107	4	time	time	NOUN
fcis-12004	107	5	,	,	PUNCT
fcis-12004	107	6	the	the	DET
fcis-12004	107	7	crossentropy	crossentropy	NOUN
fcis-12004	107	8	loss	loss	NOUN
fcis-12004	107	9	function	function	NOUN
fcis-12004	107	10	is	be	AUX
fcis-12004	107	11	relatively	relatively	ADV
fcis-12004	107	12	simple	simple	ADJ
fcis-12004	107	13	for	for	ADP
fcis-12004	107	14	calculating	calculate	VERB
fcis-12004	107	15	the	the	DET
fcis-12004	107	16	gradient	gradient	NOUN
fcis-12004	107	17	of	of	ADP
fcis-12004	107	18	the	the	DET
fcis-12004	107	19	model	model	NOUN
fcis-12004	107	20	parameters	parameter	NOUN
fcis-12004	107	21	,	,	PUNCT
fcis-12004	107	22	which	which	PRON
fcis-12004	107	23	allows	allow	VERB
fcis-12004	107	24	optimization	optimization	NOUN
fcis-12004	107	25	algorithms	algorithm	NOUN
fcis-12004	107	26	such	such	ADJ
fcis-12004	107	27	as	as	ADP
fcis-12004	107	28	gradient	gradient	ADJ
fcis-12004	107	29	descent	descent	NOUN
fcis-12004	107	30	to	to	PART
fcis-12004	107	31	effectively	effectively	ADV
fcis-12004	107	32	update	update	VERB
fcis-12004	107	33	the	the	DET
fcis-12004	107	34	parameters	parameter	NOUN
fcis-12004	107	35	to	to	PART
fcis-12004	107	36	reduce	reduce	VERB
fcis-12004	107	37	the	the	DET
fcis-12004	107	38	loss	loss	NOUN
fcis-12004	107	39	.	.	PUNCT
fcis-12004	108	1	it	it	PRON
fcis-12004	108	2	helps	help	VERB
fcis-12004	108	3	the	the	DET
fcis-12004	108	4	model	model	NOUN
fcis-12004	108	5	to	to	PART
fcis-12004	108	6	learn	learn	VERB
fcis-12004	108	7	the	the	DET
fcis-12004	108	8	correct	correct	ADJ
fcis-12004	108	9	category	category	NOUN
fcis-12004	108	10	distribution	distribution	NOUN
fcis-12004	108	11	and	and	CCONJ
fcis-12004	108	12	improve	improve	VERB
fcis-12004	108	13	the	the	DET
fcis-12004	108	14	classification	classification	NOUN
fcis-12004	108	15	accuracy	accuracy	NOUN
fcis-12004	108	16	.	.	PUNCT
fcis-12004	109	1	3.4	3.4	NUM
fcis-12004	109	2	.	.	PUNCT
fcis-12004	109	3	model	model	NOUN
fcis-12004	109	4	training	train	VERB
fcis-12004	109	5	the	the	DET
fcis-12004	109	6	videos	video	NOUN
fcis-12004	109	7	in	in	ADP
fcis-12004	109	8	the	the	DET
fcis-12004	109	9	dataset	dataset	NOUN
fcis-12004	109	10	are	be	AUX
fcis-12004	109	11	divided	divide	VERB
fcis-12004	109	12	into	into	ADP
fcis-12004	109	13	two	two	NUM
fcis-12004	109	14	parts	part	NOUN
fcis-12004	109	15	according	accord	VERB
fcis-12004	109	16	to	to	ADP
fcis-12004	109	17	3:2	3:2	NUM
fcis-12004	109	18	for	for	ADP
fcis-12004	109	19	training	training	NOUN
fcis-12004	109	20	set	set	NOUN
fcis-12004	109	21	and	and	CCONJ
fcis-12004	109	22	test	test	NOUN
fcis-12004	109	23	set	set	VERB
fcis-12004	109	24	.	.	PUNCT
fcis-12004	110	1	after	after	ADP
fcis-12004	110	2	that	that	SCONJ
fcis-12004	110	3	the	the	DET
fcis-12004	110	4	videos	video	NOUN
fcis-12004	110	5	are	be	AUX
fcis-12004	110	6	divided	divide	VERB
fcis-12004	110	7	equally	equally	ADV
fcis-12004	110	8	without	without	ADP
fcis-12004	110	9	repetition	repetition	NOUN
fcis-12004	110	10	into	into	ADP
fcis-12004	110	11	several	several	ADJ
fcis-12004	110	12	short	short	ADJ
fcis-12004	110	13	videos	video	NOUN
fcis-12004	110	14	,	,	PUNCT
fcis-12004	110	15	each	each	PRON
fcis-12004	110	16	containing	contain	VERB
fcis-12004	110	17	15	15	NUM
fcis-12004	110	18	frames	frame	NOUN
fcis-12004	110	19	of	of	ADP
fcis-12004	110	20	images	image	NOUN
fcis-12004	110	21	,	,	PUNCT
fcis-12004	110	22	and	and	CCONJ
fcis-12004	110	23	such	such	ADJ
fcis-12004	110	24	short	short	ADJ
fcis-12004	110	25	videos	video	NOUN
fcis-12004	110	26	are	be	AUX
fcis-12004	110	27	used	use	VERB
fcis-12004	110	28	as	as	ADP
fcis-12004	110	29	samples	sample	NOUN
fcis-12004	110	30	for	for	ADP
fcis-12004	110	31	training	training	NOUN
fcis-12004	110	32	.	.	PUNCT
fcis-12004	111	1	the	the	DET
fcis-12004	111	2	visual	visual	ADJ
fcis-12004	111	3	features	feature	NOUN
fcis-12004	111	4	are	be	AUX
fcis-12004	111	5	extracted	extract	VERB
fcis-12004	111	6	using	use	VERB
fcis-12004	111	7	the	the	DET
fcis-12004	111	8	fully	fully	ADV
fcis-12004	111	9	connected	connected	ADJ
fcis-12004	111	10	layer	layer	NOUN
fcis-12004	111	11	of	of	ADP
fcis-12004	111	12	the	the	DET
fcis-12004	111	13	i3d	i3d	PROPN
fcis-12004	111	14	network	network	NOUN
fcis-12004	111	15	.	.	PUNCT
fcis-12004	112	1	before	before	ADP
fcis-12004	112	2	computing	compute	VERB
fcis-12004	112	3	the	the	DET
fcis-12004	112	4	features	feature	NOUN
fcis-12004	112	5	,	,	PUNCT
fcis-12004	112	6	each	each	DET
fcis-12004	112	7	video	video	NOUN
fcis-12004	112	8	frame	frame	NOUN
fcis-12004	112	9	was	be	AUX
fcis-12004	112	10	resized	resize	VERB
fcis-12004	112	11	to	to	ADP
fcis-12004	112	12	210	210	NUM
fcis-12004	112	13	×	×	NOUN
fcis-12004	112	14	100	100	NUM
fcis-12004	112	15	pixels	pixel	NOUN
fcis-12004	112	16	and	and	CCONJ
fcis-12004	112	17	the	the	DET
fcis-12004	112	18	frame	frame	NOUN
fcis-12004	112	19	rate	rate	NOUN
fcis-12004	112	20	was	be	AUX
fcis-12004	112	21	fixed	fix	VERB
fcis-12004	112	22	to	to	ADP
fcis-12004	112	23	30	30	NUM
fcis-12004	112	24	fps	fps	PROPN
fcis-12004	112	25	.	.	PUNCT
fcis-12004	113	1	the	the	DET
fcis-12004	113	2	i3d	i3d	PROPN
fcis-12004	113	3	features	feature	VERB
fcis-12004	113	4	for	for	ADP
fcis-12004	113	5	each	each	DET
fcis-12004	113	6	15	15	NUM
fcis-12004	113	7	-	-	PUNCT
fcis-12004	113	8	frame	frame	NOUN
fcis-12004	113	9	video	video	NOUN
fcis-12004	113	10	clip	clip	NOUN
fcis-12004	113	11	were	be	AUX
fcis-12004	113	12	computed	compute	VERB
fcis-12004	113	13	and	and	CCONJ
fcis-12004	113	14	then	then	ADV
fcis-12004	113	15	normalized	normalize	VERB
fcis-12004	113	16	.	.	PUNCT
fcis-12004	114	1	in	in	ADP
fcis-12004	114	2	order	order	NOUN
fcis-12004	114	3	to	to	PART
fcis-12004	114	4	obtain	obtain	VERB
fcis-12004	114	5	the	the	DET
fcis-12004	114	6	features	feature	NOUN
fcis-12004	114	7	of	of	ADP
fcis-12004	114	8	a	a	DET
fcis-12004	114	9	video	video	NOUN
fcis-12004	114	10	clip	clip	NOUN
fcis-12004	114	11	,	,	PUNCT
fcis-12004	114	12	the	the	DET
fcis-12004	114	13	average	average	NOUN
fcis-12004	114	14	of	of	ADP
fcis-12004	114	15	the	the	DET
fcis-12004	114	16	features	feature	NOUN
fcis-12004	114	17	of	of	ADP
fcis-12004	114	18	all	all	DET
fcis-12004	114	19	15frame	15frame	NUM
fcis-12004	114	20	clips	clip	NOUN
fcis-12004	114	21	within	within	ADP
fcis-12004	114	22	that	that	DET
fcis-12004	114	23	clip	clip	NOUN
fcis-12004	114	24	was	be	AUX
fcis-12004	114	25	taken	take	VERB
fcis-12004	114	26	.	.	PUNCT
fcis-12004	115	1	these	these	DET
fcis-12004	115	2	features	feature	NOUN
fcis-12004	115	3	are	be	AUX
fcis-12004	115	4	later	later	ADV
fcis-12004	115	5	fed	feed	VERB
fcis-12004	115	6	into	into	ADP
fcis-12004	115	7	the	the	DET
fcis-12004	115	8	subsequent	subsequent	ADJ
fcis-12004	115	9	i3d	i3d	CCONJ
fcis-12004	115	10	neural	neural	ADJ
fcis-12004	115	11	network	network	NOUN
fcis-12004	115	12	.	.	PUNCT
fcis-12004	116	1	3.5	3.5	NUM
fcis-12004	116	2	.	.	PUNCT
fcis-12004	116	3	results	result	VERB
fcis-12004	116	4	the	the	DET
fcis-12004	116	5	dataset	dataset	NOUN
fcis-12004	116	6	as	as	SCONJ
fcis-12004	116	7	both	both	CCONJ
fcis-12004	116	8	normal	normal	ADJ
fcis-12004	116	9	and	and	CCONJ
fcis-12004	116	10	abnormal	abnormal	ADJ
fcis-12004	116	11	videos	video	NOUN
fcis-12004	116	12	are	be	AUX
fcis-12004	116	13	labeled	label	VERB
fcis-12004	116	14	and	and	CCONJ
fcis-12004	116	15	sent	send	VERB
fcis-12004	116	16	to	to	ADP
fcis-12004	116	17	i3d	i3d	PROPN
fcis-12004	116	18	for	for	ADP
fcis-12004	116	19	training	training	NOUN
fcis-12004	116	20	gives	give	VERB
fcis-12004	116	21	very	very	ADV
fcis-12004	116	22	clear	clear	ADJ
fcis-12004	116	23	results	result	NOUN
fcis-12004	116	24	as	as	SCONJ
fcis-12004	116	25	shown	show	VERB
fcis-12004	116	26	in	in	ADP
fcis-12004	116	27	table	table	NOUN
fcis-12004	116	28	1	1	NUM
fcis-12004	116	29	.	.	PUNCT
fcis-12004	117	1	for	for	ADP
fcis-12004	117	2	video	video	NOUN
fcis-12004	117	3	recognition	recognition	NOUN
fcis-12004	117	4	,	,	PUNCT
fcis-12004	117	5	10	10	NUM
fcis-12004	117	6	videos	video	NOUN
fcis-12004	117	7	were	be	AUX
fcis-12004	117	8	used	use	VERB
fcis-12004	117	9	from	from	ADP
fcis-12004	117	10	each	each	DET
fcis-12004	117	11	event	event	NOUN
fcis-12004	117	12	and	and	CCONJ
fcis-12004	117	13	they	they	PRON
fcis-12004	117	14	were	be	AUX
fcis-12004	117	15	divided	divide	VERB
fcis-12004	117	16	into	into	ADP
fcis-12004	117	17	3:2	3:2	NUM
fcis-12004	117	18	ratio	ratio	NOUN
fcis-12004	117	19	for	for	ADP
fcis-12004	117	20	training	training	NOUN
fcis-12004	117	21	and	and	CCONJ
fcis-12004	117	22	testing	testing	NOUN
fcis-12004	117	23	.	.	PUNCT
fcis-12004	118	1	there	there	PRON
fcis-12004	118	2	is	be	VERB
fcis-12004	118	3	also	also	ADV
fcis-12004	118	4	another	another	DET
fcis-12004	118	5	portion	portion	NOUN
fcis-12004	118	6	of	of	ADP
fcis-12004	118	7	the	the	DET
fcis-12004	118	8	dataset	dataset	NOUN
fcis-12004	118	9	trained	train	VERB
fcis-12004	118	10	using	use	VERB
fcis-12004	118	11	other	other	ADJ
fcis-12004	118	12	models	model	NOUN
fcis-12004	118	13	and	and	CCONJ
fcis-12004	118	14	a	a	DET
fcis-12004	118	15	data	datum	NOUN
fcis-12004	118	16	comparison	comparison	NOUN
fcis-12004	118	17	is	be	AUX
fcis-12004	118	18	done	do	VERB
fcis-12004	118	19	and	and	CCONJ
fcis-12004	118	20	the	the	DET
fcis-12004	118	21	results	result	NOUN
fcis-12004	118	22	are	be	AUX
fcis-12004	118	23	found	find	VERB
fcis-12004	118	24	to	to	PART
fcis-12004	118	25	be	be	AUX
fcis-12004	118	26	very	very	ADV
fcis-12004	118	27	different	different	ADJ
fcis-12004	118	28	.	.	PUNCT
fcis-12004	119	1	this	this	PRON
fcis-12004	119	2	is	be	AUX
fcis-12004	119	3	due	due	ADJ
fcis-12004	119	4	to	to	ADP
fcis-12004	119	5	the	the	DET
fcis-12004	119	6	fact	fact	NOUN
fcis-12004	119	7	that	that	SCONJ
fcis-12004	119	8	these	these	PRON
fcis-12004	119	9	are	be	AUX
fcis-12004	119	10	long	long	ADV
fcis-12004	119	11	untrimmed	untrimmed	ADJ
fcis-12004	119	12	videos	video	NOUN
fcis-12004	119	13	with	with	ADP
fcis-12004	119	14	low	low	ADJ
fcis-12004	119	15	resolution	resolution	NOUN
fcis-12004	119	16	and	and	CCONJ
fcis-12004	119	17	the	the	DET
fcis-12004	119	18	anomalies	anomaly	NOUN
fcis-12004	119	19	appear	appear	VERB
fcis-12004	119	20	very	very	ADV
fcis-12004	119	21	randomly	randomly	ADV
fcis-12004	119	22	.	.	PUNCT
fcis-12004	120	1	in	in	ADP
fcis-12004	120	2	addition	addition	NOUN
fcis-12004	120	3	,	,	PUNCT
fcis-12004	120	4	there	there	PRON
fcis-12004	120	5	are	be	VERB
fcis-12004	120	6	large	large	ADJ
fcis-12004	120	7	variations	variation	NOUN
fcis-12004	120	8	due	due	ADJ
fcis-12004	120	9	to	to	ADP
fcis-12004	120	10	changes	change	NOUN
fcis-12004	120	11	in	in	ADP
fcis-12004	120	12	camera	camera	NOUN
fcis-12004	120	13	viewpoint	viewpoint	NOUN
fcis-12004	120	14	and	and	CCONJ
fcis-12004	120	15	lighting	light	VERB
fcis-12004	120	16	as	as	ADV
fcis-12004	120	17	well	well	ADV
fcis-12004	120	18	as	as	ADP
fcis-12004	120	19	background	background	NOUN
fcis-12004	120	20	noise	noise	NOUN
fcis-12004	120	21	[	[	X
fcis-12004	120	22	10	10	NUM
fcis-12004	120	23	]	]	PUNCT
fcis-12004	120	24	.	.	PUNCT
fcis-12004	121	1	therefore	therefore	ADV
fcis-12004	121	2	,	,	PUNCT
fcis-12004	121	3	preprocessing	preprocesse	VERB
fcis-12004	121	4	the	the	DET
fcis-12004	121	5	data	datum	NOUN
fcis-12004	121	6	would	would	AUX
fcis-12004	121	7	have	have	AUX
fcis-12004	121	8	made	make	VERB
fcis-12004	121	9	the	the	DET
fcis-12004	121	10	experimental	experimental	ADJ
fcis-12004	121	11	results	result	NOUN
fcis-12004	121	12	more	more	ADV
fcis-12004	121	13	in	in	ADP
fcis-12004	121	14	line	line	NOUN
fcis-12004	121	15	with	with	ADP
fcis-12004	121	16	expectations	expectation	NOUN
fcis-12004	121	17	.	.	PUNCT
fcis-12004	122	1	table	table	NOUN
fcis-12004	122	2	1	1	NUM
fcis-12004	122	3	.	.	PUNCT
fcis-12004	123	1	comparative	comparative	ADJ
fcis-12004	123	2	results	result	NOUN
fcis-12004	123	3	table	table	NOUN
fcis-12004	123	4	method	method	NOUN
fcis-12004	123	5	methodology	methodology	NOUN
fcis-12004	123	6	of	of	ADP
fcis-12004	123	7	this	this	DET
fcis-12004	123	8	paper	paper	NOUN
fcis-12004	123	9	c3d+cnn	c3d+cnn	PRON
fcis-12004	123	10	c3d+mlp	c3d+mlp	PUNCT
fcis-12004	123	11	twostream	twostream	ADJ
fcis-12004	123	12	correct	correct	ADJ
fcis-12004	123	13	rate	rate	NOUN
fcis-12004	123	14	86	86	NUM
fcis-12004	123	15	83.0	83.0	NUM
fcis-12004	123	16	76.3	76.3	NUM
fcis-12004	123	17	64.0	64.0	NUM
fcis-12004	123	18	4	4	NUM
fcis-12004	123	19	.	.	PUNCT
fcis-12004	124	1	conclusion	conclusion	NOUN
fcis-12004	124	2	mudslide	mudslide	ADJ
fcis-12004	124	3	disasters	disaster	NOUN
fcis-12004	124	4	can	can	AUX
fcis-12004	124	5	bring	bring	VERB
fcis-12004	124	6	serious	serious	ADJ
fcis-12004	124	7	disaster	disaster	NOUN
fcis-12004	124	8	impacts	impact	NOUN
fcis-12004	124	9	.	.	PUNCT
fcis-12004	125	1	in	in	ADP
fcis-12004	125	2	this	this	DET
fcis-12004	125	3	paper	paper	NOUN
fcis-12004	125	4	,	,	PUNCT
fcis-12004	125	5	based	base	VERB
fcis-12004	125	6	on	on	ADP
fcis-12004	125	7	the	the	DET
fcis-12004	125	8	video	video	NOUN
fcis-12004	125	9	monitoring	monitor	VERB
fcis-12004	125	10	data	datum	NOUN
fcis-12004	125	11	,	,	PUNCT
fcis-12004	125	12	a	a	DET
fcis-12004	125	13	combination	combination	NOUN
fcis-12004	125	14	of	of	ADP
fcis-12004	125	15	anomaly	anomaly	NOUN
fcis-12004	125	16	detection	detection	NOUN
fcis-12004	125	17	and	and	CCONJ
fcis-12004	125	18	i3d	i3d	NOUN
fcis-12004	125	19	network	network	NOUN
fcis-12004	125	20	is	be	AUX
fcis-12004	125	21	used	use	VERB
fcis-12004	125	22	to	to	PART
fcis-12004	125	23	realize	realize	VERB
fcis-12004	125	24	the	the	DET
fcis-12004	125	25	detection	detection	NOUN
fcis-12004	125	26	of	of	ADP
fcis-12004	125	27	mudslide	mudslide	NOUN
fcis-12004	125	28	in	in	ADP
fcis-12004	125	29	the	the	DET
fcis-12004	125	30	video	video	NOUN
fcis-12004	125	31	.	.	PUNCT
fcis-12004	126	1	the	the	DET
fcis-12004	126	2	occurrence	occurrence	NOUN
fcis-12004	126	3	of	of	ADP
fcis-12004	126	4	mudslide	mudslide	NOUN
fcis-12004	126	5	can	can	AUX
fcis-12004	126	6	be	be	AUX
fcis-12004	126	7	detected	detect	VERB
fcis-12004	126	8	with	with	ADP
fcis-12004	126	9	high	high	ADJ
fcis-12004	126	10	accuracy	accuracy	NOUN
fcis-12004	126	11	,	,	PUNCT
fcis-12004	126	12	thus	thus	ADV
fcis-12004	126	13	realizing	realize	VERB
fcis-12004	126	14	the	the	DET
fcis-12004	126	15	disaster	disaster	NOUN
fcis-12004	126	16	warning	warning	NOUN
fcis-12004	126	17	of	of	ADP
fcis-12004	126	18	mudslide	mudslide	NOUN
fcis-12004	126	19	and	and	CCONJ
fcis-12004	126	20	diffuse	diffuse	VERB
fcis-12004	126	21	flow	flow	NOUN
fcis-12004	126	22	area	area	NOUN
fcis-12004	126	23	range	range	NOUN
fcis-12004	126	24	,	,	PUNCT
fcis-12004	126	25	providing	provide	VERB
fcis-12004	126	26	a	a	DET
fcis-12004	126	27	new	new	ADJ
fcis-12004	126	28	idea	idea	NOUN
fcis-12004	126	29	for	for	ADP
fcis-12004	126	30	mudslide	mudslide	ADJ
fcis-12004	126	31	disaster	disaster	NOUN
fcis-12004	126	32	monitoring	monitoring	NOUN
fcis-12004	126	33	and	and	CCONJ
fcis-12004	126	34	warning	warning	NOUN
fcis-12004	126	35	,	,	PUNCT
fcis-12004	126	36	and	and	CCONJ
fcis-12004	126	37	enriching	enrich	VERB
fcis-12004	126	38	the	the	DET
fcis-12004	126	39	mudslide	mudslide	ADJ
fcis-12004	126	40	disaster	disaster	NOUN
fcis-12004	126	41	prevention	prevention	NOUN
fcis-12004	126	42	and	and	CCONJ
fcis-12004	126	43	mitigation	mitigation	NOUN
fcis-12004	126	44	system	system	NOUN
fcis-12004	126	45	.	.	PUNCT
fcis-12004	127	1	references	reference	NOUN
fcis-12004	127	2	[	[	X
fcis-12004	127	3	1	1	NUM
fcis-12004	127	4	]	]	PUNCT
fcis-12004	127	5	lohumi	lohumi	NOUN
fcis-12004	127	6	k	k	PROPN
fcis-12004	127	7	,	,	PUNCT
fcis-12004	127	8	roy	roy	PROPN
fcis-12004	127	9	s.	s.	PROPN
fcis-12004	127	10	automatic	automatic	ADJ
fcis-12004	127	11	detection	detection	NOUN
fcis-12004	127	12	of	of	ADP
fcis-12004	127	13	flood	flood	NOUN
fcis-12004	127	14	severity	severity	NOUN
fcis-12004	127	15	level	level	NOUN
fcis-12004	127	16	from	from	ADP
fcis-12004	127	17	flood	flood	NOUN
fcis-12004	127	18	videos	video	NOUN
fcis-12004	127	19	using	use	VERB
fcis-12004	127	20	deep	deep	ADJ
fcis-12004	127	21	learning	learn	VERB
fcis-12004	127	22	models[c]//2018	models[c]//2018	PROPN
fcis-12004	127	23	5th	5th	ADJ
fcis-12004	127	24	international	international	ADJ
fcis-12004	127	25	conference	conference	NOUN
fcis-12004	127	26	on	on	ADP
fcis-12004	127	27	information	information	NOUN
fcis-12004	127	28	and	and	CCONJ
fcis-12004	127	29	communication	communication	NOUN
fcis-12004	127	30	technologies	technology	NOUN
fcis-12004	127	31	for	for	ADP
fcis-12004	127	32	disaster	disaster	NOUN
fcis-12004	127	33	management	management	NOUN
fcis-12004	127	34	(	(	PUNCT
fcis-12004	127	35	ict	ict	PROPN
fcis-12004	127	36	-	-	PUNCT
fcis-12004	127	37	dm	dm	PROPN
fcis-12004	127	38	)	)	PUNCT
fcis-12004	127	39	.	.	PUNCT
fcis-12004	128	1	ieee	ieee	NOUN
fcis-12004	128	2	,	,	PUNCT
fcis-12004	128	3	2018	2018	NUM
fcis-12004	128	4	:	:	PUNCT
fcis-12004	128	5	1	1	NUM
fcis-12004	128	6	-	-	SYM
fcis-12004	128	7	7	7	NUM
fcis-12004	128	8	.	.	PUNCT
fcis-12004	129	1	[	[	X
fcis-12004	129	2	2	2	NUM
fcis-12004	129	3	]	]	X
fcis-12004	129	4	lopez	lopez	NOUN
fcis-12004	129	5	-	-	PUNCT
fcis-12004	129	6	fuentes	fuentes	PROPN
fcis-12004	129	7	l	l	PROPN
fcis-12004	129	8	,	,	PUNCT
fcis-12004	129	9	van	van	PROPN
fcis-12004	129	10	de	de	PROPN
fcis-12004	129	11	weijer	weijer	PROPN
fcis-12004	129	12	j	j	PROPN
fcis-12004	129	13	,	,	PUNCT
fcis-12004	129	14	bolanos	bolanos	VERB
fcis-12004	129	15	m	m	PROPN
fcis-12004	129	16	,	,	PUNCT
fcis-12004	129	17	et	et	PROPN
fcis-12004	129	18	al	al	PROPN
fcis-12004	129	19	.	.	PUNCT
fcis-12004	130	1	multimodal	multimodal	ADJ
fcis-12004	130	2	deep	deep	ADJ
fcis-12004	130	3	learning	learning	NOUN
fcis-12004	130	4	approach	approach	NOUN
fcis-12004	130	5	for	for	ADP
fcis-12004	130	6	flood	flood	NOUN
fcis-12004	130	7	detection[j	detection[j	PROPN
fcis-12004	130	8	]	]	PUNCT
fcis-12004	130	9	.	.	PUNCT
fcis-12004	131	1	mediaeval	mediaeval	ADJ
fcis-12004	131	2	,	,	PUNCT
fcis-12004	131	3	2017	2017	NUM
fcis-12004	131	4	,	,	PUNCT
fcis-12004	131	5	17	17	NUM
fcis-12004	131	6	:	:	SYM
fcis-12004	131	7	13	13	NUM
fcis-12004	131	8	-	-	SYM
fcis-12004	131	9	15	15	NUM
fcis-12004	131	10	.	.	PUNCT
fcis-12004	132	1	[	[	X
fcis-12004	132	2	3	3	X
fcis-12004	132	3	]	]	PUNCT
fcis-12004	132	4	pang	pang	NOUN
fcis-12004	132	5	g	g	PROPN
fcis-12004	132	6	,	,	PUNCT
fcis-12004	132	7	shen	shen	PROPN
fcis-12004	132	8	c	c	PROPN
fcis-12004	132	9	,	,	PUNCT
fcis-12004	132	10	cao	cao	PROPN
fcis-12004	132	11	l	l	PROPN
fcis-12004	132	12	,	,	PUNCT
fcis-12004	132	13	et	et	PROPN
fcis-12004	132	14	al	al	PROPN
fcis-12004	132	15	.	.	PUNCT
fcis-12004	133	1	deep	deep	ADJ
fcis-12004	133	2	learning	learning	NOUN
fcis-12004	133	3	for	for	ADP
fcis-12004	133	4	anomaly	anomaly	NOUN
fcis-12004	133	5	detection	detection	NOUN
fcis-12004	133	6	:	:	PUNCT
fcis-12004	133	7	a	a	DET
fcis-12004	133	8	review[j	review[j	PROPN
fcis-12004	133	9	]	]	PUNCT
fcis-12004	133	10	.	.	PUNCT
fcis-12004	134	1	acm	acm	PROPN
fcis-12004	134	2	computing	computing	NOUN
fcis-12004	134	3	surveys	survey	NOUN
fcis-12004	134	4	(	(	PUNCT
fcis-12004	134	5	csur	csur	NOUN
fcis-12004	134	6	)	)	PUNCT
fcis-12004	134	7	,	,	PUNCT
fcis-12004	134	8	2021	2021	NUM
fcis-12004	134	9	,	,	PUNCT
fcis-12004	134	10	54(2	54(2	NUM
fcis-12004	134	11	):	):	PUNCT
fcis-12004	134	12	1	1	NUM
fcis-12004	134	13	-	-	SYM
fcis-12004	134	14	38	38	NUM
fcis-12004	134	15	.	.	PUNCT
fcis-12004	135	1	[	[	X
fcis-12004	135	2	4	4	NUM
fcis-12004	135	3	]	]	PUNCT
fcis-12004	135	4	soltani	soltani	PROPN
fcis-12004	135	5	nejad	nejad	PROPN
fcis-12004	135	6	s.	s.	PROPN
fcis-12004	135	7	weakly	weakly	ADV
fcis-12004	135	8	-	-	PUNCT
fcis-12004	135	9	supervised	supervise	VERB
fcis-12004	135	10	anomaly	anomaly	NOUN
fcis-12004	135	11	detection	detection	NOUN
fcis-12004	135	12	in	in	ADP
fcis-12004	135	13	surveillance	surveillance	NOUN
fcis-12004	135	14	videos	video	NOUN
fcis-12004	135	15	based	base	VERB
fcis-12004	135	16	on	on	ADP
fcis-12004	135	17	two	two	NUM
fcis-12004	135	18	-	-	PUNCT
fcis-12004	135	19	stream	stream	NOUN
fcis-12004	135	20	i3d	i3d	NOUN
fcis-12004	135	21	convolution	convolution	NOUN
fcis-12004	135	22	network[j	network[j	PROPN
fcis-12004	135	23	]	]	PUNCT
fcis-12004	135	24	.	.	PUNCT
fcis-12004	136	1	2023	2023	NUM
fcis-12004	136	2	.	.	PUNCT
fcis-12004	137	1	[	[	X
fcis-12004	137	2	5	5	NUM
fcis-12004	137	3	]	]	PUNCT
fcis-12004	137	4	munukutla	munukutla	NOUN
fcis-12004	137	5	p	p	PROPN
fcis-12004	137	6	s	s	PROPN
fcis-12004	137	7	,	,	PUNCT
fcis-12004	137	8	jain	jain	PROPN
fcis-12004	137	9	s.	s.	PROPN
fcis-12004	137	10	one	one	NUM
fcis-12004	137	11	shot	shot	NOUN
fcis-12004	137	12	learning	learn	VERB
fcis-12004	137	13	for	for	ADP
fcis-12004	137	14	video	video	NOUN
fcis-12004	137	15	object	object	NOUN
fcis-12004	137	16	segmentation	segmentation	NOUN
fcis-12004	137	17	using	use	VERB
fcis-12004	137	18	fully	fully	ADV
fcis-12004	137	19	convolutional	convolutional	ADJ
fcis-12004	137	20	i3d	i3d	NOUN
fcis-12004	137	21	network[j	network[j	PROPN
fcis-12004	137	22	]	]	PUNCT
fcis-12004	137	23	.	.	PUNCT
fcis-12004	138	1	[	[	X
fcis-12004	138	2	6	6	NUM
fcis-12004	138	3	]	]	X
fcis-12004	138	4	kexuan	kexuan	PROPN
fcis-12004	138	5	w	w	PROPN
fcis-12004	138	6	,	,	PUNCT
fcis-12004	138	7	yifei	yifei	NOUN
fcis-12004	138	8	x	x	PROPN
fcis-12004	138	9	,	,	PUNCT
fcis-12004	138	10	xinyu	xinyu	PROPN
fcis-12004	138	11	x.	x.	PROPN
fcis-12004	138	12	research	research	PROPN
fcis-12004	138	13	on	on	ADP
fcis-12004	138	14	machine	machine	NOUN
fcis-12004	138	15	learning	learning	NOUN
fcis-12004	138	16	based	base	VERB
fcis-12004	138	17	on	on	ADP
fcis-12004	138	18	multi	multi	ADJ
fcis-12004	138	19	-	-	ADJ
fcis-12004	138	20	label	label	ADJ
fcis-12004	138	21	algorithm[c]//2020	algorithm[c]//2020	ADP
fcis-12004	138	22	ieee	ieee	NOUN
fcis-12004	138	23	international	international	ADJ
fcis-12004	138	24	conference	conference	NOUN
fcis-12004	138	25	on	on	ADP
fcis-12004	138	26	artificial	artificial	ADJ
fcis-12004	138	27	intelligence	intelligence	NOUN
fcis-12004	138	28	and	and	CCONJ
fcis-12004	138	29	computer	computer	NOUN
fcis-12004	138	30	applications	application	NOUN
fcis-12004	138	31	(	(	PUNCT
fcis-12004	138	32	icaica	icaica	PROPN
fcis-12004	138	33	)	)	PUNCT
fcis-12004	138	34	.	.	PUNCT
fcis-12004	139	1	ieee	ieee	PROPN
fcis-12004	139	2	,	,	PUNCT
fcis-12004	139	3	2020	2020	NUM
fcis-12004	139	4	:	:	PUNCT
fcis-12004	139	5	824	824	NUM
fcis-12004	139	6	-	-	SYM
fcis-12004	139	7	829	829	NUM
fcis-12004	139	8	.	.	PUNCT
fcis-12004	140	1	[	[	X
fcis-12004	140	2	7	7	X
fcis-12004	140	3	]	]	PUNCT
fcis-12004	140	4	huaihui	huaihui	PROPN
fcis-12004	140	5	c	c	PROPN
fcis-12004	140	6	,	,	PUNCT
fcis-12004	140	7	shengbo	shengbo	PROPN
fcis-12004	140	8	z.	z.	PROPN
fcis-12004	140	9	multi	multi	PROPN
fcis-12004	140	10	instance	instance	PROPN
fcis-12004	140	11	deep	deep	ADJ
fcis-12004	140	12	learning	learning	NOUN
fcis-12004	140	13	target	target	NOUN
fcis-12004	140	14	tracking[j	tracking[j	NOUN
fcis-12004	140	15	]	]	PUNCT
fcis-12004	140	16	.	.	PUNCT
fcis-12004	141	1	the	the	DET
fcis-12004	141	2	frontiers	frontier	NOUN
fcis-12004	141	3	of	of	ADP
fcis-12004	141	4	society	society	NOUN
fcis-12004	141	5	,	,	PUNCT
fcis-12004	141	6	science	science	NOUN
fcis-12004	141	7	and	and	CCONJ
fcis-12004	141	8	technology	technology	NOUN
fcis-12004	141	9	,	,	PUNCT
fcis-12004	141	10	2020	2020	NUM
fcis-12004	141	11	,	,	PUNCT
fcis-12004	141	12	2(9	2(9	NUM
fcis-12004	141	13	)	)	PUNCT
fcis-12004	141	14	.	.	PUNCT
fcis-12004	142	1	[	[	X
fcis-12004	142	2	8	8	NUM
fcis-12004	142	3	]	]	X
fcis-12004	142	4	chaganti	chaganti	NOUN
fcis-12004	142	5	p	p	PROPN
fcis-12004	142	6	c	c	PROPN
fcis-12004	142	7	v	v	NOUN
fcis-12004	142	8	,	,	PUNCT
fcis-12004	142	9	vasireddy	vasireddy	NOUN
fcis-12004	142	10	k	k	PROPN
fcis-12004	142	11	,	,	PUNCT
fcis-12004	142	12	reddy	reddy	PROPN
fcis-12004	142	13	e	e	PROPN
fcis-12004	142	14	r	r	PROPN
fcis-12004	142	15	,	,	PUNCT
fcis-12004	142	16	et	et	PROPN
fcis-12004	142	17	al	al	PROPN
fcis-12004	142	18	.	.	PROPN
fcis-12004	142	19	predicting	predict	VERB
fcis-12004	142	20	landslides	landslide	NOUN
fcis-12004	142	21	and	and	CCONJ
fcis-12004	142	22	floods	flood	NOUN
fcis-12004	142	23	with	with	ADP
fcis-12004	142	24	deep	deep	ADJ
fcis-12004	142	25	learning[c]//2023	learning[c]//2023	PRON
fcis-12004	142	26	4th	4th	ADJ
fcis-12004	142	27	international	international	ADJ
fcis-12004	142	28	conference	conference	NOUN
fcis-12004	142	29	on	on	ADP
fcis-12004	142	30	electronics	electronic	NOUN
fcis-12004	142	31	and	and	CCONJ
fcis-12004	142	32	sustainable	sustainable	ADJ
fcis-12004	142	33	communication	communication	NOUN
fcis-12004	142	34	systems	system	NOUN
fcis-12004	142	35	(	(	PUNCT
fcis-12004	142	36	icesc	icesc	PROPN
fcis-12004	142	37	)	)	PUNCT
fcis-12004	142	38	.	.	PUNCT
fcis-12004	143	1	ieee	ieee	NOUN
fcis-12004	143	2	,	,	PUNCT
fcis-12004	143	3	2023	2023	NUM
fcis-12004	143	4	:	:	PUNCT
fcis-12004	143	5	1259	1259	NUM
fcis-12004	143	6	-	-	SYM
fcis-12004	143	7	1265	1265	NUM
fcis-12004	143	8	.	.	PUNCT
fcis-12004	144	1	[	[	X
fcis-12004	144	2	9	9	NUM
fcis-12004	144	3	]	]	SYM
fcis-12004	144	4	li	li	PROPN
fcis-12004	144	5	l	l	PROPN
fcis-12004	144	6	,	,	PUNCT
fcis-12004	144	7	doroslovački	doroslovački	PROPN
fcis-12004	144	8	m	m	PROPN
fcis-12004	144	9	,	,	PUNCT
fcis-12004	144	10	loew	loew	PROPN
fcis-12004	144	11	m	m	AUX
fcis-12004	144	12	h.	h.	NOUN
fcis-12004	144	13	approximating	approximate	VERB
fcis-12004	144	14	the	the	DET
fcis-12004	144	15	gradient	gradient	NOUN
fcis-12004	144	16	of	of	ADP
fcis-12004	144	17	cross	cross	ADJ
fcis-12004	144	18	-	-	ADJ
fcis-12004	144	19	entropy	entropy	ADJ
fcis-12004	144	20	loss	loss	NOUN
fcis-12004	144	21	function[j	function[j	PROPN
fcis-12004	144	22	]	]	PUNCT
fcis-12004	144	23	.	.	PUNCT
fcis-12004	145	1	ieee	ieee	NOUN
fcis-12004	145	2	access	access	NOUN
fcis-12004	145	3	,	,	PUNCT
fcis-12004	145	4	2020	2020	NUM
fcis-12004	145	5	,	,	PUNCT
fcis-12004	145	6	8	8	NUM
fcis-12004	145	7	:	:	SYM
fcis-12004	145	8	111626	111626	NUM
fcis-12004	145	9	-	-	SYM
fcis-12004	145	10	111635	111635	NUM
fcis-12004	145	11	.	.	PUNCT
fcis-12004	146	1	[	[	X
fcis-12004	146	2	10	10	NUM
fcis-12004	146	3	]	]	X
fcis-12004	146	4	tran	tran	PROPN
fcis-12004	146	5	d	d	PROPN
fcis-12004	146	6	,	,	PUNCT
fcis-12004	146	7	bourdev	bourdev	PROPN
fcis-12004	146	8	l	l	NOUN
fcis-12004	146	9	,	,	PUNCT
fcis-12004	146	10	fergus	fergus	PROPN
fcis-12004	146	11	r	r	PROPN
fcis-12004	146	12	,	,	PUNCT
fcis-12004	146	13	et	et	PROPN
fcis-12004	146	14	al	al	PROPN
fcis-12004	146	15	.	.	PUNCT
fcis-12004	147	1	learning	learn	VERB
fcis-12004	147	2	spatiotemporal	spatiotemporal	ADJ
fcis-12004	147	3	features	feature	NOUN
fcis-12004	147	4	with	with	ADP
fcis-12004	147	5	3d	3d	NUM
fcis-12004	147	6	convolutional	convolutional	ADJ
fcis-12004	147	7	networks[c]//proceedings	networks[c]//proceeding	NOUN
fcis-12004	147	8	of	of	ADP
fcis-12004	147	9	the	the	DET
fcis-12004	147	10	ieee	ieee	NOUN
fcis-12004	147	11	international	international	PROPN
fcis-12004	147	12	conference	conference	NOUN
fcis-12004	147	13	on	on	ADP
fcis-12004	147	14	computer	computer	NOUN
fcis-12004	147	15	vision	vision	NOUN
fcis-12004	147	16	.	.	PUNCT
fcis-12004	148	1	2015	2015	NUM
fcis-12004	148	2	:	:	PUNCT
fcis-12004	148	3	4489	4489	NUM
fcis-12004	148	4	-	-	SYM
fcis-12004	148	5	4497	4497	NUM
fcis-12004	148	6	.	.	PUNCT