id	sid	tid	token	lemma	pos
fcis-6030	1	1	frontiers	frontier	NOUN
fcis-6030	1	2	in	in	ADP
fcis-6030	1	3	computing	computing	NOUN
fcis-6030	1	4	and	and	CCONJ
fcis-6030	1	5	intelligent	intelligent	ADJ
fcis-6030	1	6	systems	system	NOUN
fcis-6030	1	7	issn	issn	VERB
fcis-6030	1	8	:	:	PUNCT
fcis-6030	1	9	2832	2832	NUM
fcis-6030	1	10	-	-	SYM
fcis-6030	1	11	6024	6024	NUM
fcis-6030	1	12	|	|	NOUN
fcis-6030	1	13	vol	vol	NOUN
fcis-6030	1	14	.	.	PROPN
fcis-6030	2	1	3	3	NUM
fcis-6030	2	2	,	,	PUNCT
fcis-6030	2	3	no	no	INTJ
fcis-6030	2	4	.	.	NOUN
fcis-6030	2	5	1	1	NUM
fcis-6030	2	6	,	,	PUNCT
fcis-6030	2	7	2023	2023	NUM
fcis-6030	2	8	85	85	NUM
fcis-6030	2	9	text	text	NOUN
fcis-6030	2	10	-	-	PUNCT
fcis-6030	2	11	to	to	ADP
fcis-6030	2	12	-	-	PUNCT
fcis-6030	2	13	classic	classic	ADJ
fcis-6030	2	14	:	:	PUNCT
fcis-6030	2	15	a	a	DET
fcis-6030	2	16	diffusion	diffusion	NOUN
fcis-6030	2	17	method	method	NOUN
fcis-6030	2	18	for	for	ADP
fcis-6030	2	19	classical	classical	ADJ
fcis-6030	2	20	art	art	NOUN
fcis-6030	2	21	generation	generation	NOUN
fcis-6030	2	22	based	base	VERB
fcis-6030	2	23	on	on	ADP
fcis-6030	2	24	text	text	NOUN
fcis-6030	2	25	yi	yi	PROPN
fcis-6030	2	26	li	li	PROPN
fcis-6030	2	27	school	school	PROPN
fcis-6030	2	28	of	of	ADP
fcis-6030	2	29	science	science	NOUN
fcis-6030	2	30	and	and	CCONJ
fcis-6030	2	31	engineering	engineering	NOUN
fcis-6030	2	32	,	,	PUNCT
fcis-6030	2	33	the	the	DET
fcis-6030	2	34	chinese	chinese	ADJ
fcis-6030	2	35	university	university	PROPN
fcis-6030	2	36	of	of	ADP
fcis-6030	2	37	hong	hong	PROPN
fcis-6030	2	38	kong	kong	PROPN
fcis-6030	2	39	shenzhen	shenzhen	PROPN
fcis-6030	2	40	,	,	PUNCT
fcis-6030	2	41	shenzhen	shenzhen	PROPN
fcis-6030	2	42	,	,	PUNCT
fcis-6030	2	43	518172	518172	NUM
fcis-6030	2	44	,	,	PUNCT
fcis-6030	2	45	china	china	PROPN
fcis-6030	2	46	yili24@link.cuhk.edu.cn	yili24@link.cuhk.edu.cn	PROPN
fcis-6030	2	47	abstract	abstract	NOUN
fcis-6030	2	48	:	:	PUNCT
fcis-6030	2	49	text	text	NOUN
fcis-6030	2	50	-	-	PUNCT
fcis-6030	2	51	to	to	ADP
fcis-6030	2	52	-	-	PUNCT
fcis-6030	2	53	image	image	NOUN
fcis-6030	2	54	generation	generation	NOUN
fcis-6030	2	55	has	have	AUX
fcis-6030	2	56	recently	recently	ADV
fcis-6030	2	57	become	become	VERB
fcis-6030	2	58	a	a	DET
fcis-6030	2	59	hot	hot	ADJ
fcis-6030	2	60	research	research	NOUN
fcis-6030	2	61	topic	topic	NOUN
fcis-6030	2	62	and	and	CCONJ
fcis-6030	2	63	diffusion	diffusion	NOUN
fcis-6030	2	64	models	model	NOUN
fcis-6030	2	65	have	have	AUX
fcis-6030	2	66	achieved	achieve	VERB
fcis-6030	2	67	remarkable	remarkable	ADJ
fcis-6030	2	68	performance	performance	NOUN
fcis-6030	2	69	in	in	ADP
fcis-6030	2	70	this	this	DET
fcis-6030	2	71	task	task	NOUN
fcis-6030	2	72	.	.	PUNCT
fcis-6030	3	1	however	however	ADV
fcis-6030	3	2	,	,	PUNCT
fcis-6030	3	3	most	most	ADV
fcis-6030	3	4	previous	previous	ADJ
fcis-6030	3	5	researches	research	NOUN
fcis-6030	3	6	aim	aim	VERB
fcis-6030	3	7	at	at	ADP
fcis-6030	3	8	real	real	ADJ
fcis-6030	3	9	scene	scene	NOUN
fcis-6030	3	10	generation	generation	NOUN
fcis-6030	3	11	.	.	PUNCT
fcis-6030	4	1	few	few	ADJ
fcis-6030	4	2	researches	research	NOUN
fcis-6030	4	3	focus	focus	VERB
fcis-6030	4	4	on	on	ADP
fcis-6030	4	5	classical	classical	ADJ
fcis-6030	4	6	art	art	NOUN
fcis-6030	4	7	paintings	painting	NOUN
fcis-6030	4	8	.	.	PUNCT
fcis-6030	5	1	besides	besides	SCONJ
fcis-6030	5	2	,	,	PUNCT
fcis-6030	5	3	diffusion	diffusion	NOUN
fcis-6030	5	4	models	model	NOUN
fcis-6030	5	5	are	be	AUX
fcis-6030	5	6	commonly	commonly	ADV
fcis-6030	5	7	heavy	heavy	ADV
fcis-6030	5	8	-	-	PUNCT
fcis-6030	5	9	weighted	weight	VERB
fcis-6030	5	10	with	with	ADP
fcis-6030	5	11	a	a	DET
fcis-6030	5	12	large	large	ADJ
fcis-6030	5	13	number	number	NOUN
fcis-6030	5	14	of	of	ADP
fcis-6030	5	15	parameters	parameter	NOUN
fcis-6030	5	16	,	,	PUNCT
fcis-6030	5	17	which	which	PRON
fcis-6030	5	18	has	have	VERB
fcis-6030	5	19	a	a	DET
fcis-6030	5	20	high	high	ADJ
fcis-6030	5	21	computational	computational	ADJ
fcis-6030	5	22	cost	cost	NOUN
fcis-6030	5	23	.	.	PUNCT
fcis-6030	6	1	in	in	ADP
fcis-6030	6	2	this	this	DET
fcis-6030	6	3	paper	paper	NOUN
fcis-6030	6	4	,	,	PUNCT
fcis-6030	6	5	we	we	PRON
fcis-6030	6	6	aim	aim	VERB
fcis-6030	6	7	to	to	PART
fcis-6030	6	8	solve	solve	VERB
fcis-6030	6	9	the	the	DET
fcis-6030	6	10	classical	classical	ADJ
fcis-6030	6	11	art	art	NOUN
fcis-6030	6	12	paintings	painting	NOUN
fcis-6030	6	13	synthesis	synthesis	NOUN
fcis-6030	6	14	subtask	subtask	NOUN
fcis-6030	6	15	.	.	PUNCT
fcis-6030	7	1	we	we	PRON
fcis-6030	7	2	propose	propose	VERB
fcis-6030	7	3	a	a	DET
fcis-6030	7	4	lightweight	lightweight	ADJ
fcis-6030	7	5	diffusion	diffusion	NOUN
fcis-6030	7	6	model	model	NOUN
fcis-6030	7	7	text	text	NOUN
fcis-6030	7	8	-	-	PUNCT
fcis-6030	7	9	to	to	ADP
fcis-6030	7	10	-	-	PUNCT
fcis-6030	7	11	classic(t2c	classic(t2c	PROPN
fcis-6030	7	12	)	)	PUNCT
fcis-6030	7	13	to	to	PART
fcis-6030	7	14	synthesize	synthesize	VERB
fcis-6030	7	15	classical	classical	ADJ
fcis-6030	7	16	art	art	NOUN
fcis-6030	7	17	paintings	painting	NOUN
fcis-6030	7	18	according	accord	VERB
fcis-6030	7	19	to	to	ADP
fcis-6030	7	20	text	text	NOUN
fcis-6030	7	21	descriptions	description	NOUN
fcis-6030	7	22	.	.	PUNCT
fcis-6030	8	1	experiment	experiment	NOUN
fcis-6030	8	2	results	result	NOUN
fcis-6030	8	3	show	show	VERB
fcis-6030	8	4	that	that	SCONJ
fcis-6030	8	5	our	our	PRON
fcis-6030	8	6	method	method	NOUN
fcis-6030	8	7	can	can	AUX
fcis-6030	8	8	achieve	achieve	VERB
fcis-6030	8	9	good	good	ADJ
fcis-6030	8	10	performance	performance	NOUN
fcis-6030	8	11	with	with	ADP
fcis-6030	8	12	fewer	few	ADJ
fcis-6030	8	13	parameters	parameter	NOUN
fcis-6030	8	14	.	.	PUNCT
fcis-6030	9	1	keywords	keyword	NOUN
fcis-6030	9	2	:	:	PUNCT
fcis-6030	9	3	denoising	denoise	VERB
fcis-6030	9	4	diffusion	diffusion	NOUN
fcis-6030	9	5	probabilistic	probabilistic	ADJ
fcis-6030	9	6	model	model	NOUN
fcis-6030	9	7	;	;	PUNCT
fcis-6030	9	8	text	text	NOUN
fcis-6030	9	9	-	-	PUNCT
fcis-6030	9	10	to	to	ADP
fcis-6030	9	11	-	-	PUNCT
fcis-6030	9	12	image	image	NOUN
fcis-6030	9	13	generation	generation	NOUN
fcis-6030	9	14	;	;	PUNCT
fcis-6030	9	15	art	art	NOUN
fcis-6030	9	16	.	.	PUNCT
fcis-6030	10	1	1	1	X
fcis-6030	10	2	.	.	X
fcis-6030	10	3	introduction	introduction	NOUN
fcis-6030	10	4	with	with	ADP
fcis-6030	10	5	the	the	DET
fcis-6030	10	6	progress	progress	NOUN
fcis-6030	10	7	in	in	ADP
fcis-6030	10	8	artificial	artificial	ADJ
fcis-6030	10	9	intelligence	intelligence	NOUN
fcis-6030	10	10	(	(	PUNCT
fcis-6030	10	11	ai	ai	NOUN
fcis-6030	10	12	)	)	PUNCT
fcis-6030	10	13	and	and	CCONJ
fcis-6030	10	14	deep	deep	ADJ
fcis-6030	10	15	learning	learning	NOUN
fcis-6030	10	16	,	,	PUNCT
fcis-6030	10	17	researches	research	VERB
fcis-6030	10	18	on	on	ADP
fcis-6030	10	19	image	image	NOUN
fcis-6030	10	20	synthesis	synthesis	NOUN
fcis-6030	10	21	have	have	AUX
fcis-6030	10	22	come	come	VERB
fcis-6030	10	23	into	into	ADP
fcis-6030	10	24	prominence	prominence	NOUN
fcis-6030	10	25	.	.	PUNCT
fcis-6030	11	1	ai	ai	VERB
fcis-6030	11	2	image	image	NOUN
fcis-6030	11	3	synthesis	synthesis	NOUN
fcis-6030	11	4	is	be	AUX
fcis-6030	11	5	a	a	DET
fcis-6030	11	6	task	task	NOUN
fcis-6030	11	7	where	where	SCONJ
fcis-6030	11	8	ai	ai	VERB
fcis-6030	11	9	learns	learn	VERB
fcis-6030	11	10	to	to	PART
fcis-6030	11	11	understand	understand	VERB
fcis-6030	11	12	and	and	CCONJ
fcis-6030	11	13	reproduce	reproduce	VERB
fcis-6030	11	14	images	image	NOUN
fcis-6030	11	15	generated	generate	VERB
fcis-6030	11	16	by	by	ADP
fcis-6030	11	17	humans	human	NOUN
fcis-6030	11	18	.	.	PUNCT
fcis-6030	12	1	many	many	ADJ
fcis-6030	12	2	methods	method	NOUN
fcis-6030	12	3	have	have	AUX
fcis-6030	12	4	achieved	achieve	VERB
fcis-6030	12	5	remarkable	remarkable	ADJ
fcis-6030	12	6	performances	performance	NOUN
fcis-6030	12	7	and	and	CCONJ
fcis-6030	12	8	generated	generate	VERB
fcis-6030	12	9	images	image	NOUN
fcis-6030	12	10	indistinguishable	indistinguishable	ADJ
fcis-6030	12	11	from	from	ADP
fcis-6030	12	12	the	the	DET
fcis-6030	12	13	real	real	NOUN
fcis-6030	12	14	by	by	ADP
fcis-6030	12	15	most	most	ADJ
fcis-6030	12	16	people	people	NOUN
fcis-6030	12	17	.	.	PUNCT
fcis-6030	13	1	while	while	SCONJ
fcis-6030	13	2	traditionally	traditionally	ADV
fcis-6030	13	3	realistic	realistic	ADJ
fcis-6030	13	4	images	image	NOUN
fcis-6030	13	5	are	be	AUX
fcis-6030	13	6	synthesized	synthesize	VERB
fcis-6030	13	7	from	from	ADP
fcis-6030	13	8	noise	noise	NOUN
fcis-6030	13	9	inputs	input	NOUN
fcis-6030	13	10	,	,	PUNCT
fcis-6030	13	11	recently	recently	ADV
fcis-6030	13	12	multimodal	multimodal	ADJ
fcis-6030	13	13	learning	learning	NOUN
fcis-6030	13	14	has	have	AUX
fcis-6030	13	15	become	become	VERB
fcis-6030	13	16	a	a	DET
fcis-6030	13	17	hot	hot	ADJ
fcis-6030	13	18	spot	spot	NOUN
fcis-6030	13	19	and	and	CCONJ
fcis-6030	13	20	text	text	NOUN
fcis-6030	13	21	-	-	PUNCT
fcis-6030	13	22	to	to	ADP
fcis-6030	13	23	-	-	PUNCT
fcis-6030	13	24	image	image	NOUN
fcis-6030	13	25	synthesis	synthesis	NOUN
fcis-6030	13	26	is	be	AUX
fcis-6030	13	27	at	at	ADP
fcis-6030	13	28	the	the	DET
fcis-6030	13	29	forefront	forefront	NOUN
fcis-6030	13	30	.	.	PUNCT
fcis-6030	14	1	in	in	ADP
fcis-6030	14	2	text	text	NOUN
fcis-6030	14	3	-	-	PUNCT
fcis-6030	14	4	image	image	NOUN
fcis-6030	14	5	multimodal	multimodal	NOUN
fcis-6030	14	6	learning	learning	NOUN
fcis-6030	14	7	,	,	PUNCT
fcis-6030	14	8	ai	ai	VERB
fcis-6030	14	9	learns	learn	NOUN
fcis-6030	14	10	to	to	PART
fcis-6030	14	11	understand	understand	VERB
fcis-6030	14	12	textimage	textimage	NOUN
fcis-6030	14	13	correspondence	correspondence	NOUN
fcis-6030	14	14	.	.	PUNCT
fcis-6030	15	1	it	it	PRON
fcis-6030	15	2	combines	combine	VERB
fcis-6030	15	3	natural	natural	ADJ
fcis-6030	15	4	language	language	NOUN
fcis-6030	15	5	processing	processing	NOUN
fcis-6030	15	6	(	(	PUNCT
fcis-6030	15	7	nlp	nlp	NOUN
fcis-6030	15	8	)	)	PUNCT
fcis-6030	15	9	and	and	CCONJ
fcis-6030	15	10	computer	computer	NOUN
fcis-6030	15	11	vision	vision	NOUN
fcis-6030	15	12	(	(	PUNCT
fcis-6030	15	13	cv	cv	PROPN
fcis-6030	15	14	)	)	PUNCT
fcis-6030	15	15	.	.	PUNCT
fcis-6030	16	1	in	in	ADP
fcis-6030	16	2	the	the	DET
fcis-6030	16	3	task	task	NOUN
fcis-6030	16	4	of	of	ADP
fcis-6030	16	5	text	text	NOUN
fcis-6030	16	6	-	-	PUNCT
fcis-6030	16	7	to	to	ADP
fcis-6030	16	8	-	-	PUNCT
fcis-6030	16	9	image	image	NOUN
fcis-6030	16	10	synthesis	synthesis	NOUN
fcis-6030	16	11	,	,	PUNCT
fcis-6030	16	12	given	give	VERB
fcis-6030	16	13	a	a	DET
fcis-6030	16	14	description	description	NOUN
fcis-6030	16	15	in	in	ADP
fcis-6030	16	16	natural	natural	ADJ
fcis-6030	16	17	language	language	NOUN
fcis-6030	16	18	,	,	PUNCT
fcis-6030	16	19	a	a	DET
fcis-6030	16	20	realistic	realistic	ADJ
fcis-6030	16	21	image	image	NOUN
fcis-6030	16	22	matching	match	VERB
fcis-6030	16	23	the	the	DET
fcis-6030	16	24	description	description	NOUN
fcis-6030	16	25	will	will	AUX
fcis-6030	16	26	be	be	AUX
fcis-6030	16	27	generated	generate	VERB
fcis-6030	16	28	.	.	PUNCT
fcis-6030	17	1	a	a	DET
fcis-6030	17	2	typical	typical	ADJ
fcis-6030	17	3	approach	approach	NOUN
fcis-6030	17	4	for	for	ADP
fcis-6030	17	5	text	text	NOUN
fcis-6030	17	6	-	-	PUNCT
fcis-6030	17	7	to	to	ADP
fcis-6030	17	8	-	-	PUNCT
fcis-6030	17	9	image	image	NOUN
fcis-6030	17	10	tasks	task	NOUN
fcis-6030	17	11	is	be	AUX
fcis-6030	17	12	to	to	PART
fcis-6030	17	13	use	use	VERB
fcis-6030	17	14	an	an	DET
fcis-6030	17	15	nlp	nlp	NOUN
fcis-6030	17	16	model	model	NOUN
fcis-6030	17	17	as	as	ADP
fcis-6030	17	18	the	the	DET
fcis-6030	17	19	encoder	encoder	NOUN
fcis-6030	17	20	and	and	CCONJ
fcis-6030	17	21	an	an	DET
fcis-6030	17	22	image	image	NOUN
fcis-6030	17	23	synthesis	synthesis	NOUN
fcis-6030	17	24	model	model	NOUN
fcis-6030	17	25	as	as	ADP
fcis-6030	17	26	the	the	DET
fcis-6030	17	27	decoder	decoder	NOUN
fcis-6030	17	28	.	.	PUNCT
fcis-6030	18	1	by	by	ADP
fcis-6030	18	2	such	such	DET
fcis-6030	18	3	an	an	DET
fcis-6030	18	4	encoder	encoder	NOUN
fcis-6030	18	5	-	-	PUNCT
fcis-6030	18	6	decoder	decoder	NOUN
fcis-6030	18	7	design	design	NOUN
fcis-6030	18	8	,	,	PUNCT
fcis-6030	18	9	ai	ai	VERB
fcis-6030	18	10	learns	learn	NOUN
fcis-6030	18	11	to	to	PART
fcis-6030	18	12	combine	combine	VERB
fcis-6030	18	13	both	both	CCONJ
fcis-6030	18	14	linguistic	linguistic	ADJ
fcis-6030	18	15	and	and	CCONJ
fcis-6030	18	16	visual	visual	ADJ
fcis-6030	18	17	information	information	NOUN
fcis-6030	18	18	.	.	PUNCT
fcis-6030	19	1	while	while	SCONJ
fcis-6030	19	2	the	the	DET
fcis-6030	19	3	correspondence	correspondence	NOUN
fcis-6030	19	4	between	between	ADP
fcis-6030	19	5	common	common	ADJ
fcis-6030	19	6	-	-	PUNCT
fcis-6030	19	7	scene	scene	NOUN
fcis-6030	19	8	images	image	NOUN
fcis-6030	19	9	and	and	CCONJ
fcis-6030	19	10	their	their	PRON
fcis-6030	19	11	descriptions	description	NOUN
fcis-6030	19	12	is	be	AUX
fcis-6030	19	13	relatively	relatively	ADV
fcis-6030	19	14	simple	simple	ADJ
fcis-6030	19	15	,	,	PUNCT
fcis-6030	19	16	understanding	understand	VERB
fcis-6030	19	17	artwork	artwork	NOUN
fcis-6030	19	18	is	be	AUX
fcis-6030	19	19	much	much	ADV
fcis-6030	19	20	more	more	ADV
fcis-6030	19	21	challenging	challenging	ADJ
fcis-6030	19	22	.	.	PUNCT
fcis-6030	20	1	text	text	NOUN
fcis-6030	20	2	-	-	PUNCT
fcis-6030	20	3	to	to	ADP
fcis-6030	20	4	-	-	PUNCT
fcis-6030	20	5	art	art	NOUN
fcis-6030	20	6	,	,	PUNCT
fcis-6030	20	7	as	as	ADP
fcis-6030	20	8	a	a	DET
fcis-6030	20	9	subtask	subtask	NOUN
fcis-6030	20	10	of	of	ADP
fcis-6030	20	11	text	text	NOUN
fcis-6030	20	12	-	-	PUNCT
fcis-6030	20	13	toimage	toimage	NOUN
fcis-6030	20	14	synthesis	synthesis	NOUN
fcis-6030	20	15	,	,	PUNCT
fcis-6030	20	16	aims	aim	VERB
fcis-6030	20	17	at	at	ADP
fcis-6030	20	18	training	training	NOUN
fcis-6030	20	19	ai	ai	VERB
fcis-6030	20	20	to	to	PART
fcis-6030	20	21	understand	understand	VERB
fcis-6030	20	22	art	art	NOUN
fcis-6030	20	23	language	language	NOUN
fcis-6030	20	24	.	.	PUNCT
fcis-6030	21	1	the	the	DET
fcis-6030	21	2	goal	goal	NOUN
fcis-6030	21	3	of	of	ADP
fcis-6030	21	4	this	this	DET
fcis-6030	21	5	task	task	NOUN
fcis-6030	21	6	is	be	AUX
fcis-6030	21	7	to	to	PART
fcis-6030	21	8	generate	generate	VERB
fcis-6030	21	9	high	high	ADJ
fcis-6030	21	10	-	-	PUNCT
fcis-6030	21	11	quality	quality	NOUN
fcis-6030	21	12	artworks	artwork	NOUN
fcis-6030	21	13	from	from	ADP
fcis-6030	21	14	descriptions	description	NOUN
fcis-6030	21	15	.	.	PUNCT
fcis-6030	22	1	given	give	VERB
fcis-6030	22	2	its	its	PRON
fcis-6030	22	3	great	great	ADJ
fcis-6030	22	4	value	value	NOUN
fcis-6030	22	5	in	in	ADP
fcis-6030	22	6	social	social	ADJ
fcis-6030	22	7	media	medium	NOUN
fcis-6030	22	8	,	,	PUNCT
fcis-6030	22	9	understanding	understand	VERB
fcis-6030	22	10	artworks	artwork	NOUN
fcis-6030	22	11	based	base	VERB
fcis-6030	22	12	on	on	ADP
fcis-6030	22	13	heuristics	heuristic	NOUN
fcis-6030	22	14	has	have	AUX
fcis-6030	22	15	been	be	AUX
fcis-6030	22	16	widely	widely	ADV
fcis-6030	22	17	researched	research	VERB
fcis-6030	22	18	.	.	PUNCT
fcis-6030	23	1	it	it	PRON
fcis-6030	23	2	is	be	AUX
fcis-6030	23	3	easy	easy	ADJ
fcis-6030	23	4	for	for	SCONJ
fcis-6030	23	5	normal	normal	ADJ
fcis-6030	23	6	people	people	NOUN
fcis-6030	23	7	to	to	PART
fcis-6030	23	8	imagine	imagine	VERB
fcis-6030	23	9	common	common	ADJ
fcis-6030	23	10	scenes	scene	NOUN
fcis-6030	23	11	based	base	VERB
fcis-6030	23	12	on	on	ADP
fcis-6030	23	13	descriptions	description	NOUN
fcis-6030	23	14	since	since	SCONJ
fcis-6030	23	15	these	these	DET
fcis-6030	23	16	elements	element	NOUN
fcis-6030	23	17	are	be	AUX
fcis-6030	23	18	familiar	familiar	ADJ
fcis-6030	23	19	in	in	ADP
fcis-6030	23	20	our	our	PRON
fcis-6030	23	21	daily	daily	ADJ
fcis-6030	23	22	life	life	NOUN
fcis-6030	23	23	.	.	PUNCT
fcis-6030	24	1	however	however	ADV
fcis-6030	24	2	,	,	PUNCT
fcis-6030	24	3	envisaging	envisage	VERB
fcis-6030	24	4	art	art	NOUN
fcis-6030	24	5	paintings	painting	NOUN
fcis-6030	24	6	is	be	AUX
fcis-6030	24	7	much	much	ADV
fcis-6030	24	8	harder	hard	ADJ
fcis-6030	24	9	for	for	ADP
fcis-6030	24	10	the	the	DET
fcis-6030	24	11	public	public	NOUN
fcis-6030	24	12	due	due	ADP
fcis-6030	24	13	to	to	ADP
fcis-6030	24	14	the	the	DET
fcis-6030	24	15	lack	lack	NOUN
fcis-6030	24	16	of	of	ADP
fcis-6030	24	17	art	art	NOUN
fcis-6030	24	18	knowledge	knowledge	NOUN
fcis-6030	24	19	.	.	PUNCT
fcis-6030	25	1	understanding	understand	VERB
fcis-6030	25	2	art	art	NOUN
fcis-6030	25	3	seems	seem	VERB
fcis-6030	25	4	to	to	PART
fcis-6030	25	5	become	become	VERB
fcis-6030	25	6	the	the	DET
fcis-6030	25	7	privilege	privilege	NOUN
fcis-6030	25	8	of	of	ADP
fcis-6030	25	9	professional	professional	ADJ
fcis-6030	25	10	art	art	NOUN
fcis-6030	25	11	critics	critic	NOUN
fcis-6030	25	12	.	.	PUNCT
fcis-6030	26	1	to	to	PART
fcis-6030	26	2	address	address	VERB
fcis-6030	26	3	such	such	DET
fcis-6030	26	4	a	a	DET
fcis-6030	26	5	problem	problem	NOUN
fcis-6030	26	6	,	,	PUNCT
fcis-6030	26	7	ai	ai	VERB
fcis-6030	26	8	art	art	NOUN
fcis-6030	26	9	understanding	understanding	NOUN
fcis-6030	26	10	has	have	AUX
fcis-6030	26	11	become	become	VERB
fcis-6030	26	12	necessary	necessary	ADJ
fcis-6030	26	13	.	.	PUNCT
fcis-6030	27	1	neural	neural	ADJ
fcis-6030	27	2	style	style	NOUN
fcis-6030	27	3	transfer	transfer	NOUN
fcis-6030	27	4	(	(	PUNCT
fcis-6030	27	5	nst	nst	NOUN
fcis-6030	27	6	)	)	PUNCT
fcis-6030	27	7	aims	aim	VERB
fcis-6030	27	8	to	to	PART
fcis-6030	27	9	train	train	VERB
fcis-6030	27	10	ai	ai	VERB
fcis-6030	27	11	to	to	PART
fcis-6030	27	12	learn	learn	VERB
fcis-6030	27	13	from	from	ADP
fcis-6030	27	14	style	style	NOUN
fcis-6030	27	15	images	image	NOUN
fcis-6030	27	16	.	.	PUNCT
fcis-6030	28	1	in	in	ADP
fcis-6030	28	2	nst	nst	PROPN
fcis-6030	28	3	,	,	PUNCT
fcis-6030	28	4	features	feature	NOUN
fcis-6030	28	5	from	from	ADP
fcis-6030	28	6	a	a	DET
fcis-6030	28	7	“	"	PUNCT
fcis-6030	28	8	style	style	NOUN
fcis-6030	28	9	”	"	PUNCT
fcis-6030	28	10	image	image	NOUN
fcis-6030	28	11	and	and	CCONJ
fcis-6030	28	12	a	a	DET
fcis-6030	28	13	‘	'	PUNCT
fcis-6030	28	14	content	content	ADJ
fcis-6030	28	15	”	"	PUNCT
fcis-6030	28	16	image	image	NOUN
fcis-6030	28	17	are	be	AUX
fcis-6030	28	18	extracted	extract	VERB
fcis-6030	28	19	,	,	PUNCT
fcis-6030	28	20	and	and	CCONJ
fcis-6030	28	21	a	a	DET
fcis-6030	28	22	stylized	stylize	VERB
fcis-6030	28	23	image	image	NOUN
fcis-6030	28	24	is	be	AUX
fcis-6030	28	25	created	create	VERB
fcis-6030	28	26	by	by	ADP
fcis-6030	28	27	combining	combine	VERB
fcis-6030	28	28	these	these	DET
fcis-6030	28	29	features	feature	NOUN
fcis-6030	28	30	.	.	PUNCT
fcis-6030	29	1	in	in	ADP
fcis-6030	29	2	nst	nst	PROPN
fcis-6030	29	3	,	,	PUNCT
fcis-6030	29	4	ai	ai	VERB
fcis-6030	29	5	learns	learn	NOUN
fcis-6030	29	6	to	to	PART
fcis-6030	29	7	understand	understand	VERB
fcis-6030	29	8	artworks	artwork	NOUN
fcis-6030	29	9	from	from	ADP
fcis-6030	29	10	images	image	NOUN
fcis-6030	29	11	with	with	ADP
fcis-6030	29	12	different	different	ADJ
fcis-6030	29	13	styles	style	NOUN
fcis-6030	29	14	.	.	PUNCT
fcis-6030	30	1	however	however	ADV
fcis-6030	30	2	,	,	PUNCT
fcis-6030	30	3	it	it	PRON
fcis-6030	30	4	is	be	AUX
fcis-6030	30	5	hard	hard	ADJ
fcis-6030	30	6	for	for	SCONJ
fcis-6030	30	7	the	the	DET
fcis-6030	30	8	public	public	NOUN
fcis-6030	30	9	to	to	PART
fcis-6030	30	10	find	find	VERB
fcis-6030	30	11	the	the	DET
fcis-6030	30	12	style	style	NOUN
fcis-6030	30	13	images	image	NOUN
fcis-6030	30	14	that	that	PRON
fcis-6030	30	15	exactly	exactly	ADV
fcis-6030	30	16	meet	meet	VERB
fcis-6030	30	17	their	their	PRON
fcis-6030	30	18	requirements	requirement	NOUN
fcis-6030	30	19	.	.	PUNCT
fcis-6030	31	1	compared	compare	VERB
fcis-6030	31	2	with	with	ADP
fcis-6030	31	3	style	style	NOUN
fcis-6030	31	4	images	image	NOUN
fcis-6030	31	5	,	,	PUNCT
fcis-6030	31	6	text	text	NOUN
fcis-6030	31	7	descriptions	description	NOUN
fcis-6030	31	8	are	be	AUX
fcis-6030	31	9	more	more	ADV
fcis-6030	31	10	obtainable	obtainable	ADJ
fcis-6030	31	11	heuristics	heuristic	NOUN
fcis-6030	31	12	.	.	PUNCT
fcis-6030	32	1	text	text	NOUN
fcis-6030	32	2	-	-	PUNCT
fcis-6030	32	3	to	to	ADP
fcis-6030	32	4	-	-	PUNCT
fcis-6030	32	5	art	art	NOUN
fcis-6030	32	6	synthesis	synthesis	NOUN
fcis-6030	32	7	,	,	PUNCT
fcis-6030	32	8	where	where	SCONJ
fcis-6030	32	9	ai	ai	AUX
fcis-6030	32	10	understands	understand	VERB
fcis-6030	32	11	art	art	NOUN
fcis-6030	32	12	by	by	ADP
fcis-6030	32	13	natural	natural	ADJ
fcis-6030	32	14	language	language	NOUN
fcis-6030	32	15	,	,	PUNCT
fcis-6030	32	16	can	can	AUX
fcis-6030	32	17	convert	convert	VERB
fcis-6030	32	18	abstract	abstract	ADJ
fcis-6030	32	19	art	art	NOUN
fcis-6030	32	20	words	word	NOUN
fcis-6030	32	21	into	into	ADP
fcis-6030	32	22	vivid	vivid	ADJ
fcis-6030	32	23	art	art	NOUN
fcis-6030	32	24	paintings	painting	NOUN
fcis-6030	32	25	.	.	PUNCT
fcis-6030	33	1	most	most	ADJ
fcis-6030	33	2	multimodal	multimodal	ADJ
fcis-6030	33	3	works	work	NOUN
fcis-6030	33	4	do	do	AUX
fcis-6030	33	5	not	not	PART
fcis-6030	33	6	treat	treat	VERB
fcis-6030	33	7	text	text	NOUN
fcis-6030	33	8	-	-	PUNCT
fcis-6030	33	9	to	to	ADP
fcis-6030	33	10	-	-	PUNCT
fcis-6030	33	11	art	art	NOUN
fcis-6030	33	12	as	as	ADP
fcis-6030	33	13	an	an	DET
fcis-6030	33	14	independent	independent	ADJ
fcis-6030	33	15	task	task	NOUN
fcis-6030	33	16	.	.	PUNCT
fcis-6030	34	1	for	for	ADP
fcis-6030	34	2	those	those	DET
fcis-6030	34	3	works	work	NOUN
fcis-6030	34	4	involving	involve	VERB
fcis-6030	34	5	art	art	NOUN
fcis-6030	34	6	synthesis	synthesis	NOUN
fcis-6030	34	7	,	,	PUNCT
fcis-6030	34	8	it	it	PRON
fcis-6030	34	9	only	only	ADV
fcis-6030	34	10	includes	include	VERB
fcis-6030	34	11	art	art	NOUN
fcis-6030	34	12	figures	figure	NOUN
fcis-6030	34	13	as	as	ADP
fcis-6030	34	14	part	part	NOUN
fcis-6030	34	15	of	of	ADP
fcis-6030	34	16	the	the	DET
fcis-6030	34	17	training	training	NOUN
fcis-6030	34	18	data	datum	NOUN
fcis-6030	34	19	,	,	PUNCT
fcis-6030	34	20	and	and	CCONJ
fcis-6030	34	21	mainly	mainly	ADV
fcis-6030	34	22	focuses	focus	VERB
fcis-6030	34	23	on	on	ADP
fcis-6030	34	24	fine	fine	ADJ
fcis-6030	34	25	art	art	NOUN
fcis-6030	34	26	.	.	PUNCT
fcis-6030	35	1	figure	figure	NOUN
fcis-6030	35	2	1	1	NUM
fcis-6030	35	3	.	.	PUNCT
fcis-6030	35	4	overall	overall	ADJ
fcis-6030	35	5	pipeline	pipeline	NOUN
fcis-6030	35	6	of	of	ADP
fcis-6030	35	7	our	our	PRON
fcis-6030	35	8	proposed	propose	VERB
fcis-6030	35	9	method	method	NOUN
fcis-6030	35	10	we	we	PRON
fcis-6030	35	11	use	use	VERB
fcis-6030	35	12	imagen	imagen	NOUN
fcis-6030	35	13	as	as	ADP
fcis-6030	35	14	our	our	PRON
fcis-6030	35	15	framwork	framwork	NOUN
fcis-6030	35	16	.	.	PUNCT
fcis-6030	36	1	a	a	DET
fcis-6030	36	2	text	text	NOUN
fcis-6030	36	3	description	description	NOUN
fcis-6030	36	4	is	be	AUX
fcis-6030	36	5	first	first	ADV
fcis-6030	36	6	encoded	encode	VERB
fcis-6030	36	7	to	to	ADP
fcis-6030	36	8	high	high	ADJ
fcis-6030	36	9	-	-	PUNCT
fcis-6030	36	10	level	level	NOUN
fcis-6030	36	11	features	feature	NOUN
fcis-6030	36	12	by	by	ADP
fcis-6030	36	13	t5	t5	PROPN
fcis-6030	36	14	encoder	encoder	NOUN
fcis-6030	36	15	.	.	PUNCT
fcis-6030	37	1	the	the	DET
fcis-6030	37	2	text	text	NOUN
fcis-6030	37	3	features	feature	NOUN
fcis-6030	37	4	are	be	AUX
fcis-6030	37	5	then	then	ADV
fcis-6030	37	6	fed	feed	VERB
fcis-6030	37	7	to	to	ADP
fcis-6030	37	8	the	the	DET
fcis-6030	37	9	two	two	NUM
fcis-6030	37	10	-	-	PUNCT
fcis-6030	37	11	diffusion	diffusion	NOUN
fcis-6030	37	12	model	model	NOUN
fcis-6030	37	13	to	to	PART
fcis-6030	37	14	guide	guide	VERB
fcis-6030	37	15	the	the	DET
fcis-6030	37	16	generation	generation	NOUN
fcis-6030	37	17	.	.	PUNCT
fcis-6030	38	1	in	in	ADP
fcis-6030	38	2	this	this	DET
fcis-6030	38	3	paper	paper	NOUN
fcis-6030	38	4	,	,	PUNCT
fcis-6030	38	5	we	we	PRON
fcis-6030	38	6	propose	propose	VERB
fcis-6030	38	7	text	text	NOUN
fcis-6030	38	8	-	-	PUNCT
fcis-6030	38	9	to	to	ADP
fcis-6030	38	10	-	-	PUNCT
fcis-6030	38	11	classic	classic	NOUN
fcis-6030	38	12	,	,	PUNCT
fcis-6030	38	13	which	which	PRON
fcis-6030	38	14	focuses	focus	VERB
fcis-6030	38	15	on	on	ADP
fcis-6030	38	16	multimodal	multimodal	ADJ
fcis-6030	38	17	classical	classical	ADJ
fcis-6030	38	18	art	art	NOUN
fcis-6030	38	19	painting	paint	VERB
fcis-6030	38	20	synthesis	synthesis	NOUN
fcis-6030	38	21	.	.	PUNCT
fcis-6030	39	1	we	we	PRON
fcis-6030	39	2	use	use	VERB
fcis-6030	39	3	imagen	imagen	NOUN
fcis-6030	39	4	[	[	X
fcis-6030	39	5	1	1	NUM
fcis-6030	39	6	]	]	PUNCT
fcis-6030	39	7	as	as	ADP
fcis-6030	39	8	our	our	PRON
fcis-6030	39	9	pipeline	pipeline	NOUN
fcis-6030	39	10	,	,	PUNCT
fcis-6030	39	11	and	and	CCONJ
fcis-6030	39	12	compress	compress	VERB
fcis-6030	39	13	the	the	DET
fcis-6030	39	14	network	network	NOUN
fcis-6030	39	15	into	into	ADP
fcis-6030	39	16	a	a	DET
fcis-6030	39	17	lightweight	lightweight	ADJ
fcis-6030	39	18	structure	structure	NOUN
fcis-6030	39	19	.	.	PUNCT
fcis-6030	40	1	experiments	experiment	NOUN
fcis-6030	40	2	show	show	VERB
fcis-6030	40	3	that	that	SCONJ
fcis-6030	40	4	the	the	DET
fcis-6030	40	5	compressed	compress	VERB
fcis-6030	40	6	pipeline	pipeline	NOUN
fcis-6030	40	7	can	can	AUX
fcis-6030	40	8	still	still	ADV
fcis-6030	40	9	achieve	achieve	VERB
fcis-6030	40	10	a	a	DET
fcis-6030	40	11	reasonable	reasonable	ADJ
fcis-6030	40	12	synthesis	synthesis	NOUN
fcis-6030	40	13	quality	quality	NOUN
fcis-6030	40	14	,	,	PUNCT
fcis-6030	40	15	but	but	CCONJ
fcis-6030	40	16	with	with	ADP
fcis-6030	40	17	a	a	DET
fcis-6030	40	18	much	much	ADV
fcis-6030	40	19	lower	low	ADJ
fcis-6030	40	20	number	number	NOUN
fcis-6030	40	21	of	of	ADP
fcis-6030	40	22	parameters	parameter	NOUN
fcis-6030	40	23	and	and	CCONJ
fcis-6030	40	24	computation	computation	NOUN
fcis-6030	40	25	cost	cost	NOUN
fcis-6030	40	26	.	.	PUNCT
fcis-6030	40	27	)	)	PUNCT
fcis-6030	41	1	in	in	ADP
fcis-6030	41	2	summary	summary	NOUN
fcis-6030	41	3	,	,	PUNCT
fcis-6030	41	4	our	our	PRON
fcis-6030	41	5	contributions	contribution	NOUN
fcis-6030	41	6	are	be	AUX
fcis-6030	41	7	:	:	PUNCT
fcis-6030	41	8	we	we	PRON
fcis-6030	41	9	propose	propose	VERB
fcis-6030	41	10	to	to	PART
fcis-6030	41	11	apply	apply	VERB
fcis-6030	41	12	diffusion	diffusion	NOUN
fcis-6030	41	13	models	model	NOUN
fcis-6030	41	14	in	in	ADP
fcis-6030	41	15	text	text	NOUN
fcis-6030	41	16	-	-	PUNCT
fcis-6030	41	17	to	to	ADP
fcis-6030	41	18	-	-	PUNCT
fcis-6030	41	19	classic	classic	ADJ
fcis-6030	41	20	image	image	NOUN
fcis-6030	41	21	synthesis	synthesis	NOUN
fcis-6030	41	22	task	task	NOUN
fcis-6030	41	23	,	,	PUNCT
fcis-6030	41	24	which	which	PRON
fcis-6030	41	25	is	be	AUX
fcis-6030	41	26	rarely	rarely	ADV
fcis-6030	41	27	discussed	discuss	VERB
fcis-6030	41	28	by	by	ADP
fcis-6030	41	29	previous	previous	ADJ
fcis-6030	41	30	research	research	NOUN
fcis-6030	41	31	.	.	PUNCT
fcis-6030	42	1	we	we	PRON
fcis-6030	42	2	compress	compress	VERB
fcis-6030	42	3	the	the	DET
fcis-6030	42	4	network	network	NOUN
fcis-6030	42	5	of	of	ADP
fcis-6030	42	6	imagen	imagen	NOUN
fcis-6030	42	7	and	and	CCONJ
fcis-6030	42	8	reduce	reduce	VERB
fcis-6030	42	9	computational	computational	ADJ
fcis-6030	42	10	costs	cost	NOUN
fcis-6030	42	11	.	.	PUNCT
fcis-6030	43	1	the	the	DET
fcis-6030	43	2	light	light	NOUN
fcis-6030	43	3	-	-	PUNCT
fcis-6030	43	4	weighted	weight	VERB
fcis-6030	43	5	network	network	NOUN
fcis-6030	43	6	is	be	AUX
fcis-6030	43	7	proved	prove	VERB
fcis-6030	43	8	to	to	PART
fcis-6030	43	9	be	be	AUX
fcis-6030	43	10	comparable	comparable	ADJ
fcis-6030	43	11	with	with	ADP
fcis-6030	43	12	the	the	DET
fcis-6030	43	13	original	original	ADJ
fcis-6030	43	14	heavy	heavy	ADJ
fcis-6030	43	15	-	-	PUNCT
fcis-6030	43	16	weight	weight	NOUN
fcis-6030	43	17	pipeline	pipeline	NOUN
fcis-6030	43	18	by	by	ADP
fcis-6030	43	19	experiments	experiment	NOUN
fcis-6030	43	20	.	.	PUNCT
fcis-6030	44	1	2	2	X
fcis-6030	44	2	.	.	NUM
fcis-6030	44	3	related	relate	VERB
fcis-6030	44	4	works	work	NOUN
fcis-6030	44	5	since	since	SCONJ
fcis-6030	44	6	few	few	ADJ
fcis-6030	44	7	researches	research	NOUN
fcis-6030	44	8	focus	focus	VERB
fcis-6030	44	9	on	on	ADP
fcis-6030	44	10	the	the	DET
fcis-6030	44	11	classic	classic	ADJ
fcis-6030	44	12	art	art	NOUN
fcis-6030	44	13	paintings	painting	NOUN
fcis-6030	44	14	synthesis	synthesis	NOUN
fcis-6030	44	15	task	task	NOUN
fcis-6030	44	16	,	,	PUNCT
fcis-6030	44	17	in	in	ADP
fcis-6030	44	18	this	this	DET
fcis-6030	44	19	section	section	NOUN
fcis-6030	44	20	,	,	PUNCT
fcis-6030	44	21	the	the	DET
fcis-6030	44	22	more	more	ADV
fcis-6030	44	23	general	general	ADJ
fcis-6030	44	24	task	task	NOUN
fcis-6030	44	25	of	of	ADP
fcis-6030	44	26	text	text	NOUN
fcis-6030	44	27	86	86	NUM
fcis-6030	44	28	to	to	PART
fcis-6030	44	29	-	-	PUNCT
fcis-6030	44	30	image	image	NOUN
fcis-6030	44	31	generation	generation	NOUN
fcis-6030	44	32	will	will	AUX
fcis-6030	44	33	be	be	AUX
fcis-6030	44	34	discussed	discuss	VERB
fcis-6030	44	35	.	.	PUNCT
fcis-6030	45	1	in	in	ADP
fcis-6030	45	2	this	this	DET
fcis-6030	45	3	task	task	NOUN
fcis-6030	45	4	,	,	PUNCT
fcis-6030	45	5	given	give	VERB
fcis-6030	45	6	a	a	DET
fcis-6030	45	7	text	text	NOUN
fcis-6030	45	8	description	description	NOUN
fcis-6030	45	9	,	,	PUNCT
fcis-6030	45	10	an	an	DET
fcis-6030	45	11	image	image	NOUN
fcis-6030	45	12	with	with	ADP
fcis-6030	45	13	high	high	ADJ
fcis-6030	45	14	fidelity	fidelity	NOUN
fcis-6030	45	15	should	should	AUX
fcis-6030	45	16	be	be	AUX
fcis-6030	45	17	generated	generate	VERB
fcis-6030	45	18	according	accord	VERB
fcis-6030	45	19	to	to	ADP
fcis-6030	45	20	the	the	DET
fcis-6030	45	21	description	description	NOUN
fcis-6030	45	22	.	.	PUNCT
fcis-6030	46	1	it	it	PRON
fcis-6030	46	2	is	be	AUX
fcis-6030	46	3	a	a	DET
fcis-6030	46	4	combination	combination	NOUN
fcis-6030	46	5	of	of	ADP
fcis-6030	46	6	cv	cv	PROPN
fcis-6030	46	7	and	and	CCONJ
fcis-6030	46	8	nlp	nlp	NOUN
fcis-6030	46	9	and	and	CCONJ
fcis-6030	46	10	has	have	AUX
fcis-6030	46	11	drawn	draw	VERB
fcis-6030	46	12	much	much	ADJ
fcis-6030	46	13	of	of	ADP
fcis-6030	46	14	the	the	DET
fcis-6030	46	15	researchers	researcher	NOUN
fcis-6030	46	16	’	’	PART
fcis-6030	46	17	attention	attention	NOUN
fcis-6030	46	18	.	.	PUNCT
fcis-6030	47	1	2.1	2.1	NUM
fcis-6030	47	2	.	.	PUNCT
fcis-6030	47	3	gan	gin	VERB
fcis-6030	47	4	-	-	PUNCT
fcis-6030	47	5	based	base	VERB
fcis-6030	47	6	generation	generation	NOUN
fcis-6030	47	7	gan	gin	VERB
fcis-6030	47	8	-	-	PUNCT
fcis-6030	47	9	int	int	NOUN
fcis-6030	47	10	-	-	PUNCT
fcis-6030	47	11	cls	cls	NOUN
fcis-6030	47	12	[	[	X
fcis-6030	47	13	2	2	NUM
fcis-6030	47	14	]	]	PUNCT
fcis-6030	47	15	is	be	AUX
fcis-6030	47	16	the	the	DET
fcis-6030	47	17	first	first	ADJ
fcis-6030	47	18	work	work	NOUN
fcis-6030	47	19	adopting	adopt	VERB
fcis-6030	47	20	a	a	DET
fcis-6030	47	21	conditional	conditional	ADJ
fcis-6030	47	22	generative	generative	ADJ
fcis-6030	47	23	adversarial	adversarial	ADJ
fcis-6030	47	24	network	network	NOUN
fcis-6030	47	25	for	for	ADP
fcis-6030	47	26	text	text	NOUN
fcis-6030	47	27	-	-	PUNCT
fcis-6030	47	28	to	to	ADP
fcis-6030	47	29	-	-	PUNCT
fcis-6030	47	30	image	image	NOUN
fcis-6030	47	31	generation	generation	NOUN
fcis-6030	47	32	.	.	PUNCT
fcis-6030	48	1	it	it	PRON
fcis-6030	48	2	uses	use	VERB
fcis-6030	48	3	a	a	DET
fcis-6030	48	4	deep	deep	ADJ
fcis-6030	48	5	convolutional	convolutional	ADJ
fcis-6030	48	6	-	-	PUNCT
fcis-6030	48	7	recurrent	recurrent	NOUN
fcis-6030	48	8	text	text	NOUN
fcis-6030	48	9	encoder	encoder	NOUN
fcis-6030	48	10	to	to	PART
fcis-6030	48	11	encode	encode	VERB
fcis-6030	48	12	the	the	DET
fcis-6030	48	13	input	input	NOUN
fcis-6030	48	14	text	text	NOUN
fcis-6030	48	15	into	into	ADP
fcis-6030	48	16	a	a	DET
fcis-6030	48	17	128	128	NUM
fcis-6030	48	18	-	-	PUNCT
fcis-6030	48	19	dimensional	dimensional	ADJ
fcis-6030	48	20	vector	vector	NOUN
fcis-6030	48	21	.	.	PUNCT
fcis-6030	49	1	the	the	DET
fcis-6030	49	2	text	text	NOUN
fcis-6030	49	3	vector	vector	NOUN
fcis-6030	49	4	is	be	AUX
fcis-6030	49	5	then	then	ADV
fcis-6030	49	6	concatenated	concatenate	VERB
fcis-6030	49	7	with	with	ADP
fcis-6030	49	8	the	the	DET
fcis-6030	49	9	noisy	noisy	ADJ
fcis-6030	49	10	latent	latent	NOUN
fcis-6030	49	11	code	code	NOUN
fcis-6030	49	12	as	as	ADP
fcis-6030	49	13	the	the	DET
fcis-6030	49	14	input	input	NOUN
fcis-6030	49	15	to	to	ADP
fcis-6030	49	16	the	the	DET
fcis-6030	49	17	generator	generator	NOUN
fcis-6030	49	18	.	.	PUNCT
fcis-6030	50	1	[	[	X
fcis-6030	50	2	3	3	X
fcis-6030	50	3	]	]	PUNCT
fcis-6030	50	4	combines	combine	VERB
fcis-6030	50	5	both	both	DET
fcis-6030	50	6	text	text	NOUN
fcis-6030	50	7	descriptions	description	NOUN
fcis-6030	50	8	and	and	CCONJ
fcis-6030	50	9	classification	classification	NOUN
fcis-6030	50	10	labels	label	NOUN
fcis-6030	50	11	to	to	PART
fcis-6030	50	12	guide	guide	VERB
fcis-6030	50	13	the	the	DET
fcis-6030	50	14	generation	generation	NOUN
fcis-6030	50	15	.	.	PUNCT
fcis-6030	51	1	[	[	X
fcis-6030	51	2	4	4	X
fcis-6030	51	3	]	]	PUNCT
fcis-6030	51	4	proposes	propose	VERB
fcis-6030	51	5	a	a	DET
fcis-6030	51	6	cascaded	cascade	VERB
fcis-6030	51	7	two	two	NUM
fcis-6030	51	8	-	-	PUNCT
fcis-6030	51	9	stage	stage	NOUN
fcis-6030	51	10	gan	gan	NOUN
fcis-6030	51	11	to	to	PART
fcis-6030	51	12	first	first	ADV
fcis-6030	51	13	generate	generate	VERB
fcis-6030	51	14	an	an	DET
fcis-6030	51	15	image	image	NOUN
fcis-6030	51	16	with	with	ADP
fcis-6030	51	17	low	low	ADJ
fcis-6030	51	18	resolution	resolution	NOUN
fcis-6030	51	19	and	and	CCONJ
fcis-6030	51	20	then	then	ADV
fcis-6030	51	21	conduct	conduct	VERB
fcis-6030	51	22	super	super	NOUN
fcis-6030	51	23	-	-	NOUN
fcis-6030	51	24	resolution	resolution	NOUN
fcis-6030	51	25	.	.	PUNCT
fcis-6030	52	1	[	[	X
fcis-6030	52	2	5	5	NUM
fcis-6030	52	3	]	]	PUNCT
fcis-6030	52	4	leverages	leverage	VERB
fcis-6030	52	5	a	a	DET
fcis-6030	52	6	mapping	mapping	NOUN
fcis-6030	52	7	network	network	NOUN
fcis-6030	52	8	and	and	CCONJ
fcis-6030	52	9	a	a	DET
fcis-6030	52	10	ternary	ternary	ADJ
fcis-6030	52	11	mutual	mutual	ADJ
fcis-6030	52	12	information	information	NOUN
fcis-6030	52	13	loss	loss	NOUN
fcis-6030	52	14	to	to	PART
fcis-6030	52	15	combine	combine	VERB
fcis-6030	52	16	text	text	NOUN
fcis-6030	52	17	and	and	CCONJ
fcis-6030	52	18	latent	latent	NOUN
fcis-6030	52	19	information	information	NOUN
fcis-6030	52	20	and	and	CCONJ
fcis-6030	52	21	improve	improve	VERB
fcis-6030	52	22	interpretability	interpretability	NOUN
fcis-6030	52	23	.	.	PUNCT
fcis-6030	53	1	2.2	2.2	NUM
fcis-6030	53	2	.	.	PUNCT
fcis-6030	53	3	diffusion	diffusion	NOUN
fcis-6030	53	4	-	-	PUNCT
fcis-6030	53	5	based	base	VERB
fcis-6030	53	6	generation	generation	NOUN
fcis-6030	53	7	ever	ever	ADV
fcis-6030	53	8	since	since	SCONJ
fcis-6030	53	9	proposed	propose	VERB
fcis-6030	53	10	by	by	ADP
fcis-6030	53	11	[	[	X
fcis-6030	53	12	6	6	NUM
fcis-6030	53	13	]	]	PUNCT
fcis-6030	53	14	,	,	PUNCT
fcis-6030	53	15	image	image	NOUN
fcis-6030	53	16	generation	generation	NOUN
fcis-6030	53	17	models	model	NOUN
fcis-6030	53	18	based	base	VERB
fcis-6030	53	19	on	on	ADP
fcis-6030	53	20	diffusion	diffusion	NOUN
fcis-6030	53	21	have	have	AUX
fcis-6030	53	22	achieved	achieve	VERB
fcis-6030	53	23	remarkable	remarkable	ADJ
fcis-6030	53	24	performance	performance	NOUN
fcis-6030	53	25	.	.	PUNCT
fcis-6030	54	1	similar	similar	ADJ
fcis-6030	54	2	to	to	ADP
fcis-6030	54	3	gan	gan	VERB
fcis-6030	54	4	-	-	PUNCT
fcis-6030	54	5	based	base	VERB
fcis-6030	54	6	text	text	NOUN
fcis-6030	54	7	-	-	PUNCT
fcis-6030	54	8	to	to	ADP
fcis-6030	54	9	-	-	PUNCT
fcis-6030	54	10	image	image	NOUN
fcis-6030	54	11	synthesis	synthesis	NOUN
fcis-6030	54	12	methods	method	NOUN
fcis-6030	54	13	,	,	PUNCT
fcis-6030	54	14	diffusionbased	diffusionbase	VERB
fcis-6030	54	15	methods	method	NOUN
fcis-6030	54	16	use	use	VERB
fcis-6030	54	17	a	a	DET
fcis-6030	54	18	natural	natural	ADJ
fcis-6030	54	19	language	language	NOUN
fcis-6030	54	20	processing	processing	NOUN
fcis-6030	54	21	model	model	NOUN
fcis-6030	54	22	to	to	PART
fcis-6030	54	23	encode	encode	VERB
fcis-6030	54	24	text	text	NOUN
fcis-6030	54	25	information	information	NOUN
fcis-6030	54	26	,	,	PUNCT
fcis-6030	54	27	and	and	CCONJ
fcis-6030	54	28	the	the	DET
fcis-6030	54	29	difference	difference	NOUN
fcis-6030	54	30	lies	lie	VERB
fcis-6030	54	31	in	in	ADP
fcis-6030	54	32	the	the	DET
fcis-6030	54	33	decoder	decoder	NOUN
fcis-6030	54	34	.	.	PUNCT
fcis-6030	55	1	instead	instead	ADV
fcis-6030	55	2	of	of	ADP
fcis-6030	55	3	using	use	VERB
fcis-6030	55	4	gan	gan	NOUN
fcis-6030	55	5	,	,	PUNCT
fcis-6030	55	6	diffusion	diffusion	NOUN
fcis-6030	55	7	-	-	PUNCT
fcis-6030	55	8	based	base	VERB
fcis-6030	55	9	methods	method	NOUN
fcis-6030	55	10	exploit	exploit	VERB
fcis-6030	55	11	the	the	DET
fcis-6030	55	12	denoising	denoising	NOUN
fcis-6030	55	13	diffusion	diffusion	NOUN
fcis-6030	55	14	probabilistic	probabilistic	ADJ
fcis-6030	55	15	method	method	NOUN
fcis-6030	55	16	(	(	PUNCT
fcis-6030	55	17	ddpm	ddpm	PROPN
fcis-6030	55	18	)	)	PUNCT
fcis-6030	55	19	to	to	PART
fcis-6030	55	20	sample	sample	NOUN
fcis-6030	55	21	images	image	NOUN
fcis-6030	55	22	.	.	PUNCT
fcis-6030	56	1	following	follow	VERB
fcis-6030	56	2	[	[	X
fcis-6030	56	3	6	6	NUM
fcis-6030	56	4	]	]	PUNCT
fcis-6030	56	5	,	,	PUNCT
fcis-6030	56	6	most	most	ADJ
fcis-6030	56	7	diffusion	diffusion	NOUN
fcis-6030	56	8	models	model	NOUN
fcis-6030	56	9	are	be	AUX
fcis-6030	56	10	based	base	VERB
fcis-6030	56	11	on	on	ADP
fcis-6030	56	12	the	the	DET
fcis-6030	56	13	unet	unet	NOUN
fcis-6030	56	14	structure	structure	NOUN
fcis-6030	56	15	.	.	PUNCT
fcis-6030	57	1	[	[	X
fcis-6030	57	2	7	7	X
fcis-6030	57	3	]	]	PUNCT
fcis-6030	57	4	provides	provide	VERB
fcis-6030	57	5	classification	classification	NOUN
fcis-6030	57	6	labels	label	NOUN
fcis-6030	57	7	to	to	ADP
fcis-6030	57	8	the	the	DET
fcis-6030	57	9	diffusion	diffusion	NOUN
fcis-6030	57	10	model	model	NOUN
fcis-6030	57	11	,	,	PUNCT
fcis-6030	57	12	and	and	CCONJ
fcis-6030	57	13	proves	prove	VERB
fcis-6030	57	14	that	that	SCONJ
fcis-6030	57	15	diffusion	diffusion	NOUN
fcis-6030	57	16	models	model	NOUN
fcis-6030	57	17	outperform	outperform	VERB
fcis-6030	57	18	traditional	traditional	ADJ
fcis-6030	57	19	gan	gan	NOUN
fcis-6030	57	20	models	model	NOUN
fcis-6030	57	21	by	by	ADP
fcis-6030	57	22	experiments	experiment	NOUN
fcis-6030	57	23	.	.	PUNCT
fcis-6030	58	1	[	[	X
fcis-6030	58	2	8	8	NUM
fcis-6030	58	3	]	]	PUNCT
fcis-6030	58	4	adopts	adopt	VERB
fcis-6030	58	5	a	a	DET
fcis-6030	58	6	cascaded	cascade	VERB
fcis-6030	58	7	structure	structure	NOUN
fcis-6030	58	8	with	with	ADP
fcis-6030	58	9	a	a	DET
fcis-6030	58	10	low	low	ADJ
fcis-6030	58	11	-	-	PUNCT
fcis-6030	58	12	resolution	resolution	NOUN
fcis-6030	58	13	base	base	NOUN
fcis-6030	58	14	diffusion	diffusion	NOUN
fcis-6030	58	15	generator	generator	NOUN
fcis-6030	58	16	followed	follow	VERB
fcis-6030	58	17	by	by	ADP
fcis-6030	58	18	several	several	ADJ
fcis-6030	58	19	super	super	ADJ
fcis-6030	58	20	-	-	ADJ
fcis-6030	58	21	resolution	resolution	ADJ
fcis-6030	58	22	diffusion	diffusion	NOUN
fcis-6030	58	23	models	model	NOUN
fcis-6030	58	24	.	.	PUNCT
fcis-6030	59	1	similar	similar	ADJ
fcis-6030	59	2	to	to	ADP
fcis-6030	59	3	[	[	X
fcis-6030	59	4	6	6	NUM
fcis-6030	59	5	]	]	PUNCT
fcis-6030	59	6	,	,	PUNCT
fcis-6030	59	7	the	the	DET
fcis-6030	59	8	work	work	NOUN
fcis-6030	59	9	of	of	ADP
fcis-6030	59	10	[	[	X
fcis-6030	59	11	8	8	NUM
fcis-6030	59	12	]	]	PUNCT
fcis-6030	59	13	utilizes	utilize	VERB
fcis-6030	59	14	classification	classification	NOUN
fcis-6030	59	15	labels	label	NOUN
fcis-6030	59	16	as	as	ADP
fcis-6030	59	17	conditions	condition	NOUN
fcis-6030	59	18	to	to	ADP
fcis-6030	59	19	the	the	DET
fcis-6030	59	20	network	network	NOUN
fcis-6030	59	21	.	.	PUNCT
fcis-6030	60	1	[	[	X
fcis-6030	60	2	9	9	NUM
fcis-6030	60	3	]	]	PUNCT
fcis-6030	60	4	proposes	propose	VERB
fcis-6030	60	5	to	to	PART
fcis-6030	60	6	use	use	VERB
fcis-6030	60	7	texts	text	NOUN
fcis-6030	60	8	for	for	ADP
fcis-6030	60	9	conditioning	conditioning	NOUN
fcis-6030	60	10	,	,	PUNCT
fcis-6030	60	11	aiming	aim	VERB
fcis-6030	60	12	at	at	ADP
fcis-6030	60	13	the	the	DET
fcis-6030	60	14	text	text	NOUN
fcis-6030	60	15	-	-	PUNCT
fcis-6030	60	16	to	to	ADP
fcis-6030	60	17	-	-	PUNCT
fcis-6030	60	18	image	image	NOUN
fcis-6030	60	19	generation	generation	NOUN
fcis-6030	60	20	task	task	NOUN
fcis-6030	60	21	.	.	PUNCT
fcis-6030	61	1	[	[	X
fcis-6030	61	2	1	1	X
fcis-6030	61	3	]	]	PUNCT
fcis-6030	61	4	achieves	achieve	VERB
fcis-6030	61	5	the	the	DET
fcis-6030	61	6	state	state	NOUN
fcis-6030	61	7	-	-	PUNCT
fcis-6030	61	8	of	of	ADP
fcis-6030	61	9	-	-	PUNCT
fcis-6030	61	10	the	the	DET
fcis-6030	61	11	-	-	PUNCT
fcis-6030	61	12	art	art	NOUN
fcis-6030	61	13	performance	performance	NOUN
fcis-6030	61	14	in	in	ADP
fcis-6030	61	15	text	text	NOUN
fcis-6030	61	16	-	-	PUNCT
fcis-6030	61	17	to	to	ADP
fcis-6030	61	18	-	-	PUNCT
fcis-6030	61	19	image	image	NOUN
fcis-6030	61	20	synthesis	synthesis	NOUN
fcis-6030	61	21	.	.	PUNCT
fcis-6030	62	1	therefore	therefore	ADV
fcis-6030	62	2	,	,	PUNCT
fcis-6030	62	3	in	in	ADP
fcis-6030	62	4	this	this	DET
fcis-6030	62	5	paper	paper	NOUN
fcis-6030	62	6	,	,	PUNCT
fcis-6030	62	7	we	we	PRON
fcis-6030	62	8	use	use	VERB
fcis-6030	62	9	imagen	imagen	NOUN
fcis-6030	62	10	proposed	propose	VERB
fcis-6030	62	11	by	by	ADP
fcis-6030	62	12	[	[	X
fcis-6030	62	13	1	1	NUM
fcis-6030	62	14	]	]	PUNCT
fcis-6030	62	15	as	as	ADP
fcis-6030	62	16	our	our	PRON
fcis-6030	62	17	framework	framework	NOUN
fcis-6030	62	18	to	to	PART
fcis-6030	62	19	solve	solve	VERB
fcis-6030	62	20	the	the	DET
fcis-6030	62	21	text	text	NOUN
fcis-6030	62	22	-	-	PUNCT
fcis-6030	62	23	to	to	ADP
fcis-6030	62	24	-	-	PUNCT
fcis-6030	62	25	classic	classic	ADJ
fcis-6030	62	26	problem	problem	NOUN
fcis-6030	62	27	.	.	PUNCT
fcis-6030	63	1	however	however	ADV
fcis-6030	63	2	,	,	PUNCT
fcis-6030	63	3	the	the	DET
fcis-6030	63	4	origin	origin	NOUN
fcis-6030	63	5	imagen	imagen	NOUN
fcis-6030	63	6	is	be	AUX
fcis-6030	63	7	a	a	DET
fcis-6030	63	8	heavy	heavy	ADJ
fcis-6030	63	9	-	-	PUNCT
fcis-6030	63	10	weighted	weight	VERB
fcis-6030	63	11	network	network	NOUN
fcis-6030	63	12	,	,	PUNCT
fcis-6030	63	13	with	with	ADP
fcis-6030	63	14	high	high	ADJ
fcis-6030	63	15	computational	computational	ADJ
fcis-6030	63	16	costs	cost	NOUN
fcis-6030	63	17	in	in	ADP
fcis-6030	63	18	training	training	NOUN
fcis-6030	63	19	and	and	CCONJ
fcis-6030	63	20	sampling	sampling	NOUN
fcis-6030	63	21	.	.	PUNCT
fcis-6030	64	1	therefore	therefore	ADV
fcis-6030	64	2	,	,	PUNCT
fcis-6030	64	3	we	we	PRON
fcis-6030	64	4	compress	compress	VERB
fcis-6030	64	5	the	the	DET
fcis-6030	64	6	network	network	NOUN
fcis-6030	64	7	,	,	PUNCT
fcis-6030	64	8	and	and	CCONJ
fcis-6030	64	9	experiment	experiment	NOUN
fcis-6030	64	10	results	result	NOUN
fcis-6030	64	11	show	show	VERB
fcis-6030	64	12	that	that	SCONJ
fcis-6030	64	13	the	the	DET
fcis-6030	64	14	light	light	ADJ
fcis-6030	64	15	-	-	PUNCT
fcis-6030	64	16	weighted	weight	VERB
fcis-6030	64	17	network	network	NOUN
fcis-6030	64	18	has	have	VERB
fcis-6030	64	19	a	a	DET
fcis-6030	64	20	comparable	comparable	ADJ
fcis-6030	64	21	performance	performance	NOUN
fcis-6030	64	22	with	with	ADP
fcis-6030	64	23	the	the	DET
fcis-6030	64	24	original	original	ADJ
fcis-6030	64	25	one	one	NUM
fcis-6030	64	26	.	.	PUNCT
fcis-6030	65	1	3	3	X
fcis-6030	65	2	.	.	X
fcis-6030	65	3	methodology	methodology	NOUN
fcis-6030	65	4	to	to	PART
fcis-6030	65	5	solve	solve	VERB
fcis-6030	65	6	the	the	DET
fcis-6030	65	7	classical	classical	ADJ
fcis-6030	65	8	image	image	NOUN
fcis-6030	65	9	generation	generation	NOUN
fcis-6030	65	10	problem	problem	NOUN
fcis-6030	66	1	,	,	PUNCT
fcis-6030	66	2	we	we	PRON
fcis-6030	66	3	propose	propose	VERB
fcis-6030	66	4	text	text	NOUN
fcis-6030	66	5	-	-	PUNCT
fcis-6030	66	6	to	to	ADP
fcis-6030	66	7	-	-	PUNCT
fcis-6030	66	8	classic(t2c	classic(t2c	PROPN
fcis-6030	66	9	)	)	PUNCT
fcis-6030	66	10	,	,	PUNCT
fcis-6030	66	11	a	a	DET
fcis-6030	66	12	lightweight	lightweight	ADJ
fcis-6030	66	13	text	text	NOUN
fcis-6030	66	14	-	-	PUNCT
fcis-6030	66	15	to	to	ADP
fcis-6030	66	16	-	-	PUNCT
fcis-6030	66	17	image	image	NOUN
fcis-6030	66	18	generator	generator	NOUN
fcis-6030	66	19	based	base	VERB
fcis-6030	66	20	on	on	ADP
fcis-6030	66	21	the	the	DET
fcis-6030	66	22	diffusion	diffusion	NOUN
fcis-6030	66	23	model	model	NOUN
fcis-6030	66	24	.	.	PUNCT
fcis-6030	67	1	we	we	PRON
fcis-6030	67	2	leverage	leverage	VERB
fcis-6030	67	3	imagen	imagen	NOUN
fcis-6030	67	4	[	[	X
fcis-6030	67	5	1	1	NUM
fcis-6030	67	6	]	]	PUNCT
fcis-6030	67	7	as	as	ADP
fcis-6030	67	8	the	the	DET
fcis-6030	67	9	framework	framework	NOUN
fcis-6030	67	10	for	for	ADP
fcis-6030	67	11	text	text	NOUN
fcis-6030	67	12	-	-	PUNCT
fcis-6030	67	13	to	to	ADP
fcis-6030	67	14	-	-	PUNCT
fcis-6030	67	15	classic	classic	ADJ
fcis-6030	67	16	image	image	NOUN
fcis-6030	67	17	synthesis	synthesis	NOUN
fcis-6030	67	18	.	.	PUNCT
fcis-6030	68	1	imagen	imagen	PROPN
fcis-6030	68	2	is	be	AUX
fcis-6030	68	3	a	a	DET
fcis-6030	68	4	text	text	NOUN
fcis-6030	68	5	-	-	PUNCT
fcis-6030	68	6	to	to	ADP
fcis-6030	68	7	-	-	PUNCT
fcis-6030	68	8	image	image	NOUN
fcis-6030	68	9	diffusion	diffusion	NOUN
fcis-6030	68	10	model	model	NOUN
fcis-6030	68	11	consisting	consist	VERB
fcis-6030	68	12	of	of	ADP
fcis-6030	68	13	a	a	DET
fcis-6030	68	14	frozen	frozen	ADJ
fcis-6030	68	15	generic	generic	ADJ
fcis-6030	68	16	large	large	ADJ
fcis-6030	68	17	language	language	NOUN
fcis-6030	68	18	model	model	NOUN
fcis-6030	68	19	as	as	ADP
fcis-6030	68	20	the	the	DET
fcis-6030	68	21	encoder	encoder	NOUN
fcis-6030	68	22	and	and	CCONJ
fcis-6030	68	23	a	a	DET
fcis-6030	68	24	cascaded	cascade	VERB
fcis-6030	68	25	diffusion	diffusion	NOUN
fcis-6030	68	26	model	model	NOUN
fcis-6030	68	27	as	as	ADP
fcis-6030	68	28	the	the	DET
fcis-6030	68	29	decoder	decoder	NOUN
fcis-6030	68	30	.	.	PUNCT
fcis-6030	69	1	3.1	3.1	NUM
fcis-6030	69	2	.	.	NUM
fcis-6030	69	3	pretrained	pretraine	VERB
fcis-6030	69	4	text	text	NOUN
fcis-6030	69	5	encoder	encoder	NOUN
fcis-6030	69	6	text	text	NOUN
fcis-6030	69	7	encoder	encoder	NOUN
fcis-6030	69	8	can	can	AUX
fcis-6030	69	9	be	be	AUX
fcis-6030	69	10	trained	train	VERB
fcis-6030	69	11	on	on	ADP
fcis-6030	69	12	pure	pure	ADJ
fcis-6030	69	13	text	text	NOUN
fcis-6030	69	14	data	datum	NOUN
fcis-6030	69	15	or	or	CCONJ
fcis-6030	69	16	image	image	NOUN
fcis-6030	69	17	-	-	PUNCT
fcis-6030	69	18	text	text	NOUN
fcis-6030	69	19	pairs	pair	NOUN
fcis-6030	69	20	.	.	PUNCT
fcis-6030	70	1	while	while	SCONJ
fcis-6030	70	2	nlp	nlp	NOUN
fcis-6030	70	3	models	model	NOUN
fcis-6030	70	4	(	(	PUNCT
fcis-6030	70	5	e.g.	e.g.	ADV
fcis-6030	70	6	clip	clip	NOUN
fcis-6030	70	7	)	)	PUNCT
fcis-6030	70	8	trained	train	VERB
fcis-6030	70	9	on	on	ADP
fcis-6030	70	10	text	text	NOUN
fcis-6030	70	11	-	-	PUNCT
fcis-6030	70	12	imagepair	imagepair	NOUN
fcis-6030	70	13	can	can	AUX
fcis-6030	70	14	extract	extract	VERB
fcis-6030	70	15	vision	vision	NOUN
fcis-6030	70	16	-	-	PUNCT
fcis-6030	70	17	language	language	NOUN
fcis-6030	70	18	correspondence	correspondence	NOUN
fcis-6030	70	19	,	,	PUNCT
fcis-6030	70	20	which	which	PRON
fcis-6030	70	21	is	be	AUX
fcis-6030	70	22	beneficial	beneficial	ADJ
fcis-6030	70	23	to	to	ADP
fcis-6030	70	24	text	text	NOUN
fcis-6030	70	25	-	-	PUNCT
fcis-6030	70	26	to	to	ADP
fcis-6030	70	27	-	-	PUNCT
fcis-6030	70	28	image	image	NOUN
fcis-6030	70	29	synthesis	synthesis	NOUN
fcis-6030	70	30	,	,	PUNCT
fcis-6030	70	31	image	image	NOUN
fcis-6030	70	32	-	-	PUNCT
fcis-6030	70	33	text	text	NOUN
fcis-6030	70	34	datasets	dataset	NOUN
fcis-6030	70	35	are	be	AUX
fcis-6030	70	36	always	always	ADV
fcis-6030	70	37	much	much	ADV
fcis-6030	70	38	smaller	small	ADJ
fcis-6030	70	39	compared	compare	VERB
fcis-6030	70	40	with	with	ADP
fcis-6030	70	41	pure	pure	ADJ
fcis-6030	70	42	text	text	NOUN
fcis-6030	70	43	datasets	dataset	NOUN
fcis-6030	70	44	.	.	PUNCT
fcis-6030	71	1	alex	alex	PROPN
fcis-6030	71	2	et	et	PROPN
fcis-6030	71	3	al	al	PROPN
fcis-6030	72	1	[	[	X
fcis-6030	72	2	9	9	NUM
fcis-6030	72	3	]	]	PUNCT
fcis-6030	72	4	.	.	PUNCT
fcis-6030	73	1	compares	compare	VERB
fcis-6030	73	2	different	different	ADJ
fcis-6030	73	3	language	language	NOUN
fcis-6030	73	4	encoders	encoder	NOUN
fcis-6030	73	5	and	and	CCONJ
fcis-6030	73	6	found	find	VERB
fcis-6030	73	7	that	that	SCONJ
fcis-6030	73	8	models	model	NOUN
fcis-6030	73	9	trained	train	VERB
fcis-6030	73	10	on	on	ADP
fcis-6030	73	11	large	large	ADJ
fcis-6030	73	12	pure	pure	ADJ
fcis-6030	73	13	text	text	NOUN
fcis-6030	73	14	datasets	dataset	NOUN
fcis-6030	73	15	outperform	outperform	VERB
fcis-6030	73	16	those	those	PRON
fcis-6030	73	17	trained	train	VERB
fcis-6030	73	18	on	on	ADP
fcis-6030	73	19	text	text	NOUN
fcis-6030	73	20	-	-	PUNCT
fcis-6030	73	21	image	image	NOUN
fcis-6030	73	22	pairs	pair	NOUN
fcis-6030	73	23	in	in	ADP
fcis-6030	73	24	terms	term	NOUN
fcis-6030	73	25	of	of	ADP
fcis-6030	73	26	sample	sample	NOUN
fcis-6030	73	27	quality	quality	NOUN
fcis-6030	73	28	.	.	PUNCT
fcis-6030	74	1	specifically	specifically	ADV
fcis-6030	74	2	,	,	PUNCT
fcis-6030	74	3	t5	t5	PROPN
fcis-6030	74	4	model	model	NOUN
fcis-6030	74	5	is	be	AUX
fcis-6030	74	6	experimentally	experimentally	ADV
fcis-6030	74	7	proven	prove	VERB
fcis-6030	74	8	to	to	PART
fcis-6030	74	9	be	be	AUX
fcis-6030	74	10	the	the	DET
fcis-6030	74	11	most	most	ADV
fcis-6030	74	12	efficient	efficient	ADJ
fcis-6030	74	13	text	text	NOUN
fcis-6030	74	14	encoder	encoder	NOUN
fcis-6030	74	15	.	.	PUNCT
fcis-6030	75	1	text	text	NOUN
fcis-6030	75	2	-	-	PUNCT
fcis-6030	75	3	to	to	ADP
fcis-6030	75	4	-	-	PUNCT
fcis-6030	75	5	text	text	NOUN
fcis-6030	75	6	transfer	transfer	NOUN
fcis-6030	75	7	transformer(t5	transformer(t5	NOUN
fcis-6030	75	8	)	)	PUNCT
fcis-6030	75	9	is	be	AUX
fcis-6030	75	10	a	a	DET
fcis-6030	75	11	transformer	transformer	ADJ
fcis-6030	75	12	model	model	NOUN
fcis-6030	75	13	following	follow	VERB
fcis-6030	75	14	the	the	DET
fcis-6030	75	15	classical	classical	ADJ
fcis-6030	75	16	encoder	encoder	NOUN
fcis-6030	75	17	-	-	PUNCT
fcis-6030	75	18	decoder	decoder	NOUN
fcis-6030	75	19	structure	structure	NOUN
fcis-6030	75	20	proposed	propose	VERB
fcis-6030	75	21	by	by	ADP
fcis-6030	75	22	[	[	X
fcis-6030	75	23	10	10	NUM
fcis-6030	75	24	]	]	PUNCT
fcis-6030	75	25	and	and	CCONJ
fcis-6030	75	26	is	be	AUX
fcis-6030	75	27	trained	train	VERB
fcis-6030	75	28	on	on	ADP
fcis-6030	75	29	their	their	PRON
fcis-6030	75	30	colossal	colossal	ADJ
fcis-6030	75	31	clean	clean	NOUN
fcis-6030	75	32	crawled	crawl	VERB
fcis-6030	75	33	corpus(c4	corpus(c4	NOUN
fcis-6030	75	34	)	)	PUNCT
fcis-6030	75	35	dataset	dataset	NOUN
fcis-6030	75	36	.	.	PUNCT
fcis-6030	76	1	in	in	ADP
fcis-6030	76	2	the	the	DET
fcis-6030	76	3	rest	rest	NOUN
fcis-6030	76	4	of	of	ADP
fcis-6030	76	5	this	this	DET
fcis-6030	76	6	section	section	NOUN
fcis-6030	76	7	,	,	PUNCT
fcis-6030	76	8	the	the	DET
fcis-6030	76	9	encoder	encoder	NOUN
fcis-6030	76	10	-	-	PUNCT
fcis-6030	76	11	decoder	decoder	NOUN
fcis-6030	76	12	transformer	transformer	NOUN
fcis-6030	76	13	structure	structure	NOUN
fcis-6030	76	14	will	will	AUX
fcis-6030	76	15	be	be	AUX
fcis-6030	76	16	briefly	briefly	ADV
fcis-6030	76	17	introduced	introduce	VERB
fcis-6030	76	18	.	.	PUNCT
fcis-6030	77	1	a	a	DET
fcis-6030	77	2	)	)	PUNCT
fcis-6030	77	3	self	self	NOUN
fcis-6030	77	4	-	-	PUNCT
fcis-6030	77	5	attention	attention	NOUN
fcis-6030	77	6	mechanism	mechanism	NOUN
fcis-6030	77	7	:	:	PUNCT
fcis-6030	77	8	given	give	VERB
fcis-6030	77	9	an	an	DET
fcis-6030	77	10	input	input	NOUN
fcis-6030	77	11	sequence	sequence	NOUN
fcis-6030	77	12	x	x	NOUN
fcis-6030	77	13	=	=	SYM
fcis-6030	77	14	x1	x1	PROPN
fcis-6030	77	15	,	,	PUNCT
fcis-6030	77	16	x2	x2	PROPN
fcis-6030	77	17	,	,	PUNCT
fcis-6030	77	18	...	...	PUNCT
fcis-6030	77	19	,	,	PUNCT
fcis-6030	77	20	xt	xt	ADP
fcis-6030	77	21	,	,	PUNCT
fcis-6030	77	22	a	a	DET
fcis-6030	77	23	self	self	NOUN
fcis-6030	77	24	-	-	PUNCT
fcis-6030	77	25	attention	attention	NOUN
fcis-6030	77	26	function	function	NOUN
fcis-6030	77	27	first	first	ADV
fcis-6030	77	28	maps	map	VERB
fcis-6030	77	29	each	each	DET
fcis-6030	77	30	element	element	NOUN
fcis-6030	77	31	xi	xi	ADP
fcis-6030	77	32	of	of	ADP
fcis-6030	77	33	the	the	DET
fcis-6030	77	34	sequence	sequence	NOUN
fcis-6030	77	35	into	into	ADP
fcis-6030	77	36	three	three	NUM
fcis-6030	77	37	feature	feature	NOUN
fcis-6030	77	38	vectors	vector	NOUN
fcis-6030	77	39	:	:	PUNCT
fcis-6030	77	40	query	query	PROPN
fcis-6030	77	41	qi	qi	PROPN
fcis-6030	77	42	,	,	PUNCT
fcis-6030	77	43	key	key	ADJ
fcis-6030	77	44	ki	ki	PROPN
fcis-6030	77	45	and	and	CCONJ
fcis-6030	77	46	value	value	PROPN
fcis-6030	77	47	vi	vi	PROPN
fcis-6030	77	48	.	.	PUNCT
fcis-6030	78	1	qi	qi	PROPN
fcis-6030	79	1	=	=	SYM
fcis-6030	79	2	wq	wq	PROPN
fcis-6030	79	3	xi	xi	X
fcis-6030	79	4	(	(	PUNCT
fcis-6030	79	5	1	1	X
fcis-6030	79	6	)	)	PUNCT
fcis-6030	79	7	ki	ki	PROPN
fcis-6030	80	1	=	=	PUNCT
fcis-6030	80	2	wk	wk	INTJ
fcis-6030	80	3	xi	xi	X
fcis-6030	80	4	(	(	PUNCT
fcis-6030	80	5	2	2	NUM
fcis-6030	80	6	)	)	PUNCT
fcis-6030	80	7	vi	vi	NOUN
fcis-6030	80	8	=	=	SYM
fcis-6030	80	9	wv	wv	PROPN
fcis-6030	80	10	xi	xi	X
fcis-6030	80	11	(	(	PUNCT
fcis-6030	80	12	3	3	X
fcis-6030	80	13	)	)	PUNCT
fcis-6030	80	14	the	the	DET
fcis-6030	80	15	output	output	NOUN
fcis-6030	80	16	sequence	sequence	NOUN
fcis-6030	80	17	y	y	PROPN
fcis-6030	80	18	=	=	SYM
fcis-6030	80	19	{	{	PUNCT
fcis-6030	80	20	y1	y1	PROPN
fcis-6030	80	21	,	,	PUNCT
fcis-6030	80	22	y2	y2	PROPN
fcis-6030	80	23	,	,	PUNCT
fcis-6030	80	24	...	...	PUNCT
fcis-6030	80	25	,	,	PUNCT
fcis-6030	80	26	yt	yt	NOUN
fcis-6030	80	27	}	}	PUNCT
fcis-6030	80	28	of	of	ADP
fcis-6030	80	29	the	the	DET
fcis-6030	80	30	selfattention	selfattention	NOUN
fcis-6030	80	31	function	function	NOUN
fcis-6030	80	32	has	have	VERB
fcis-6030	80	33	the	the	DET
fcis-6030	80	34	same	same	ADJ
fcis-6030	80	35	length	length	NOUN
fcis-6030	80	36	as	as	ADP
fcis-6030	80	37	the	the	DET
fcis-6030	80	38	input	input	NOUN
fcis-6030	80	39	.	.	PUNCT
fcis-6030	81	1	each	each	DET
fcis-6030	81	2	element	element	NOUN
fcis-6030	81	3	in	in	ADP
fcis-6030	81	4	the	the	DET
fcis-6030	81	5	output	output	NOUN
fcis-6030	81	6	sequence	sequence	NOUN
fcis-6030	81	7	is	be	AUX
fcis-6030	81	8	computed	compute	VERB
fcis-6030	81	9	as	as	ADP
fcis-6030	81	10	the	the	DET
fcis-6030	81	11	weighted	weighted	ADJ
fcis-6030	81	12	sum	sum	NOUN
fcis-6030	81	13	of	of	ADP
fcis-6030	81	14	all	all	DET
fcis-6030	81	15	values	value	NOUN
fcis-6030	81	16	vi	vi	PROPN
fcis-6030	81	17	,	,	PUNCT
fcis-6030	81	18	and	and	CCONJ
fcis-6030	81	19	the	the	DET
fcis-6030	81	20	weights	weight	NOUN
fcis-6030	81	21	are	be	AUX
fcis-6030	81	22	calculated	calculate	VERB
fcis-6030	81	23	by	by	ADP
fcis-6030	81	24	quering	quere	VERB
fcis-6030	81	25	all	all	DET
fcis-6030	81	26	keys	key	NOUN
fcis-6030	81	27	:	:	PUNCT
fcis-6030	82	1	𝑦𝑖	𝑦𝑖	NUM
fcis-6030	82	2	=	=	PUNCT
fcis-6030	82	3	∑	∑	PUNCT
fcis-6030	82	4	𝛼𝑖𝑗𝑣𝑗	𝛼𝑖𝑗𝑣𝑗	PROPN
fcis-6030	82	5	𝑡	𝑡	X
fcis-6030	82	6	𝑗=1	𝑗=1	X
fcis-6030	82	7	(	(	PUNCT
fcis-6030	82	8	4	4	NUM
fcis-6030	82	9	)	)	PUNCT
fcis-6030	82	10	where	where	SCONJ
fcis-6030	82	11	weights	weight	NOUN
fcis-6030	82	12	αij	αij	NOUN
fcis-6030	82	13	can	can	AUX
fcis-6030	82	14	be	be	AUX
fcis-6030	82	15	computed	compute	VERB
fcis-6030	82	16	by	by	ADP
fcis-6030	82	17	:	:	PUNCT
fcis-6030	82	18	αij	αij	NOUN
fcis-6030	82	19	=	=	PROPN
fcis-6030	82	20	softmax(eij	softmax(eij	PROPN
fcis-6030	82	21	)	)	PUNCT
fcis-6030	82	22	=	=	SYM
fcis-6030	82	23	exp(eij	exp(eij	PROPN
fcis-6030	82	24	)	)	PUNCT
fcis-6030	82	25	∑	∑	ADV
fcis-6030	82	26	exp(eik)𝑡	exp(eik)𝑡	NOUN
fcis-6030	82	27	𝑘=1	𝑘=1	X
fcis-6030	82	28	(	(	PUNCT
fcis-6030	82	29	5	5	NUM
fcis-6030	82	30	)	)	PUNCT
fcis-6030	82	31	eij	eij	PROPN
fcis-6030	82	32	=	=	PROPN
fcis-6030	82	33	qi	qi	PROPN
fcis-6030	82	34	tkj	tkj	NOUN
fcis-6030	82	35	√dk	√dk	PROPN
fcis-6030	82	36	(	(	PUNCT
fcis-6030	82	37	6	6	NUM
fcis-6030	82	38	)	)	PUNCT
fcis-6030	82	39	where	where	SCONJ
fcis-6030	82	40	dk	dk	PROPN
fcis-6030	82	41	is	be	AUX
fcis-6030	82	42	the	the	DET
fcis-6030	82	43	number	number	NOUN
fcis-6030	82	44	of	of	ADP
fcis-6030	82	45	dimension	dimension	NOUN
fcis-6030	82	46	of	of	ADP
fcis-6030	82	47	keys	key	NOUN
fcis-6030	82	48	.	.	PUNCT
fcis-6030	83	1	b	b	X
fcis-6030	83	2	)	)	PUNCT
fcis-6030	83	3	multi	multi	ADJ
fcis-6030	83	4	-	-	ADJ
fcis-6030	83	5	head	head	ADJ
fcis-6030	83	6	attention	attention	NOUN
fcis-6030	83	7	subblock	subblock	NOUN
fcis-6030	83	8	:	:	PUNCT
fcis-6030	83	9	multi	multi	ADJ
fcis-6030	83	10	-	-	ADJ
fcis-6030	83	11	head	head	ADJ
fcis-6030	83	12	attention	attention	NOUN
fcis-6030	83	13	subblock	subblock	NOUN
fcis-6030	83	14	comprises	comprise	VERB
fcis-6030	83	15	multiple	multiple	ADJ
fcis-6030	83	16	self	self	NOUN
fcis-6030	83	17	-	-	PUNCT
fcis-6030	83	18	attention	attention	NOUN
fcis-6030	83	19	functions	function	NOUN
fcis-6030	83	20	.	.	PUNCT
fcis-6030	84	1	the	the	DET
fcis-6030	84	2	final	final	ADJ
fcis-6030	84	3	output	output	NOUN
fcis-6030	84	4	is	be	AUX
fcis-6030	84	5	the	the	DET
fcis-6030	84	6	weighted	weighted	ADJ
fcis-6030	84	7	sum	sum	NOUN
fcis-6030	84	8	of	of	ADP
fcis-6030	84	9	outputs	output	NOUN
fcis-6030	84	10	from	from	ADP
fcis-6030	84	11	n	n	PRON
fcis-6030	84	12	selfattention	selfattention	NOUN
fcis-6030	84	13	funtions	funtion	NOUN
fcis-6030	84	14	:	:	PUNCT
fcis-6030	84	15	yi	yi	PROPN
fcis-6030	84	16	=	=	SYM
fcis-6030	84	17	attention(x	attention(x	PROPN
fcis-6030	84	18	)	)	PUNCT
fcis-6030	84	19	(	(	PUNCT
fcis-6030	84	20	7	7	X
fcis-6030	84	21	)	)	PUNCT
fcis-6030	84	22	y	y	NOUN
fcis-6030	84	23	=	=	PUNCT
fcis-6030	84	24	∑	∑	PUNCT
fcis-6030	84	25	yin	yin	PROPN
fcis-6030	84	26	i=1	i=1	X
fcis-6030	84	27	(	(	PUNCT
fcis-6030	84	28	8)	8)	NUM
fcis-6030	84	29	figure	figure	NOUN
fcis-6030	84	30	2	2	NUM
fcis-6030	84	31	.	.	PUNCT
fcis-6030	84	32	structure	structure	NOUN
fcis-6030	84	33	of	of	ADP
fcis-6030	84	34	the	the	DET
fcis-6030	84	35	4	4	NUM
fcis-6030	84	36	-	-	PUNCT
fcis-6030	84	37	layer	layer	NOUN
fcis-6030	84	38	unets	unet	NOUN
fcis-6030	84	39	each	each	DET
fcis-6030	84	40	downsample	downsample	NOUN
fcis-6030	84	41	layer	layer	NOUN
fcis-6030	84	42	consists	consist	VERB
fcis-6030	84	43	of	of	ADP
fcis-6030	84	44	a	a	DET
fcis-6030	84	45	convolution	convolution	NOUN
fcis-6030	84	46	layer	layer	NOUN
fcis-6030	84	47	for	for	ADP
fcis-6030	84	48	pre	pre	ADJ
fcis-6030	84	49	-	-	ADJ
fcis-6030	84	50	downsampling	downsample	VERB
fcis-6030	84	51	,	,	PUNCT
fcis-6030	84	52	8	8	NUM
fcis-6030	84	53	resnet	resnet	NOUN
fcis-6030	84	54	blocks	block	NOUN
fcis-6030	84	55	,	,	PUNCT
fcis-6030	84	56	and	and	CCONJ
fcis-6030	84	57	a	a	DET
fcis-6030	84	58	cross	cross	ADJ
fcis-6030	84	59	-	-	ADJ
fcis-6030	84	60	attention	attention	ADJ
fcis-6030	84	61	layer	layer	NOUN
fcis-6030	84	62	.	.	PUNCT
fcis-6030	85	1	the	the	DET
fcis-6030	85	2	middle	middle	ADJ
fcis-6030	85	3	layer	layer	NOUN
fcis-6030	85	4	is	be	AUX
fcis-6030	85	5	composed	compose	VERB
fcis-6030	85	6	of	of	ADP
fcis-6030	85	7	2	2	NUM
fcis-6030	85	8	resnet	resnet	NOUN
fcis-6030	85	9	blocks	block	NOUN
fcis-6030	85	10	and	and	CCONJ
fcis-6030	85	11	a	a	DET
fcis-6030	85	12	cross	cross	ADJ
fcis-6030	85	13	-	-	ADJ
fcis-6030	85	14	attention	attention	ADJ
fcis-6030	85	15	layer	layer	NOUN
fcis-6030	85	16	.	.	PUNCT
fcis-6030	86	1	each	each	DET
fcis-6030	86	2	upsample	upsample	NOUN
fcis-6030	86	3	layer	layer	NOUN
fcis-6030	86	4	comprises	comprise	VERB
fcis-6030	86	5	8	8	NUM
fcis-6030	86	6	resnet	resnet	NOUN
fcis-6030	86	7	blocks	block	NOUN
fcis-6030	86	8	,	,	PUNCT
fcis-6030	86	9	a	a	DET
fcis-6030	86	10	upsample	upsample	NOUN
fcis-6030	86	11	block	block	NOUN
fcis-6030	86	12	,	,	PUNCT
fcis-6030	86	13	and	and	CCONJ
fcis-6030	86	14	a	a	DET
fcis-6030	86	15	cross	cross	ADJ
fcis-6030	86	16	-	-	ADJ
fcis-6030	86	17	attention	attention	ADJ
fcis-6030	86	18	block	block	NOUN
fcis-6030	86	19	.	.	PUNCT
fcis-6030	87	1	the	the	DET
fcis-6030	87	2	skip	skip	ADJ
fcis-6030	87	3	connecting	connect	VERB
fcis-6030	87	4	is	be	AUX
fcis-6030	87	5	between	between	ADP
fcis-6030	87	6	each	each	DET
fcis-6030	87	7	resnet	resnet	NOUN
fcis-6030	87	8	block	block	NOUN
fcis-6030	87	9	of	of	ADP
fcis-6030	87	10	downsample	downsample	PROPN
fcis-6030	87	11	layers	layer	NOUN
fcis-6030	87	12	and	and	CCONJ
fcis-6030	87	13	up	up	ADP
fcis-6030	87	14	sample	sample	NOUN
fcis-6030	87	15	layers	layer	NOUN
fcis-6030	87	16	.	.	PUNCT
fcis-6030	88	1	text	text	NOUN
fcis-6030	88	2	conditions	condition	NOUN
fcis-6030	88	3	are	be	AUX
fcis-6030	88	4	added	add	VERB
fcis-6030	88	5	to	to	ADP
fcis-6030	88	6	the	the	DET
fcis-6030	88	7	network	network	NOUN
fcis-6030	88	8	in	in	ADP
fcis-6030	88	9	cross	cross	ADJ
fcis-6030	88	10	-	-	ADJ
fcis-6030	88	11	attention	attention	ADJ
fcis-6030	88	12	block	block	NOUN
fcis-6030	88	13	.	.	PUNCT
fcis-6030	89	1	c	c	X
fcis-6030	89	2	)	)	PUNCT
fcis-6030	89	3	encoder	encoder	NOUN
fcis-6030	89	4	:	:	PUNCT
fcis-6030	89	5	the	the	DET
fcis-6030	89	6	encoder	encoder	NOUN
fcis-6030	89	7	is	be	AUX
fcis-6030	89	8	a	a	DET
fcis-6030	89	9	stack	stack	NOUN
fcis-6030	89	10	of	of	ADP
fcis-6030	89	11	identical	identical	ADJ
fcis-6030	89	12	blocks	block	NOUN
fcis-6030	89	13	.	.	PUNCT
fcis-6030	90	1	each	each	DET
fcis-6030	90	2	basic	basic	ADJ
fcis-6030	90	3	block	block	NOUN
fcis-6030	90	4	of	of	ADP
fcis-6030	90	5	the	the	DET
fcis-6030	90	6	transformer	transformer	NOUN
fcis-6030	90	7	mainly	mainly	ADV
fcis-6030	90	8	comprises	comprise	VERB
fcis-6030	90	9	two	two	NUM
fcis-6030	90	10	subblocks	subblock	NOUN
fcis-6030	90	11	:	:	PUNCT
fcis-6030	90	12	a	a	DET
fcis-6030	90	13	multi	multi	ADJ
fcis-6030	90	14	-	-	ADJ
fcis-6030	90	15	head	head	ADJ
fcis-6030	90	16	attention	attention	NOUN
fcis-6030	90	17	block	block	NOUN
fcis-6030	90	18	and	and	CCONJ
fcis-6030	90	19	a	a	DET
fcis-6030	90	20	feed	feed	NOUN
fcis-6030	90	21	forward	forward	ADJ
fcis-6030	90	22	block	block	NOUN
fcis-6030	90	23	.	.	PUNCT
fcis-6030	91	1	the	the	DET
fcis-6030	91	2	feed	feed	NOUN
fcis-6030	91	3	forward	forward	ADV
fcis-6030	91	4	block	block	NOUN
fcis-6030	91	5	is	be	AUX
fcis-6030	91	6	simply	simply	ADV
fcis-6030	91	7	a	a	DET
fcis-6030	91	8	fully	fully	ADV
fcis-6030	91	9	connected	connect	VERB
fcis-6030	91	10	layer	layer	NOUN
fcis-6030	91	11	.	.	PUNCT
fcis-6030	92	1	both	both	DET
fcis-6030	92	2	subblocks	subblock	NOUN
fcis-6030	92	3	are	be	AUX
fcis-6030	92	4	followed	follow	VERB
fcis-6030	92	5	by	by	ADP
fcis-6030	92	6	a	a	DET
fcis-6030	92	7	residual	residual	ADJ
fcis-6030	92	8	skip	skip	NOUN
fcis-6030	92	9	connection	connection	NOUN
fcis-6030	92	10	and	and	CCONJ
fcis-6030	92	11	a	a	DET
fcis-6030	92	12	layer	layer	NOUN
fcis-6030	92	13	normalization	normalization	NOUN
fcis-6030	92	14	.	.	PUNCT
fcis-6030	93	1	the	the	DET
fcis-6030	93	2	block	block	NOUN
fcis-6030	93	3	of	of	ADP
fcis-6030	93	4	t5	t5	PROPN
fcis-6030	93	5	is	be	AUX
fcis-6030	93	6	similar	similar	ADJ
fcis-6030	93	7	to	to	ADP
fcis-6030	93	8	the	the	DET
fcis-6030	93	9	block	block	NOUN
fcis-6030	93	10	proposed	propose	VERB
fcis-6030	93	11	in	in	ADP
fcis-6030	93	12	[	[	X
fcis-6030	93	13	10	10	NUM
fcis-6030	93	14	]	]	PUNCT
fcis-6030	93	15	,	,	PUNCT
fcis-6030	93	16	with	with	ADP
fcis-6030	93	17	the	the	DET
fcis-6030	93	18	modification	modification	NOUN
fcis-6030	93	19	in	in	ADP
fcis-6030	93	20	the	the	DET
fcis-6030	93	21	skip	skip	ADJ
fcis-6030	93	22	connection	connection	NOUN
fcis-6030	93	23	and	and	CCONJ
fcis-6030	93	24	normalization	normalization	NOUN
fcis-6030	93	25	.	.	PUNCT
fcis-6030	94	1	in	in	ADP
fcis-6030	94	2	t5	t5	PROPN
fcis-6030	94	3	model	model	NOUN
fcis-6030	94	4	,	,	PUNCT
fcis-6030	94	5	the	the	DET
fcis-6030	94	6	layer	layer	NOUN
fcis-6030	94	7	norm	norm	NOUN
fcis-6030	94	8	is	be	AUX
fcis-6030	94	9	simplified	simplify	VERB
fcis-6030	94	10	by	by	ADP
fcis-6030	94	11	only	only	ADV
fcis-6030	94	12	keeping	keep	VERB
fcis-6030	94	13	the	the	DET
fcis-6030	94	14	rescaled	rescaled	NOUN
fcis-6030	94	15	without	without	ADP
fcis-6030	94	16	the	the	DET
fcis-6030	94	17	additive	additive	ADJ
fcis-6030	94	18	bias	bias	NOUN
fcis-6030	94	19	.	.	PUNCT
fcis-6030	95	1	the	the	DET
fcis-6030	95	2	residual	residual	ADJ
fcis-6030	95	3	skip	skip	NOUN
fcis-6030	95	4	connection	connection	NOUN
fcis-6030	95	5	is	be	AUX
fcis-6030	95	6	after	after	ADP
fcis-6030	95	7	the	the	DET
fcis-6030	95	8	87	87	NUM
fcis-6030	95	9	normalization	normalization	NOUN
fcis-6030	95	10	,	,	PUNCT
fcis-6030	95	11	so	so	CCONJ
fcis-6030	95	12	the	the	DET
fcis-6030	95	13	output	output	NOUN
fcis-6030	95	14	of	of	ADP
fcis-6030	95	15	each	each	DET
fcis-6030	95	16	subblock	subblock	NOUN
fcis-6030	95	17	is	be	AUX
fcis-6030	95	18	x＋simplayernorm	x＋simplayernorm	PROPN
fcis-6030	95	19	(	(	PUNCT
fcis-6030	95	20	subblock(x	subblock(x	PROPN
fcis-6030	95	21	)	)	PUNCT
fcis-6030	95	22	)	)	PUNCT
fcis-6030	95	23	.	.	PUNCT
fcis-6030	96	1	besides	besides	SCONJ
fcis-6030	96	2	.	.	PUNCT
fcis-6030	97	1	dropout	dropout	NOUN
fcis-6030	97	2	is	be	AUX
fcis-6030	97	3	applied	apply	VERB
fcis-6030	97	4	within	within	ADP
fcis-6030	97	5	the	the	DET
fcis-6030	97	6	feed	feed	NOUN
fcis-6030	97	7	forward	forward	ADJ
fcis-6030	97	8	network	network	NOUN
fcis-6030	97	9	.	.	PUNCT
fcis-6030	98	1	the	the	DET
fcis-6030	98	2	input	input	NOUN
fcis-6030	98	3	text	text	NOUN
fcis-6030	98	4	tokens	token	NOUN
fcis-6030	98	5	are	be	AUX
fcis-6030	98	6	first	first	ADV
fcis-6030	98	7	mapped	map	VERB
fcis-6030	98	8	to	to	ADP
fcis-6030	98	9	sequence	sequence	NOUN
fcis-6030	98	10	embedding	embed	VERB
fcis-6030	98	11	and	and	CCONJ
fcis-6030	98	12	added	add	VERB
fcis-6030	98	13	with	with	ADP
fcis-6030	98	14	positional	positional	ADJ
fcis-6030	98	15	embedding	embed	VERB
fcis-6030	98	16	with	with	ADP
fcis-6030	98	17	the	the	DET
fcis-6030	98	18	same	same	ADJ
fcis-6030	98	19	shape	shape	NOUN
fcis-6030	98	20	,	,	PUNCT
fcis-6030	98	21	which	which	PRON
fcis-6030	98	22	is	be	AUX
fcis-6030	98	23	then	then	ADV
fcis-6030	98	24	passed	pass	VERB
fcis-6030	98	25	to	to	ADP
fcis-6030	98	26	the	the	DET
fcis-6030	98	27	encoder	encoder	NOUN
fcis-6030	98	28	.	.	PUNCT
fcis-6030	99	1	after	after	ADP
fcis-6030	99	2	several	several	ADJ
fcis-6030	99	3	transformer	transformer	NOUN
fcis-6030	99	4	blocks	block	NOUN
fcis-6030	99	5	,	,	PUNCT
fcis-6030	99	6	high	high	ADJ
fcis-6030	99	7	-	-	PUNCT
fcis-6030	99	8	level	level	NOUN
fcis-6030	99	9	features	feature	VERB
fcis-6030	99	10	f	f	X
fcis-6030	99	11	=	=	SYM
fcis-6030	99	12	{	{	PUNCT
fcis-6030	99	13	qe	qe	PROPN
fcis-6030	99	14	,	,	PUNCT
fcis-6030	99	15	ke	ke	PROPN
fcis-6030	99	16	,	,	PUNCT
fcis-6030	99	17	ve	ve	AUX
fcis-6030	99	18	}	}	PUNCT
fcis-6030	99	19	are	be	AUX
fcis-6030	99	20	extracted	extract	VERB
fcis-6030	99	21	.	.	PUNCT
fcis-6030	100	1	d	d	X
fcis-6030	100	2	)	)	PUNCT
fcis-6030	100	3	decoder	decoder	NOUN
fcis-6030	100	4	:	:	PUNCT
fcis-6030	100	5	the	the	DET
fcis-6030	100	6	decoder	decoder	NOUN
fcis-6030	100	7	also	also	ADV
fcis-6030	100	8	comprises	comprise	VERB
fcis-6030	100	9	several	several	ADJ
fcis-6030	100	10	identical	identical	ADJ
fcis-6030	100	11	blocks	block	NOUN
fcis-6030	100	12	.	.	PUNCT
fcis-6030	101	1	the	the	DET
fcis-6030	101	2	structures	structure	NOUN
fcis-6030	101	3	of	of	ADP
fcis-6030	101	4	these	these	DET
fcis-6030	101	5	blocks	block	NOUN
fcis-6030	101	6	are	be	AUX
fcis-6030	101	7	similar	similar	ADJ
fcis-6030	101	8	to	to	ADP
fcis-6030	101	9	those	those	PRON
fcis-6030	101	10	in	in	ADP
fcis-6030	101	11	the	the	DET
fcis-6030	101	12	encoder	encoder	NOUN
fcis-6030	101	13	,	,	PUNCT
fcis-6030	101	14	but	but	CCONJ
fcis-6030	101	15	with	with	ADP
fcis-6030	101	16	an	an	DET
fcis-6030	101	17	addition	addition	NOUN
fcis-6030	101	18	multi	multi	ADJ
fcis-6030	101	19	-	-	ADJ
fcis-6030	101	20	head	head	ADJ
fcis-6030	101	21	attention	attention	NOUN
fcis-6030	101	22	subblock	subblock	NOUN
fcis-6030	101	23	appended	append	VERB
fcis-6030	101	24	before	before	ADP
fcis-6030	101	25	the	the	DET
fcis-6030	101	26	feed	feed	NOUN
fcis-6030	101	27	forward	forward	ADV
fcis-6030	101	28	subblock	subblock	PROPN
fcis-6030	101	29	.	.	PUNCT
fcis-6030	102	1	while	while	SCONJ
fcis-6030	102	2	the	the	DET
fcis-6030	102	3	first	first	ADJ
fcis-6030	102	4	multi	multi	ADJ
fcis-6030	102	5	-	-	ADJ
fcis-6030	102	6	head	head	ADJ
fcis-6030	102	7	attention	attention	NOUN
fcis-6030	102	8	function	function	NOUN
fcis-6030	102	9	takes	take	VERB
fcis-6030	102	10	the	the	DET
fcis-6030	102	11	output	output	NOUN
fcis-6030	102	12	embedding	embed	VERB
fcis-6030	102	13	as	as	ADP
fcis-6030	102	14	the	the	DET
fcis-6030	102	15	input	input	NOUN
fcis-6030	102	16	,	,	PUNCT
fcis-6030	102	17	the	the	DET
fcis-6030	102	18	addition	addition	NOUN
fcis-6030	102	19	attention	attention	NOUN
fcis-6030	102	20	function	function	NOUN
fcis-6030	102	21	put	put	VERB
fcis-6030	102	22	attention	attention	NOUN
fcis-6030	102	23	to	to	ADP
fcis-6030	102	24	both	both	DET
fcis-6030	102	25	features	feature	NOUN
fcis-6030	102	26	from	from	ADP
fcis-6030	102	27	the	the	DET
fcis-6030	102	28	last	last	ADJ
fcis-6030	102	29	decoder	decoder	NOUN
fcis-6030	102	30	block	block	NOUN
fcis-6030	102	31	as	as	ADV
fcis-6030	102	32	well	well	ADV
fcis-6030	102	33	as	as	ADP
fcis-6030	102	34	the	the	DET
fcis-6030	102	35	high	high	ADJ
fcis-6030	102	36	-	-	PUNCT
fcis-6030	102	37	level	level	NOUN
fcis-6030	102	38	features	feature	NOUN
fcis-6030	102	39	from	from	ADP
fcis-6030	102	40	the	the	DET
fcis-6030	102	41	encoder	encoder	NOUN
fcis-6030	102	42	.	.	PUNCT
fcis-6030	103	1	specifically	specifically	ADV
fcis-6030	103	2	,	,	PUNCT
fcis-6030	103	3	it	it	PRON
fcis-6030	103	4	attends	attend	VERB
fcis-6030	103	5	on	on	ADP
fcis-6030	103	6	the	the	DET
fcis-6030	103	7	query	query	NOUN
fcis-6030	103	8	from	from	ADP
fcis-6030	103	9	the	the	DET
fcis-6030	103	10	first	first	ADJ
fcis-6030	103	11	attention	attention	NOUN
fcis-6030	103	12	function	function	NOUN
fcis-6030	103	13	and	and	CCONJ
fcis-6030	103	14	the	the	DET
fcis-6030	103	15	key	key	ADJ
fcis-6030	103	16	-	-	PUNCT
fcis-6030	103	17	value	value	NOUN
fcis-6030	103	18	pair	pair	NOUN
fcis-6030	103	19	from	from	ADP
fcis-6030	103	20	the	the	DET
fcis-6030	103	21	high	high	ADJ
fcis-6030	103	22	-	-	PUNCT
fcis-6030	103	23	level	level	NOUN
fcis-6030	103	24	encoder	encoder	NOUN
fcis-6030	103	25	features	feature	VERB
fcis-6030	103	26	{	{	PUNCT
fcis-6030	103	27	qd	qd	PROPN
fcis-6030	103	28	,	,	PUNCT
fcis-6030	103	29	ke	ke	PROPN
fcis-6030	103	30	,	,	PUNCT
fcis-6030	103	31	ve	ve	VERB
fcis-6030	103	32	}	}	PUNCT
fcis-6030	103	33	.	.	PUNCT
fcis-6030	104	1	the	the	DET
fcis-6030	104	2	final	final	ADJ
fcis-6030	104	3	output	output	NOUN
fcis-6030	104	4	of	of	ADP
fcis-6030	104	5	the	the	DET
fcis-6030	104	6	decoder	decoder	NOUN
fcis-6030	104	7	is	be	AUX
fcis-6030	104	8	passed	pass	VERB
fcis-6030	104	9	to	to	ADP
fcis-6030	104	10	a	a	DET
fcis-6030	104	11	softmax	softmax	NOUN
fcis-6030	104	12	layer	layer	NOUN
fcis-6030	104	13	,	,	PUNCT
fcis-6030	104	14	indicating	indicate	VERB
fcis-6030	104	15	the	the	DET
fcis-6030	104	16	probability	probability	NOUN
fcis-6030	104	17	of	of	ADP
fcis-6030	104	18	each	each	DET
fcis-6030	104	19	word	word	NOUN
fcis-6030	104	20	token	token	VERB
fcis-6030	104	21	.	.	PUNCT
fcis-6030	105	1	imagen	imagen	PROPN
fcis-6030	105	2	exploits	exploit	VERB
fcis-6030	105	3	the	the	DET
fcis-6030	105	4	encoder	encoder	NOUN
fcis-6030	105	5	of	of	ADP
fcis-6030	105	6	t5	t5	PROPN
fcis-6030	105	7	transformer	transformer	NOUN
fcis-6030	105	8	to	to	PART
fcis-6030	105	9	convert	convert	VERB
fcis-6030	105	10	text	text	NOUN
fcis-6030	105	11	tokens	token	NOUN
fcis-6030	105	12	to	to	ADP
fcis-6030	105	13	high	high	ADJ
fcis-6030	105	14	-	-	PUNCT
fcis-6030	105	15	level	level	NOUN
fcis-6030	105	16	features	feature	NOUN
fcis-6030	105	17	.	.	PUNCT
fcis-6030	106	1	3.2	3.2	NUM
fcis-6030	106	2	.	.	PUNCT
fcis-6030	106	3	diffusion	diffusion	NOUN
fcis-6030	106	4	-	-	PUNCT
fcis-6030	106	5	based	base	VERB
fcis-6030	106	6	decoder	decoder	NOUN
fcis-6030	106	7	1	1	NUM
fcis-6030	106	8	)	)	PUNCT
fcis-6030	106	9	denoising	denoise	VERB
fcis-6030	106	10	diffusion	diffusion	NOUN
fcis-6030	106	11	probabilistic	probabilistic	ADJ
fcis-6030	106	12	models	model	NOUN
fcis-6030	106	13	:	:	PUNCT
fcis-6030	106	14	ddpm	ddpm	NOUN
fcis-6030	106	15	comprises	comprise	VERB
fcis-6030	106	16	a	a	DET
fcis-6030	106	17	forward	forward	ADV
fcis-6030	106	18	noising	noising	NOUN
fcis-6030	106	19	process	process	NOUN
fcis-6030	106	20	and	and	CCONJ
fcis-6030	106	21	a	a	DET
fcis-6030	106	22	reverse	reverse	ADJ
fcis-6030	106	23	denoising	denoising	NOUN
fcis-6030	106	24	process	process	NOUN
fcis-6030	106	25	.	.	PUNCT
fcis-6030	107	1	given	give	VERB
fcis-6030	107	2	a	a	DET
fcis-6030	107	3	data	datum	NOUN
fcis-6030	107	4	x0	x0	PROPN
fcis-6030	107	5	in	in	ADP
fcis-6030	107	6	distribution	distribution	NOUN
fcis-6030	107	7	q	q	PROPN
fcis-6030	107	8	(	(	PUNCT
fcis-6030	107	9	x0	x0	PROPN
fcis-6030	107	10	):	):	PUNCT
fcis-6030	107	11	x0	x0	PROPN
fcis-6030	107	12	~	~	PUNCT
fcis-6030	107	13	q	q	X
fcis-6030	107	14	(	(	PUNCT
fcis-6030	107	15	x0	x0	PROPN
fcis-6030	107	16	)	)	PUNCT
fcis-6030	107	17	,	,	PUNCT
fcis-6030	107	18	the	the	DET
fcis-6030	107	19	forward	forward	ADJ
fcis-6030	107	20	process	process	NOUN
fcis-6030	107	21	gradually	gradually	ADV
fcis-6030	107	22	adds	add	VERB
fcis-6030	107	23	gaussian	gaussian	NOUN
fcis-6030	107	24	noise	noise	NOUN
fcis-6030	107	25	to	to	ADP
fcis-6030	107	26	the	the	DET
fcis-6030	107	27	data	datum	NOUN
fcis-6030	107	28	,	,	PUNCT
fcis-6030	107	29	producing	produce	VERB
fcis-6030	107	30	a	a	DET
fcis-6030	107	31	sequence	sequence	NOUN
fcis-6030	107	32	from	from	ADP
fcis-6030	107	33	x1	x1	PROPN
fcis-6030	107	34	to	to	PART
fcis-6030	107	35	xt	xt	NUM
fcis-6030	107	36	:	:	PUNCT
fcis-6030	107	37	q(x1	q(x1	ADJ
fcis-6030	107	38	:	:	PUNCT
fcis-6030	107	39	t|x0	t|x0	NUM
fcis-6030	107	40	)	)	PUNCT
fcis-6030	107	41	=	=	SYM
fcis-6030	107	42	∏	∏	PROPN
fcis-6030	107	43	q(xt|xt−1)t	q(xt|xt−1)t	NOUN
fcis-6030	107	44	1	1	NUM
fcis-6030	107	45	(	(	PUNCT
fcis-6030	107	46	9	9	NUM
fcis-6030	107	47	)	)	PUNCT
fcis-6030	107	48	q(xt|xt−1	q(xt|xt−1	NOUN
fcis-6030	107	49	)	)	PUNCT
fcis-6030	108	1	=	=	NOUN
fcis-6030	108	2	n(xt	n(xt	NOUN
fcis-6030	108	3	;	;	PUNCT
fcis-6030	108	4	√1	√1	PROPN
fcis-6030	108	5	−	−	PROPN
fcis-6030	108	6	βtxt−1	βtxt−1	PROPN
fcis-6030	108	7	,	,	PUNCT
fcis-6030	108	8	βti	βti	NOUN
fcis-6030	108	9	)	)	PUNCT
fcis-6030	108	10	(	(	PUNCT
fcis-6030	108	11	10	10	NUM
fcis-6030	108	12	)	)	PUNCT
fcis-6030	108	13	the	the	DET
fcis-6030	108	14	sequence	sequence	NOUN
fcis-6030	108	15	is	be	AUX
fcis-6030	108	16	a	a	DET
fcis-6030	108	17	markov	markov	NOUN
fcis-6030	108	18	chain	chain	NOUN
fcis-6030	108	19	and	and	CCONJ
fcis-6030	108	20	it	it	PRON
fcis-6030	108	21	is	be	AUX
fcis-6030	108	22	proved	prove	VERB
fcis-6030	108	23	that	that	SCONJ
fcis-6030	108	24	if	if	SCONJ
fcis-6030	108	25	t	t	PROPN
fcis-6030	108	26	is	be	AUX
fcis-6030	108	27	sufficiently	sufficiently	ADV
fcis-6030	108	28	large	large	ADJ
fcis-6030	108	29	,	,	PUNCT
fcis-6030	108	30	xt	xt	X
fcis-6030	108	31	is	be	AUX
fcis-6030	108	32	an	an	DET
fcis-6030	108	33	isotropic	isotropic	ADJ
fcis-6030	108	34	gaussian	gaussian	ADJ
fcis-6030	108	35	distribution	distribution	NOUN
fcis-6030	108	36	.	.	PUNCT
fcis-6030	109	1	therefore	therefore	ADV
fcis-6030	109	2	,	,	PUNCT
fcis-6030	109	3	if	if	SCONJ
fcis-6030	109	4	given	give	VERB
fcis-6030	109	5	the	the	DET
fcis-6030	109	6	reverse	reverse	ADJ
fcis-6030	109	7	distribution	distribution	NOUN
fcis-6030	109	8	p(xt-1|xt	p(xt-1|xt	PROPN
fcis-6030	109	9	)	)	PUNCT
fcis-6030	109	10	for	for	ADP
fcis-6030	109	11	every	every	DET
fcis-6030	109	12	time	time	NOUN
fcis-6030	109	13	step	step	NOUN
fcis-6030	109	14	t	t	NOUN
fcis-6030	109	15	in	in	ADP
fcis-6030	109	16	the	the	DET
fcis-6030	109	17	sequence	sequence	NOUN
fcis-6030	109	18	,	,	PUNCT
fcis-6030	109	19	the	the	DET
fcis-6030	109	20	original	original	ADJ
fcis-6030	109	21	data	datum	NOUN
fcis-6030	109	22	x0	x0	PROPN
fcis-6030	109	23	can	can	AUX
fcis-6030	109	24	be	be	AUX
fcis-6030	109	25	recovered	recover	VERB
fcis-6030	109	26	from	from	ADP
fcis-6030	109	27	a	a	DET
fcis-6030	109	28	pure	pure	ADJ
fcis-6030	109	29	gaussian	gaussian	ADJ
fcis-6030	109	30	noise	noise	NOUN
fcis-6030	109	31	through	through	ADP
fcis-6030	109	32	a	a	DET
fcis-6030	109	33	sequence	sequence	NOUN
fcis-6030	109	34	of	of	ADP
fcis-6030	109	35	sampling	sample	VERB
fcis-6030	109	36	.	.	PUNCT
fcis-6030	110	1	while	while	SCONJ
fcis-6030	110	2	the	the	DET
fcis-6030	110	3	reverse	reverse	ADJ
fcis-6030	110	4	distribution	distribution	NOUN
fcis-6030	110	5	p(xt-1|xt	p(xt-1|xt	PROPN
fcis-6030	110	6	)	)	PUNCT
fcis-6030	110	7	depends	depend	VERB
fcis-6030	110	8	on	on	ADP
fcis-6030	110	9	the	the	DET
fcis-6030	110	10	forward	forward	ADJ
fcis-6030	110	11	process	process	NOUN
fcis-6030	110	12	,	,	PUNCT
fcis-6030	110	13	a	a	DET
fcis-6030	110	14	neural	neural	ADJ
fcis-6030	110	15	network	network	NOUN
fcis-6030	110	16	can	can	AUX
fcis-6030	110	17	be	be	AUX
fcis-6030	110	18	used	use	VERB
fcis-6030	110	19	to	to	PART
fcis-6030	110	20	infer	infer	VERB
fcis-6030	110	21	it	it	PRON
fcis-6030	110	22	without	without	ADP
fcis-6030	110	23	forwarding	forward	VERB
fcis-6030	110	24	information	information	NOUN
fcis-6030	110	25	:	:	PUNCT
fcis-6030	110	26	pθ(xt−1|xt	pθ(xt−1|xt	ADV
fcis-6030	110	27	)	)	PUNCT
fcis-6030	110	28	=	=	SYM
fcis-6030	111	1	n(xt−1	n(xt−1	NOUN
fcis-6030	111	2	;	;	PUNCT
fcis-6030	111	3	μθ(xt	μθ(xt	NOUN
fcis-6030	111	4	,	,	PUNCT
fcis-6030	111	5	t	t	PROPN
fcis-6030	111	6	)	)	PUNCT
fcis-6030	111	7	,	,	PUNCT
fcis-6030	111	8	∑	∑	PROPN
fcis-6030	111	9	(	(	PUNCT
fcis-6030	111	10	xt	xt	PROPN
fcis-6030	111	11	,	,	PUNCT
fcis-6030	111	12	t))θ	t))θ	NOUN
fcis-6030	111	13	(	(	PUNCT
fcis-6030	111	14	11	11	NUM
fcis-6030	111	15	)	)	PUNCT
fcis-6030	111	16	where	where	SCONJ
fcis-6030	111	17	µθ	µθ	NOUN
fcis-6030	111	18	,	,	PUNCT
fcis-6030	111	19	σθ	σθ	NOUN
fcis-6030	111	20	are	be	AUX
fcis-6030	111	21	the	the	DET
fcis-6030	111	22	parameters	parameter	NOUN
fcis-6030	111	23	predicted	predict	VERB
fcis-6030	111	24	by	by	ADP
fcis-6030	111	25	the	the	DET
fcis-6030	111	26	network	network	NOUN
fcis-6030	111	27	.	.	PUNCT
fcis-6030	112	1	in	in	ADP
fcis-6030	112	2	training	training	NOUN
fcis-6030	112	3	,	,	PUNCT
fcis-6030	112	4	the	the	DET
fcis-6030	112	5	goal	goal	NOUN
fcis-6030	112	6	is	be	AUX
fcis-6030	112	7	to	to	PART
fcis-6030	112	8	adjust	adjust	VERB
fcis-6030	112	9	parameter	parameter	NOUN
fcis-6030	112	10	θ	θ	PROPN
fcis-6030	112	11	to	to	PART
fcis-6030	112	12	make	make	VERB
fcis-6030	112	13	the	the	DET
fcis-6030	112	14	predicted	predict	VERB
fcis-6030	112	15	distribution	distribution	NOUN
fcis-6030	112	16	of	of	ADP
fcis-6030	112	17	reversed	reverse	VERB
fcis-6030	112	18	markov	markov	NOUN
fcis-6030	112	19	chain	chain	NOUN
fcis-6030	112	20	pθ(x0	pθ(x0	NOUN
fcis-6030	112	21	,	,	PUNCT
fcis-6030	112	22	x1	x1	PROPN
fcis-6030	112	23	,	,	PUNCT
fcis-6030	112	24	...	...	PUNCT
fcis-6030	112	25	,	,	PUNCT
fcis-6030	112	26	xt	xt	X
fcis-6030	112	27	)	)	PUNCT
fcis-6030	112	28	close	close	ADV
fcis-6030	112	29	to	to	ADP
fcis-6030	112	30	the	the	DET
fcis-6030	112	31	groundtruth	groundtruth	NOUN
fcis-6030	112	32	distribution	distribution	NOUN
fcis-6030	112	33	q(x0	q(x0	NOUN
fcis-6030	112	34	,	,	PUNCT
fcis-6030	112	35	x1	x1	PROPN
fcis-6030	112	36	,	,	PUNCT
fcis-6030	112	37	...	...	PUNCT
fcis-6030	112	38	,	,	PUNCT
fcis-6030	112	39	xt	xt	NUM
fcis-6030	112	40	)	)	PUNCT
fcis-6030	112	41	.	.	PUNCT
fcis-6030	113	1	this	this	PRON
fcis-6030	113	2	can	can	AUX
fcis-6030	113	3	be	be	AUX
fcis-6030	113	4	achieved	achieve	VERB
fcis-6030	113	5	by	by	ADP
fcis-6030	113	6	minimizing	minimize	VERB
fcis-6030	113	7	the	the	DET
fcis-6030	113	8	variational	variational	ADJ
fcis-6030	113	9	lower	lower	ADV
fcis-6030	113	10	bound	bind	VERB
fcis-6030	113	11	(	(	PUNCT
fcis-6030	113	12	vlb	vlb	PROPN
fcis-6030	113	13	):	):	PUNCT
fcis-6030	113	14	lvlb	lvlb	PROPN
fcis-6030	114	1	=	=	PUNCT
fcis-6030	114	2	∑	∑	PROPN
fcis-6030	114	3	li	li	PROPN
fcis-6030	114	4	t	t	PROPN
fcis-6030	114	5	i=0	i=0	PROPN
fcis-6030	114	6	(	(	PUNCT
fcis-6030	114	7	12	12	NUM
fcis-6030	114	8	)	)	PUNCT
fcis-6030	114	9	where	where	SCONJ
fcis-6030	114	10	l0	l0	NOUN
fcis-6030	114	11	=	=	PUNCT
fcis-6030	114	12	−log	−log	NOUN
fcis-6030	114	13	pθ(x0|x1	pθ(x0|x1	PROPN
fcis-6030	114	14	)	)	PUNCT
fcis-6030	114	15	(	(	PUNCT
fcis-6030	114	16	13	13	X
fcis-6030	114	17	)	)	PUNCT
fcis-6030	114	18	lt−1	lt−1	PROPN
fcis-6030	114	19	=	=	SYM
fcis-6030	114	20	dkl(q	dkl(q	PROPN
fcis-6030	114	21	(	(	PUNCT
fcis-6030	114	22	xt−1|xt	xt−1|xt	INTJ
fcis-6030	114	23	,	,	PUNCT
fcis-6030	114	24	x0)||pθ(xt−1|xt	x0)||pθ(xt−1|xt	NUM
fcis-6030	114	25	)	)	PUNCT
fcis-6030	114	26	(	(	PUNCT
fcis-6030	114	27	14	14	NUM
fcis-6030	114	28	)	)	PUNCT
fcis-6030	114	29	lt	lt	NOUN
fcis-6030	114	30	=	=	PROPN
fcis-6030	114	31	dkl	dkl	PROPN
fcis-6030	114	32	(	(	PUNCT
fcis-6030	114	33	q	q	PROPN
fcis-6030	114	34	(	(	PUNCT
fcis-6030	114	35	xt|x0)||p(xt	xt|x0)||p(xt	NOUN
fcis-6030	114	36	)	)	PUNCT
fcis-6030	114	37	)	)	PUNCT
fcis-6030	114	38	(	(	PUNCT
fcis-6030	114	39	15	15	NUM
fcis-6030	114	40	)	)	PUNCT
fcis-6030	114	41	however	however	ADV
fcis-6030	114	42	,	,	PUNCT
fcis-6030	114	43	in	in	ADP
fcis-6030	114	44	practice	practice	NOUN
fcis-6030	114	45	,	,	PUNCT
fcis-6030	114	46	a	a	DET
fcis-6030	114	47	simplified	simplified	ADJ
fcis-6030	114	48	training	training	NOUN
fcis-6030	114	49	objective	objective	NOUN
fcis-6030	114	50	is	be	AUX
fcis-6030	114	51	used	use	VERB
fcis-6030	114	52	.	.	PUNCT
fcis-6030	115	1	instead	instead	ADV
fcis-6030	115	2	of	of	ADP
fcis-6030	115	3	predicting	predict	VERB
fcis-6030	115	4	the	the	DET
fcis-6030	115	5	mean	mean	NOUN
fcis-6030	115	6	and	and	CCONJ
fcis-6030	115	7	variance	variance	NOUN
fcis-6030	115	8	of	of	ADP
fcis-6030	115	9	the	the	DET
fcis-6030	115	10	distribution	distribution	NOUN
fcis-6030	115	11	in	in	ADP
fcis-6030	115	12	each	each	DET
fcis-6030	115	13	step	step	NOUN
fcis-6030	115	14	,	,	PUNCT
fcis-6030	115	15	the	the	DET
fcis-6030	115	16	network	network	NOUN
fcis-6030	115	17	could	could	AUX
fcis-6030	115	18	also	also	ADV
fcis-6030	115	19	predict	predict	VERB
fcis-6030	115	20	the	the	DET
fcis-6030	115	21	total	total	ADJ
fcis-6030	115	22	noise	noise	NOUN
fcis-6030	115	23	∈	∈	PROPN
fcis-6030	115	24	added	add	VERB
fcis-6030	115	25	to	to	ADP
fcis-6030	115	26	the	the	DET
fcis-6030	115	27	original	original	ADJ
fcis-6030	115	28	data	datum	NOUN
fcis-6030	115	29	at	at	ADP
fcis-6030	115	30	time	time	NOUN
fcis-6030	115	31	step	step	NOUN
fcis-6030	115	32	i.	i.	PROPN
fcis-6030	115	33	the	the	DET
fcis-6030	115	34	objective	objective	NOUN
fcis-6030	115	35	becomes	become	VERB
fcis-6030	115	36	:	:	PUNCT
fcis-6030	115	37	lsimple	lsimple	ADJ
fcis-6030	115	38	(	(	PUNCT
fcis-6030	115	39	θ	θ	NOUN
fcis-6030	115	40	)	)	PUNCT
fcis-6030	115	41	=	=	SYM
fcis-6030	115	42	e[||ϵ	e[||ϵ	NOUN
fcis-6030	115	43	−	−	NOUN
fcis-6030	115	44	ϵθ(xt	ϵθ(xt	NOUN
fcis-6030	115	45	,	,	PUNCT
fcis-6030	115	46	t	t	NOUN
fcis-6030	115	47	)	)	PUNCT
fcis-6030	115	48	||2	||2	NOUN
fcis-6030	115	49	]	]	PUNCT
fcis-6030	115	50	(	(	PUNCT
fcis-6030	115	51	16	16	NUM
fcis-6030	115	52	)	)	PUNCT
fcis-6030	115	53	where	where	SCONJ
fcis-6030	115	54	∈	∈	PROPN
fcis-6030	115	55	θ	θ	PROPN
fcis-6030	115	56	is	be	AUX
fcis-6030	115	57	the	the	DET
fcis-6030	115	58	predicted	predict	VERB
fcis-6030	115	59	noise	noise	NOUN
fcis-6030	115	60	,	,	PUNCT
fcis-6030	115	61	and	and	CCONJ
fcis-6030	115	62	∈	∈	PROPN
fcis-6030	115	63	is	be	AUX
fcis-6030	115	64	the	the	DET
fcis-6030	115	65	groundtruth	groundtruth	PROPN
fcis-6030	115	66	noise	noise	NOUN
fcis-6030	115	67	randomly	randomly	ADV
fcis-6030	115	68	added	add	VERB
fcis-6030	115	69	to	to	ADP
fcis-6030	115	70	the	the	DET
fcis-6030	115	71	origin	origin	NOUN
fcis-6030	115	72	data	datum	NOUN
fcis-6030	115	73	x0	x0	PROPN
fcis-6030	115	74	to	to	PART
fcis-6030	115	75	generate	generate	VERB
fcis-6030	115	76	the	the	DET
fcis-6030	115	77	data	datum	NOUN
fcis-6030	115	78	xi	xi	ADP
fcis-6030	115	79	in	in	ADP
fcis-6030	115	80	step	step	NOUN
fcis-6030	115	81	i	i	PRON
fcis-6030	115	82	during	during	ADP
fcis-6030	115	83	training	training	NOUN
fcis-6030	115	84	:	:	PUNCT
fcis-6030	115	85	xi	xi	X
fcis-6030	115	86	=	=	PUNCT
fcis-6030	116	1	√αi	√αi	PROPN
fcis-6030	116	2	x0	x0	PROPN
fcis-6030	116	3	+	+	CCONJ
fcis-6030	116	4	βi	βi	PROPN
fcis-6030	116	5	√1−αi̅̅	√1−αi̅̅	NUM
fcis-6030	116	6	̅	̅	NOUN
fcis-6030	116	7	ϵ	ϵ	X
fcis-6030	116	8	(	(	PUNCT
fcis-6030	116	9	17	17	NUM
fcis-6030	116	10	)	)	PUNCT
fcis-6030	116	11	αi	αi	VERB
fcis-6030	116	12	=	=	SYM
fcis-6030	116	13	1	1	NUM
fcis-6030	116	14	−	−	NUM
fcis-6030	116	15	βi	βi	SYM
fcis-6030	116	16	(	(	PUNCT
fcis-6030	116	17	18	18	NUM
fcis-6030	116	18	)	)	PUNCT
fcis-6030	116	19	αi̅	αi̅	NOUN
fcis-6030	116	20	=	=	SYM
fcis-6030	116	21	∏	∏	NUM
fcis-6030	116	22	αj	αj	NOUN
fcis-6030	116	23	i	i	PRON
fcis-6030	116	24	j=0	j=0	PROPN
fcis-6030	116	25	(	(	PUNCT
fcis-6030	116	26	19	19	NUM
fcis-6030	116	27	)	)	PUNCT
fcis-6030	116	28	a	a	DET
fcis-6030	116	29	better	well	ADJ
fcis-6030	116	30	sample	sample	NOUN
fcis-6030	116	31	quality	quality	NOUN
fcis-6030	116	32	can	can	AUX
fcis-6030	116	33	be	be	AUX
fcis-6030	116	34	obtained	obtain	VERB
fcis-6030	116	35	by	by	ADP
fcis-6030	116	36	minimizing	minimize	VERB
fcis-6030	116	37	such	such	DET
fcis-6030	116	38	a	a	DET
fcis-6030	116	39	simplified	simplified	ADJ
fcis-6030	116	40	objective	objective	ADJ
fcis-6030	116	41	function	function	NOUN
fcis-6030	116	42	,	,	PUNCT
fcis-6030	116	43	according	accord	VERB
fcis-6030	116	44	to	to	ADP
fcis-6030	116	45	ho	ho	PROPN
fcis-6030	116	46	et	et	PROPN
fcis-6030	116	47	al	al	PROPN
fcis-6030	116	48	.	.	PUNCT
fcis-6030	117	1	[	[	X
fcis-6030	117	2	11	11	NUM
fcis-6030	117	3	]	]	PUNCT
fcis-6030	117	4	.	.	PUNCT
fcis-6030	118	1	similar	similar	ADJ
fcis-6030	118	2	to	to	ADP
fcis-6030	118	3	gan	gan	VERB
fcis-6030	118	4	-	-	PUNCT
fcis-6030	118	5	based	base	VERB
fcis-6030	118	6	image	image	NOUN
fcis-6030	118	7	synthesis	synthesis	NOUN
fcis-6030	118	8	,	,	PUNCT
fcis-6030	118	9	diffusion	diffusion	NOUN
fcis-6030	118	10	-	-	PUNCT
fcis-6030	118	11	based	base	VERB
fcis-6030	118	12	image	image	NOUN
fcis-6030	118	13	generation	generation	NOUN
fcis-6030	118	14	can	can	AUX
fcis-6030	118	15	be	be	AUX
fcis-6030	118	16	conditional	conditional	ADJ
fcis-6030	118	17	.	.	PUNCT
fcis-6030	119	1	conditional	conditional	ADJ
fcis-6030	119	2	diffusion	diffusion	NOUN
fcis-6030	119	3	models	model	NOUN
fcis-6030	119	4	predict	predict	VERB
fcis-6030	119	5	noise	noise	NOUN
fcis-6030	119	6	∈	∈	PROPN
fcis-6030	119	7	θ	θ	NOUN
fcis-6030	119	8	not	not	PART
fcis-6030	119	9	only	only	ADV
fcis-6030	119	10	based	base	VERB
fcis-6030	119	11	on	on	ADP
fcis-6030	119	12	the	the	DET
fcis-6030	119	13	sampled	sample	VERB
fcis-6030	119	14	sequence	sequence	NOUN
fcis-6030	119	15	but	but	CCONJ
fcis-6030	119	16	also	also	ADV
fcis-6030	119	17	under	under	ADP
fcis-6030	119	18	the	the	DET
fcis-6030	119	19	guidance	guidance	NOUN
fcis-6030	119	20	of	of	ADP
fcis-6030	119	21	conditioning	condition	VERB
fcis-6030	119	22	information	information	NOUN
fcis-6030	119	23	c.	c.	NOUN
fcis-6030	119	24	therefore	therefore	ADV
fcis-6030	119	25	,	,	PUNCT
fcis-6030	119	26	the	the	DET
fcis-6030	119	27	optimization	optimization	NOUN
fcis-6030	119	28	objective	objective	NOUN
fcis-6030	119	29	becomes	become	VERB
fcis-6030	119	30	:	:	PUNCT
fcis-6030	119	31	lsimple	lsimple	ADJ
fcis-6030	119	32	(	(	PUNCT
fcis-6030	119	33	θ	θ	NOUN
fcis-6030	119	34	)	)	PUNCT
fcis-6030	119	35	=	=	SYM
fcis-6030	119	36	e	e	X
fcis-6030	120	1	[	[	X
fcis-6030	120	2	||ϵ	||ϵ	X
fcis-6030	120	3	−	−	PROPN
fcis-6030	120	4	ϵθ(xt	ϵθ(xt	PROPN
fcis-6030	120	5	,	,	PUNCT
fcis-6030	120	6	t	t	PROPN
fcis-6030	120	7	,	,	PUNCT
fcis-6030	120	8	c	c	NOUN
fcis-6030	120	9	)	)	PUNCT
fcis-6030	120	10	||2	||2	NOUN
fcis-6030	120	11	]	]	PUNCT
fcis-6030	120	12	(	(	PUNCT
fcis-6030	120	13	20	20	NUM
fcis-6030	120	14	)	)	SYM
fcis-6030	120	15	2	2	NUM
fcis-6030	120	16	)	)	PUNCT
fcis-6030	120	17	efficient	efficient	ADJ
fcis-6030	120	18	unet	unet	NOUN
fcis-6030	120	19	decoder	decoder	NOUN
fcis-6030	120	20	:	:	PUNCT
fcis-6030	120	21	the	the	DET
fcis-6030	120	22	decoder	decoder	NOUN
fcis-6030	120	23	in	in	ADP
fcis-6030	120	24	imagen	imagen	NOUN
fcis-6030	120	25	conditions	condition	NOUN
fcis-6030	120	26	the	the	DET
fcis-6030	120	27	text	text	NOUN
fcis-6030	120	28	embedding	embed	VERB
fcis-6030	120	29	extracted	extract	VERB
fcis-6030	120	30	by	by	ADP
fcis-6030	120	31	the	the	DET
fcis-6030	120	32	encoder	encoder	NOUN
fcis-6030	120	33	and	and	CCONJ
fcis-6030	120	34	synthesizes	synthesize	VERB
fcis-6030	120	35	an	an	DET
fcis-6030	120	36	image	image	NOUN
fcis-6030	120	37	matching	match	VERB
fcis-6030	120	38	the	the	DET
fcis-6030	120	39	description	description	NOUN
fcis-6030	120	40	.	.	PUNCT
fcis-6030	121	1	it	it	PRON
fcis-6030	121	2	follows	follow	VERB
fcis-6030	121	3	a	a	DET
fcis-6030	121	4	cascaded	cascade	VERB
fcis-6030	121	5	structure	structure	NOUN
fcis-6030	121	6	consisting	consist	VERB
fcis-6030	121	7	of	of	ADP
fcis-6030	121	8	three	three	NUM
fcis-6030	121	9	diffusion	diffusion	NOUN
fcis-6030	121	10	models	model	NOUN
fcis-6030	121	11	.	.	PUNCT
fcis-6030	122	1	the	the	DET
fcis-6030	122	2	base	base	NOUN
fcis-6030	122	3	model	model	NOUN
fcis-6030	122	4	maps	map	VERB
fcis-6030	122	5	the	the	DET
fcis-6030	122	6	text	text	NOUN
fcis-6030	122	7	embedding	embed	VERB
fcis-6030	122	8	into	into	ADP
fcis-6030	122	9	a	a	DET
fcis-6030	122	10	low	low	ADJ
fcis-6030	122	11	resolution	resolution	NOUN
fcis-6030	122	12	64	64	NUM
fcis-6030	122	13	x	x	SYM
fcis-6030	122	14	64	64	NUM
fcis-6030	122	15	image	image	NOUN
fcis-6030	122	16	,	,	PUNCT
fcis-6030	122	17	which	which	PRON
fcis-6030	122	18	is	be	AUX
fcis-6030	122	19	then	then	ADV
fcis-6030	122	20	passed	pass	VERB
fcis-6030	122	21	to	to	ADP
fcis-6030	122	22	a	a	DET
fcis-6030	122	23	64×64→256×256	64×64→256×256	NUM
fcis-6030	122	24	and	and	CCONJ
fcis-6030	122	25	a	a	DET
fcis-6030	122	26	256×256→1024×1024	256×256→1024×1024	NUM
fcis-6030	122	27	diffusion	diffusion	NOUN
fcis-6030	122	28	super	super	ADJ
fcis-6030	122	29	-	-	ADJ
fcis-6030	122	30	resolution	resolution	ADJ
fcis-6030	122	31	model	model	NOUN
fcis-6030	122	32	.	.	PUNCT
fcis-6030	123	1	all	all	DET
fcis-6030	123	2	three	three	NUM
fcis-6030	123	3	models	model	NOUN
fcis-6030	123	4	are	be	AUX
fcis-6030	123	5	based	base	VERB
fcis-6030	123	6	on	on	ADP
fcis-6030	123	7	an	an	DET
fcis-6030	123	8	unet	unet	NOUN
fcis-6030	123	9	architecture	architecture	NOUN
fcis-6030	123	10	.	.	PUNCT
fcis-6030	124	1	the	the	DET
fcis-6030	124	2	base	base	NOUN
fcis-6030	124	3	unet	unet	NOUN
fcis-6030	124	4	takes	take	VERB
fcis-6030	124	5	the	the	DET
fcis-6030	124	6	noisy	noisy	ADJ
fcis-6030	124	7	data	datum	NOUN
fcis-6030	124	8	xt	xt	ADV
fcis-6030	124	9	in	in	ADP
fcis-6030	124	10	a	a	DET
fcis-6030	124	11	sampling	sample	VERB
fcis-6030	124	12	sequence	sequence	NOUN
fcis-6030	124	13	as	as	ADP
fcis-6030	124	14	an	an	DET
fcis-6030	124	15	input	input	NOUN
fcis-6030	124	16	,	,	PUNCT
fcis-6030	124	17	and	and	CCONJ
fcis-6030	124	18	the	the	DET
fcis-6030	124	19	output	output	NOUN
fcis-6030	124	20	is	be	AUX
fcis-6030	124	21	the	the	DET
fcis-6030	124	22	predicted	predict	VERB
fcis-6030	124	23	noise	noise	NOUN
fcis-6030	124	24	∈	∈	PROPN
fcis-6030	124	25	θ	θ	PROPN
fcis-6030	124	26	.	.	PROPN
fcis-6030	125	1	for	for	ADP
fcis-6030	125	2	the	the	DET
fcis-6030	125	3	two	two	NUM
fcis-6030	125	4	superresolution	superresolution	NOUN
fcis-6030	125	5	unets	unet	NOUN
fcis-6030	125	6	,	,	PUNCT
fcis-6030	125	7	noisy	noisy	ADJ
fcis-6030	125	8	data	datum	NOUN
fcis-6030	125	9	xt	xt	VERB
fcis-6030	125	10	is	be	AUX
fcis-6030	125	11	first	first	ADV
fcis-6030	125	12	concatenated	concatenate	VERB
fcis-6030	125	13	with	with	ADP
fcis-6030	125	14	the	the	DET
fcis-6030	125	15	resized	resize	VERB
fcis-6030	125	16	low	low	ADJ
fcis-6030	125	17	-	-	PUNCT
fcis-6030	125	18	resolution	resolution	NOUN
fcis-6030	125	19	image	image	NOUN
fcis-6030	125	20	and	and	CCONJ
fcis-6030	125	21	then	then	ADV
fcis-6030	125	22	fed	feed	VERB
fcis-6030	125	23	into	into	ADP
fcis-6030	125	24	the	the	DET
fcis-6030	125	25	networks	network	NOUN
fcis-6030	125	26	.	.	PUNCT
fcis-6030	126	1	the	the	DET
fcis-6030	126	2	cross	cross	ADJ
fcis-6030	126	3	-	-	ADJ
fcis-6030	126	4	attention	attention	ADJ
fcis-6030	126	5	mechanism	mechanism	NOUN
fcis-6030	126	6	is	be	AUX
fcis-6030	126	7	added	add	VERB
fcis-6030	126	8	to	to	ADP
fcis-6030	126	9	all	all	DET
fcis-6030	126	10	three	three	NUM
fcis-6030	126	11	unets	unet	NOUN
fcis-6030	126	12	to	to	PART
fcis-6030	126	13	condition	condition	VERB
fcis-6030	126	14	text	text	NOUN
fcis-6030	126	15	embeddings	embedding	NOUN
fcis-6030	126	16	.	.	PUNCT
fcis-6030	127	1	in	in	ADP
fcis-6030	127	2	attention	attention	NOUN
fcis-6030	127	3	heads	head	NOUN
fcis-6030	127	4	,	,	PUNCT
fcis-6030	127	5	queries	query	NOUN
fcis-6030	127	6	are	be	AUX
fcis-6030	127	7	derived	derive	VERB
fcis-6030	127	8	from	from	ADP
fcis-6030	127	9	features	feature	NOUN
fcis-6030	127	10	extracted	extract	VERB
fcis-6030	127	11	from	from	ADP
fcis-6030	127	12	xt	xt	NOUN
fcis-6030	127	13	while	while	SCONJ
fcis-6030	127	14	text	text	NOUN
fcis-6030	127	15	embedding	embed	VERB
fcis-6030	127	16	provides	provide	VERB
fcis-6030	127	17	key	key	ADJ
fcis-6030	127	18	-	-	PUNCT
fcis-6030	127	19	value	value	NOUN
fcis-6030	127	20	pairs	pair	NOUN
fcis-6030	127	21	.	.	PUNCT
fcis-6030	128	1	we	we	PRON
fcis-6030	128	2	only	only	ADV
fcis-6030	128	3	utilize	utilize	VERB
fcis-6030	128	4	the	the	DET
fcis-6030	128	5	first	first	ADJ
fcis-6030	128	6	two	two	NUM
fcis-6030	128	7	unets	unet	NOUN
fcis-6030	128	8	of	of	ADP
fcis-6030	128	9	imagen	imagen	NOUN
fcis-6030	128	10	,	,	PUNCT
fcis-6030	128	11	so	so	ADV
fcis-6030	128	12	the	the	DET
fcis-6030	128	13	size	size	NOUN
fcis-6030	128	14	of	of	ADP
fcis-6030	128	15	generated	generate	VERB
fcis-6030	128	16	paintings	painting	NOUN
fcis-6030	128	17	is	be	AUX
fcis-6030	128	18	256	256	NUM
fcis-6030	128	19	×	×	NOUN
fcis-6030	128	20	256	256	NUM
fcis-6030	128	21	.	.	PUNCT
fcis-6030	129	1	in	in	ADP
fcis-6030	129	2	the	the	DET
fcis-6030	129	3	work	work	NOUN
fcis-6030	129	4	of	of	ADP
fcis-6030	129	5	[	[	X
fcis-6030	129	6	1	1	NUM
fcis-6030	129	7	]	]	PUNCT
fcis-6030	129	8	,	,	PUNCT
fcis-6030	129	9	unets	unet	NOUN
fcis-6030	129	10	are	be	AUX
fcis-6030	129	11	heavy	heavy	ADV
fcis-6030	129	12	-	-	PUNCT
fcis-6030	129	13	weighted	weight	VERB
fcis-6030	129	14	with	with	ADP
fcis-6030	129	15	a	a	DET
fcis-6030	129	16	large	large	ADJ
fcis-6030	129	17	number	number	NOUN
fcis-6030	129	18	of	of	ADP
fcis-6030	129	19	parameters	parameter	NOUN
fcis-6030	129	20	:	:	PUNCT
fcis-6030	129	21	the	the	DET
fcis-6030	129	22	64	64	NUM
fcis-6030	129	23	x	x	SYM
fcis-6030	129	24	64	64	NUM
fcis-6030	129	25	base	base	NOUN
fcis-6030	129	26	unet	unet	NOUN
fcis-6030	129	27	contains	contain	VERB
fcis-6030	129	28	2b	2b	NUM
fcis-6030	129	29	parameters	parameter	NOUN
fcis-6030	129	30	while	while	SCONJ
fcis-6030	129	31	the	the	DET
fcis-6030	129	32	64	64	NUM
fcis-6030	129	33	×	×	NOUN
fcis-6030	129	34	64	64	NUM
fcis-6030	129	35	→	→	SYM
fcis-6030	129	36	256	256	NUM
fcis-6030	129	37	×	×	NOUN
fcis-6030	129	38	256	256	NUM
fcis-6030	129	39	super	super	ADJ
fcis-6030	129	40	-	-	ADJ
fcis-6030	129	41	resolution	resolution	ADJ
fcis-6030	129	42	unet	unet	NOUN
fcis-6030	129	43	contains	contain	VERB
fcis-6030	129	44	400	400	NUM
fcis-6030	129	45	m	m	PROPN
fcis-6030	129	46	parameters	parameter	NOUN
fcis-6030	129	47	.	.	PUNCT
fcis-6030	130	1	such	such	ADJ
fcis-6030	130	2	large	large	ADJ
fcis-6030	130	3	networks	network	NOUN
fcis-6030	130	4	not	not	PART
fcis-6030	130	5	only	only	ADV
fcis-6030	130	6	result	result	VERB
fcis-6030	130	7	in	in	ADP
fcis-6030	130	8	difficulties	difficulty	NOUN
fcis-6030	130	9	in	in	ADP
fcis-6030	130	10	training	training	NOUN
fcis-6030	130	11	and	and	CCONJ
fcis-6030	130	12	sampling	sampling	NOUN
fcis-6030	130	13	but	but	CCONJ
fcis-6030	130	14	also	also	ADV
fcis-6030	130	15	cause	cause	VERB
fcis-6030	130	16	overfitting	overfitte	VERB
fcis-6030	130	17	problem	problem	NOUN
fcis-6030	130	18	when	when	SCONJ
fcis-6030	130	19	training	train	VERB
fcis-6030	130	20	on	on	ADP
fcis-6030	130	21	small	small	ADJ
fcis-6030	130	22	datasets	dataset	NOUN
fcis-6030	130	23	.	.	PUNCT
fcis-6030	131	1	for	for	ADP
fcis-6030	131	2	text	text	NOUN
fcis-6030	131	3	-	-	PUNCT
fcis-6030	131	4	to	to	ADP
fcis-6030	131	5	-	-	PUNCT
fcis-6030	131	6	classic	classic	ADJ
fcis-6030	131	7	synthesis	synthesis	NOUN
fcis-6030	131	8	,	,	PUNCT
fcis-6030	131	9	image	image	NOUN
fcis-6030	131	10	-	-	PUNCT
fcis-6030	131	11	description	description	NOUN
fcis-6030	131	12	pairs	pair	NOUN
fcis-6030	131	13	are	be	AUX
fcis-6030	131	14	much	much	ADV
fcis-6030	131	15	less	less	ADJ
fcis-6030	131	16	than	than	ADP
fcis-6030	131	17	common	common	ADJ
fcis-6030	131	18	scene	scene	NOUN
fcis-6030	131	19	datasets	dataset	NOUN
fcis-6030	131	20	such	such	ADJ
fcis-6030	131	21	as	as	ADP
fcis-6030	131	22	coco	coco	PROPN
fcis-6030	131	23	[	[	X
fcis-6030	131	24	11	11	NUM
fcis-6030	131	25	]	]	PUNCT
fcis-6030	131	26	.	.	PUNCT
fcis-6030	132	1	therefore	therefore	ADV
fcis-6030	132	2	,	,	PUNCT
fcis-6030	132	3	a	a	DET
fcis-6030	132	4	lighter	light	ADJ
fcis-6030	132	5	-	-	PUNCT
fcis-6030	132	6	weight	weight	NOUN
fcis-6030	132	7	imagen	imagen	NOUN
fcis-6030	132	8	is	be	AUX
fcis-6030	132	9	needed	need	VERB
fcis-6030	132	10	for	for	ADP
fcis-6030	132	11	this	this	DET
fcis-6030	132	12	task	task	NOUN
fcis-6030	132	13	.	.	PUNCT
fcis-6030	133	1	we	we	PRON
fcis-6030	133	2	compress	compress	VERB
fcis-6030	133	3	the	the	DET
fcis-6030	133	4	network	network	NOUN
fcis-6030	133	5	by	by	ADP
fcis-6030	133	6	dimensionality	dimensionality	NOUN
fcis-6030	133	7	shrinking	shrinking	NOUN
fcis-6030	133	8	of	of	ADP
fcis-6030	133	9	feature	feature	NOUN
fcis-6030	133	10	maps	map	NOUN
fcis-6030	133	11	in	in	ADP
fcis-6030	133	12	the	the	DET
fcis-6030	133	13	unet	unet	NOUN
fcis-6030	133	14	pyramid	pyramid	NOUN
fcis-6030	133	15	.	.	PUNCT
fcis-6030	134	1	we	we	PRON
fcis-6030	134	2	reduce	reduce	VERB
fcis-6030	134	3	the	the	DET
fcis-6030	134	4	dimension	dimension	NOUN
fcis-6030	134	5	of	of	ADP
fcis-6030	134	6	feature	feature	NOUN
fcis-6030	134	7	maps	map	NOUN
fcis-6030	134	8	in	in	ADP
fcis-6030	134	9	two	two	NUM
fcis-6030	134	10	ways	way	NOUN
fcis-6030	134	11	:	:	PUNCT
fcis-6030	134	12	decreasing	decrease	VERB
fcis-6030	134	13	the	the	DET
fcis-6030	134	14	base	base	NOUN
fcis-6030	134	15	dimension	dimension	NOUN
fcis-6030	134	16	,	,	PUNCT
fcis-6030	134	17	and	and	CCONJ
fcis-6030	134	18	decrementing	decremente	VERB
fcis-6030	134	19	the	the	DET
fcis-6030	134	20	multiplication	multiplication	NOUN
fcis-6030	134	21	factor	factor	NOUN
fcis-6030	134	22	in	in	ADP
fcis-6030	134	23	each	each	DET
fcis-6030	134	24	layer	layer	NOUN
fcis-6030	134	25	.	.	PUNCT
fcis-6030	135	1	by	by	ADP
fcis-6030	135	2	shrinking	shrink	VERB
fcis-6030	135	3	the	the	DET
fcis-6030	135	4	dimension	dimension	NOUN
fcis-6030	135	5	of	of	ADP
fcis-6030	135	6	feature	feature	NOUN
fcis-6030	135	7	maps	map	NOUN
fcis-6030	135	8	,	,	PUNCT
fcis-6030	135	9	the	the	DET
fcis-6030	135	10	network	network	NOUN
fcis-6030	135	11	can	can	AUX
fcis-6030	135	12	be	be	AUX
fcis-6030	135	13	efficiently	efficiently	ADV
fcis-6030	135	14	compressed	compress	VERB
fcis-6030	135	15	while	while	SCONJ
fcis-6030	135	16	the	the	DET
fcis-6030	135	17	multiscale	multiscale	ADJ
fcis-6030	135	18	information	information	NOUN
fcis-6030	135	19	can	can	AUX
fcis-6030	135	20	be	be	AUX
fcis-6030	135	21	preserved	preserve	VERB
fcis-6030	135	22	.	.	PUNCT
fcis-6030	136	1	for	for	ADP
fcis-6030	136	2	columns	column	NOUN
fcis-6030	136	3	,	,	PUNCT
fcis-6030	136	4	from	from	ADP
fcis-6030	136	5	left	leave	VERB
fcis-6030	136	6	to	to	ADP
fcis-6030	136	7	right	right	ADJ
fcis-6030	136	8	are	be	AUX
fcis-6030	136	9	:	:	PUNCT
fcis-6030	136	10	text	text	NOUN
fcis-6030	136	11	description	description	NOUN
fcis-6030	136	12	of	of	ADP
fcis-6030	136	13	art	art	NOUN
fcis-6030	136	14	paintings	painting	NOUN
fcis-6030	136	15	,	,	PUNCT
fcis-6030	136	16	ground	ground	NOUN
fcis-6030	136	17	truth	truth	NOUN
fcis-6030	136	18	image	image	NOUN
fcis-6030	136	19	of	of	ADP
fcis-6030	136	20	paintings	painting	NOUN
fcis-6030	136	21	,	,	PUNCT
fcis-6030	136	22	64	64	NUM
fcis-6030	136	23	×	×	NOUN
fcis-6030	136	24	64	64	NUM
fcis-6030	136	25	intermediary	intermediary	ADJ
fcis-6030	136	26	image	image	NOUN
fcis-6030	136	27	output	output	NOUN
fcis-6030	136	28	by	by	ADP
fcis-6030	136	29	the	the	DET
fcis-6030	136	30	base	base	NOUN
fcis-6030	136	31	unet	unet	NOUN
fcis-6030	136	32	,	,	PUNCT
fcis-6030	136	33	and	and	CCONJ
fcis-6030	136	34	the	the	DET
fcis-6030	136	35	final	final	ADJ
fcis-6030	136	36	256	256	NUM
fcis-6030	136	37	×	×	NOUN
fcis-6030	136	38	256	256	NUM
fcis-6030	136	39	synthesized	synthesize	VERB
fcis-6030	136	40	images	image	NOUN
fcis-6030	136	41	.	.	PUNCT
fcis-6030	137	1	88	88	NUM
fcis-6030	137	2	figure	figure	NOUN
fcis-6030	137	3	3	3	NUM
fcis-6030	137	4	.	.	PUNCT
fcis-6030	137	5	qualitative	qualitative	ADJ
fcis-6030	137	6	results	result	NOUN
fcis-6030	137	7	of	of	ADP
fcis-6030	137	8	our	our	PRON
fcis-6030	137	9	method	method	NOUN
fcis-6030	137	10	4	4	NUM
fcis-6030	137	11	.	.	PUNCT
fcis-6030	137	12	experiments	experiment	NOUN
fcis-6030	137	13	4.1	4.1	NUM
fcis-6030	137	14	.	.	PUNCT
fcis-6030	137	15	dataset	dataset	VERB
fcis-6030	137	16	our	our	PRON
fcis-6030	137	17	t2c	t2c	PROPN
fcis-6030	137	18	model	model	NOUN
fcis-6030	137	19	is	be	AUX
fcis-6030	137	20	trained	train	VERB
fcis-6030	137	21	and	and	CCONJ
fcis-6030	137	22	tested	test	VERB
fcis-6030	137	23	in	in	ADP
fcis-6030	137	24	semart	semart	NOUN
fcis-6030	137	25	dataset	dataset	VERB
fcis-6030	138	1	[	[	X
fcis-6030	138	2	12	12	NUM
fcis-6030	138	3	]	]	PUNCT
fcis-6030	138	4	.	.	PUNCT
fcis-6030	139	1	semart	semart	PROPN
fcis-6030	139	2	is	be	AUX
fcis-6030	139	3	a	a	DET
fcis-6030	139	4	collection	collection	NOUN
fcis-6030	139	5	of	of	ADP
fcis-6030	139	6	classic	classic	ADJ
fcis-6030	139	7	art	art	NOUN
fcis-6030	139	8	painting	painting	NOUN
fcis-6030	139	9	images	image	NOUN
fcis-6030	139	10	.	.	PUNCT
fcis-6030	140	1	it	it	PRON
fcis-6030	140	2	contains	contain	VERB
fcis-6030	140	3	21,384	21,384	NUM
fcis-6030	140	4	samples	sample	NOUN
fcis-6030	140	5	and	and	CCONJ
fcis-6030	140	6	each	each	DET
fcis-6030	140	7	sample	sample	NOUN
fcis-6030	140	8	consists	consist	VERB
fcis-6030	140	9	of	of	ADP
fcis-6030	140	10	a	a	DET
fcis-6030	140	11	classic	classic	ADJ
fcis-6030	140	12	art	art	NOUN
fcis-6030	140	13	painting	painting	NOUN
fcis-6030	140	14	and	and	CCONJ
fcis-6030	140	15	its	its	PRON
fcis-6030	140	16	information	information	NOUN
fcis-6030	140	17	in	in	ADP
fcis-6030	140	18	texts	text	NOUN
fcis-6030	140	19	.	.	PUNCT
fcis-6030	141	1	the	the	DET
fcis-6030	141	2	21,384	21,384	NUM
fcis-6030	141	3	samples	sample	NOUN
fcis-6030	141	4	are	be	AUX
fcis-6030	141	5	split	split	VERB
fcis-6030	141	6	into	into	ADP
fcis-6030	141	7	training	training	NOUN
fcis-6030	141	8	,	,	PUNCT
fcis-6030	141	9	validation	validation	NOUN
fcis-6030	141	10	,	,	PUNCT
fcis-6030	141	11	and	and	CCONJ
fcis-6030	141	12	test	test	NOUN
fcis-6030	141	13	sets	set	NOUN
fcis-6030	141	14	according	accord	VERB
fcis-6030	141	15	to	to	ADP
fcis-6030	141	16	a	a	DET
fcis-6030	141	17	ratio	ratio	NOUN
fcis-6030	141	18	of	of	ADP
fcis-6030	141	19	20:1:1	20:1:1	NUM
fcis-6030	141	20	,	,	PUNCT
fcis-6030	141	21	following	follow	VERB
fcis-6030	141	22	the	the	DET
fcis-6030	141	23	official	official	ADJ
fcis-6030	141	24	partition	partition	NOUN
fcis-6030	141	25	[	[	X
fcis-6030	141	26	12	12	NUM
fcis-6030	141	27	]	]	PUNCT
fcis-6030	141	28	.	.	PUNCT
fcis-6030	142	1	the	the	DET
fcis-6030	142	2	number	number	NOUN
fcis-6030	142	3	of	of	ADP
fcis-6030	142	4	samples	sample	NOUN
fcis-6030	142	5	in	in	ADP
fcis-6030	142	6	each	each	DET
fcis-6030	142	7	set	set	NOUN
fcis-6030	142	8	is	be	AUX
fcis-6030	142	9	summarized	summarize	VERB
fcis-6030	142	10	in	in	ADP
fcis-6030	142	11	table	table	NOUN
fcis-6030	142	12	1	1	NUM
fcis-6030	142	13	.	.	PUNCT
fcis-6030	142	14	table	table	NOUN
fcis-6030	142	15	1	1	NUM
fcis-6030	142	16	.	.	X
fcis-6030	143	1	fid	fid	NOUN
fcis-6030	143	2	achieved	achieve	VERB
fcis-6030	143	3	by	by	ADP
fcis-6030	143	4	base	base	NOUN
fcis-6030	143	5	unet	unet	NOUN
fcis-6030	143	6	with	with	ADP
fcis-6030	143	7	different	different	ADJ
fcis-6030	143	8	base	base	NOUN
fcis-6030	143	9	dimension	dimension	NOUN
fcis-6030	143	10	base	base	NOUN
fcis-6030	143	11	dimension	dimension	NOUN
fcis-6030	143	12	number	number	NOUN
fcis-6030	143	13	of	of	ADP
fcis-6030	143	14	parameters	parameter	NOUN
fcis-6030	143	15	fid	fid	VERB
fcis-6030	143	16	32	32	NUM
fcis-6030	143	17	8	8	NUM
fcis-6030	143	18	m	m	NOUN
fcis-6030	143	19	110.8821	110.8821	NUM
fcis-6030	143	20	64	64	NUM
fcis-6030	143	21	28	28	NUM
fcis-6030	143	22	m	m	NOUN
fcis-6030	143	23	78.3743	78.3743	NUM
fcis-6030	143	24	128	128	NUM
fcis-6030	143	25	106	106	NUM
fcis-6030	143	26	m	m	PROPN
fcis-6030	143	27	82.6170	82.6170	NUM
fcis-6030	143	28	256	256	NUM
fcis-6030	143	29	410	410	NUM
fcis-6030	143	30	m	m	NOUN
fcis-6030	143	31	71.2723	71.2723	NUM
fcis-6030	143	32	320	320	NUM
fcis-6030	143	33	635	635	NUM
fcis-6030	143	34	m	m	NUM
fcis-6030	143	35	71.3565	71.3565	NUM
fcis-6030	143	36	to	to	PART
fcis-6030	143	37	measure	measure	VERB
fcis-6030	143	38	the	the	DET
fcis-6030	143	39	performance	performance	NOUN
fcis-6030	143	40	of	of	ADP
fcis-6030	143	41	our	our	PRON
fcis-6030	143	42	t2c	t2c	NOUN
fcis-6030	143	43	quantitatively	quantitatively	ADV
fcis-6030	143	44	,	,	PUNCT
fcis-6030	143	45	we	we	PRON
fcis-6030	143	46	adopt	adopt	VERB
fcis-6030	143	47	the	the	DET
fcis-6030	143	48	popular	popular	ADJ
fcis-6030	143	49	metric	metric	ADJ
fcis-6030	143	50	fr	fr	PROPN
fcis-6030	143	51	chet	chet	PROPN
fcis-6030	143	52	inception	inception	PROPN
fcis-6030	143	53	distance	distance	NOUN
fcis-6030	143	54	(	(	PUNCT
fcis-6030	143	55	fid	fid	NOUN
fcis-6030	143	56	)	)	PUNCT
fcis-6030	143	57	.	.	PUNCT
fcis-6030	144	1	we	we	PRON
fcis-6030	144	2	use	use	VERB
fcis-6030	144	3	the	the	DET
fcis-6030	144	4	test	test	NOUN
fcis-6030	144	5	set	set	VERB
fcis-6030	144	6	to	to	PART
fcis-6030	144	7	calculate	calculate	VERB
fcis-6030	144	8	fid	fid	NOUN
fcis-6030	144	9	.	.	PUNCT
fcis-6030	144	10	4.2	4.2	NUM
fcis-6030	144	11	.	.	PUNCT
fcis-6030	145	1	experiment	experiment	NOUN
fcis-6030	145	2	setup	setup	NOUN
fcis-6030	145	3	different	different	ADJ
fcis-6030	145	4	dimensionality	dimensionality	NOUN
fcis-6030	145	5	configurations	configuration	NOUN
fcis-6030	145	6	of	of	ADP
fcis-6030	145	7	feature	feature	NOUN
fcis-6030	145	8	maps	map	NOUN
fcis-6030	145	9	are	be	AUX
fcis-6030	145	10	tested	test	VERB
fcis-6030	145	11	,	,	PUNCT
fcis-6030	145	12	to	to	PART
fcis-6030	145	13	measure	measure	VERB
fcis-6030	145	14	the	the	DET
fcis-6030	145	15	effect	effect	NOUN
fcis-6030	145	16	of	of	ADP
fcis-6030	145	17	network	network	NOUN
fcis-6030	145	18	compression	compression	NOUN
fcis-6030	145	19	.	.	PUNCT
fcis-6030	146	1	the	the	DET
fcis-6030	146	2	64	64	NUM
fcis-6030	146	3	×	×	NOUN
fcis-6030	146	4	64	64	NUM
fcis-6030	146	5	base	base	NOUN
fcis-6030	146	6	unet	unet	NOUN
fcis-6030	146	7	and	and	CCONJ
fcis-6030	146	8	the	the	DET
fcis-6030	146	9	64	64	NUM
fcis-6030	146	10	×	×	NOUN
fcis-6030	146	11	64	64	NUM
fcis-6030	146	12	→	→	SYM
fcis-6030	146	13	256	256	NUM
fcis-6030	146	14	×	×	NOUN
fcis-6030	146	15	256	256	NUM
fcis-6030	146	16	super	super	ADJ
fcis-6030	146	17	-	-	ADJ
fcis-6030	146	18	resolution	resolution	ADJ
fcis-6030	146	19	unet	unet	NOUN
fcis-6030	146	20	are	be	AUX
fcis-6030	146	21	tested	test	VERB
fcis-6030	146	22	separately	separately	ADV
fcis-6030	146	23	.	.	PUNCT
fcis-6030	147	1	pyramids	pyramid	NOUN
fcis-6030	147	2	of	of	ADP
fcis-6030	147	3	both	both	DET
fcis-6030	147	4	unets	unet	NOUN
fcis-6030	147	5	have	have	VERB
fcis-6030	147	6	4	4	NUM
fcis-6030	147	7	layers	layer	NOUN
fcis-6030	147	8	,	,	PUNCT
fcis-6030	147	9	and	and	CCONJ
fcis-6030	147	10	feature	feature	NOUN
fcis-6030	147	11	maps	map	NOUN
fcis-6030	147	12	of	of	ADP
fcis-6030	147	13	the	the	DET
fcis-6030	147	14	first	first	ADJ
fcis-6030	147	15	layer	layer	NOUN
fcis-6030	147	16	have	have	VERB
fcis-6030	147	17	a	a	DET
fcis-6030	147	18	base	base	ADJ
fcis-6030	147	19	dimension	dimension	NOUN
fcis-6030	147	20	.	.	PUNCT
fcis-6030	148	1	for	for	ADP
fcis-6030	148	2	each	each	PRON
fcis-6030	148	3	of	of	ADP
fcis-6030	148	4	the	the	DET
fcis-6030	148	5	rest	rest	NOUN
fcis-6030	148	6	of	of	ADP
fcis-6030	148	7	the	the	DET
fcis-6030	148	8	layers	layer	NOUN
fcis-6030	148	9	,	,	PUNCT
fcis-6030	148	10	the	the	DET
fcis-6030	148	11	dimension	dimension	NOUN
fcis-6030	148	12	of	of	ADP
fcis-6030	148	13	feature	feature	NOUN
fcis-6030	148	14	maps	map	NOUN
fcis-6030	148	15	is	be	AUX
fcis-6030	148	16	the	the	DET
fcis-6030	148	17	multiple	multiple	NOUN
fcis-6030	148	18	of	of	ADP
fcis-6030	148	19	those	those	PRON
fcis-6030	148	20	in	in	ADP
fcis-6030	148	21	its	its	PRON
fcis-6030	148	22	last	last	ADJ
fcis-6030	148	23	layer	layer	NOUN
fcis-6030	148	24	.	.	PUNCT
fcis-6030	149	1	di	di	NOUN
fcis-6030	150	1	=	=	NOUN
fcis-6030	150	2	αi	αi	PROPN
fcis-6030	150	3	×	×	PROPN
fcis-6030	150	4	di−1	di−1	PROPN
fcis-6030	150	5	,	,	PUNCT
fcis-6030	150	6	i	i	NOUN
fcis-6030	150	7	=	=	NOUN
fcis-6030	150	8	2	2	NUM
fcis-6030	150	9	,	,	PUNCT
fcis-6030	150	10	3	3	NUM
fcis-6030	150	11	,	,	PUNCT
fcis-6030	150	12	4	4	NUM
fcis-6030	150	13	(	(	PUNCT
fcis-6030	150	14	21	21	NUM
fcis-6030	150	15	)	)	PUNCT
fcis-6030	150	16	where	where	SCONJ
fcis-6030	150	17	di	di	NOUN
fcis-6030	150	18	is	be	AUX
fcis-6030	150	19	the	the	DET
fcis-6030	150	20	dimension	dimension	NOUN
fcis-6030	150	21	of	of	ADP
fcis-6030	150	22	feature	feature	NOUN
fcis-6030	150	23	maps	map	NOUN
fcis-6030	150	24	in	in	ADP
fcis-6030	150	25	layer	layer	NOUN
fcis-6030	150	26	-	-	PUNCT
fcis-6030	150	27	i	i	NOUN
fcis-6030	150	28	,	,	PUNCT
fcis-6030	150	29	and	and	CCONJ
fcis-6030	150	30	αi	αi	VERB
fcis-6030	150	31	is	be	AUX
fcis-6030	150	32	the	the	DET
fcis-6030	150	33	multiplication	multiplication	NOUN
fcis-6030	150	34	factor	factor	NOUN
fcis-6030	150	35	.	.	PUNCT
fcis-6030	151	1	the	the	DET
fcis-6030	151	2	multiplication	multiplication	NOUN
fcis-6030	151	3	factor	factor	NOUN
fcis-6030	151	4	is	be	AUX
fcis-6030	151	5	set	set	VERB
fcis-6030	151	6	to	to	PART
fcis-6030	151	7	be	be	AUX
fcis-6030	151	8	α2	α2	ADJ
fcis-6030	151	9	=	=	SYM
fcis-6030	151	10	2	2	NUM
fcis-6030	151	11	,	,	PUNCT
fcis-6030	151	12	α3	α3	NOUN
fcis-6030	151	13	=	=	SYM
fcis-6030	151	14	3	3	NUM
fcis-6030	151	15	,	,	PUNCT
fcis-6030	151	16	α4	α4	NOUN
fcis-6030	151	17	=	=	SYM
fcis-6030	151	18	4	4	X
fcis-6030	151	19	.	.	PUNCT
fcis-6030	152	1	different	different	ADJ
fcis-6030	152	2	base	base	NOUN
fcis-6030	152	3	dimensions	dimension	NOUN
fcis-6030	152	4	d1	d1	PROPN
fcis-6030	152	5	are	be	AUX
fcis-6030	152	6	explored	explore	VERB
fcis-6030	152	7	.	.	PUNCT
fcis-6030	153	1	two	two	NUM
fcis-6030	153	2	unets	unet	NOUN
fcis-6030	153	3	are	be	AUX
fcis-6030	153	4	evaluated	evaluate	VERB
fcis-6030	153	5	separately	separately	ADV
fcis-6030	153	6	,	,	PUNCT
fcis-6030	153	7	and	and	CCONJ
fcis-6030	153	8	the	the	DET
fcis-6030	153	9	superresolution	superresolution	NOUN
fcis-6030	153	10	unet	unet	NOUN
fcis-6030	153	11	samples	sample	NOUN
fcis-6030	153	12	on	on	ADP
fcis-6030	153	13	resized	resize	VERB
fcis-6030	153	14	64×64	64×64	NUM
fcis-6030	153	15	ground	ground	NOUN
fcis-6030	153	16	-	-	PUNCT
fcis-6030	153	17	truth	truth	NOUN
fcis-6030	153	18	images	image	NOUN
fcis-6030	153	19	.	.	PUNCT
fcis-6030	154	1	table	table	NOUN
fcis-6030	154	2	2	2	NUM
fcis-6030	154	3	.	.	X
fcis-6030	154	4	fid	fid	NOUN
fcis-6030	154	5	achieved	achieve	VERB
fcis-6030	154	6	by	by	ADP
fcis-6030	154	7	super	super	ADJ
fcis-6030	154	8	-	-	ADJ
fcis-6030	154	9	resolution	resolution	ADJ
fcis-6030	154	10	unet	unet	NOUN
fcis-6030	154	11	with	with	ADP
fcis-6030	154	12	different	different	ADJ
fcis-6030	154	13	base	base	NOUN
fcis-6030	154	14	dimension	dimension	NOUN
fcis-6030	154	15	base	base	NOUN
fcis-6030	154	16	dimension	dimension	NOUN
fcis-6030	154	17	number	number	NOUN
fcis-6030	154	18	of	of	ADP
fcis-6030	154	19	parameters	parameter	NOUN
fcis-6030	154	20	fid	fid	VERB
fcis-6030	154	21	32	32	NUM
fcis-6030	154	22	16	16	NUM
fcis-6030	154	23	m	m	PROPN
fcis-6030	154	24	110.8127	110.8127	NUM
fcis-6030	154	25	64	64	NUM
fcis-6030	154	26	61	61	NUM
fcis-6030	154	27	m	m	NUM
fcis-6030	154	28	68.2957	68.2957	NUM
fcis-6030	154	29	128	128	NUM
fcis-6030	154	30	241	241	NUM
fcis-6030	154	31	m	m	PROPN
fcis-6030	154	32	64.0859	64.0859	NUM
fcis-6030	154	33	192	192	NUM
fcis-6030	154	34	538	538	NUM
fcis-6030	154	35	m	m	NUM
fcis-6030	154	36	62.6451	62.6451	NUM
fcis-6030	154	37	4.3	4.3	NUM
fcis-6030	154	38	.	.	PUNCT
fcis-6030	155	1	quantitative	quantitative	ADJ
fcis-6030	155	2	results	result	NOUN
fcis-6030	155	3	as	as	SCONJ
fcis-6030	155	4	is	be	AUX
fcis-6030	155	5	shown	show	VERB
fcis-6030	155	6	in	in	ADP
fcis-6030	155	7	table	table	NOUN
fcis-6030	155	8	1	1	NUM
fcis-6030	155	9	,	,	PUNCT
fcis-6030	155	10	for	for	ADP
fcis-6030	155	11	the	the	DET
fcis-6030	155	12	base	base	NOUN
fcis-6030	155	13	text	text	NOUN
fcis-6030	155	14	-	-	PUNCT
fcis-6030	155	15	to	to	ADP
fcis-6030	155	16	-	-	PUNCT
fcis-6030	155	17	image	image	NOUN
fcis-6030	155	18	unet	unet	NOUN
fcis-6030	155	19	,	,	PUNCT
fcis-6030	155	20	shrinking	shrink	VERB
fcis-6030	155	21	the	the	DET
fcis-6030	155	22	base	base	NOUN
fcis-6030	155	23	dimension	dimension	NOUN
fcis-6030	155	24	leads	lead	VERB
fcis-6030	155	25	to	to	ADP
fcis-6030	155	26	a	a	DET
fcis-6030	155	27	small	small	ADJ
fcis-6030	155	28	loss	loss	NOUN
fcis-6030	155	29	in	in	ADP
fcis-6030	155	30	synthesis	synthesis	NOUN
fcis-6030	155	31	quality	quality	NOUN
fcis-6030	155	32	.	.	PUNCT
fcis-6030	156	1	however	however	ADV
fcis-6030	156	2	,	,	PUNCT
fcis-6030	156	3	the	the	DET
fcis-6030	156	4	fid	fid	NOUN
fcis-6030	156	5	value	value	NOUN
fcis-6030	156	6	is	be	AUX
fcis-6030	156	7	still	still	ADV
fcis-6030	156	8	reasonable	reasonable	ADJ
fcis-6030	156	9	when	when	SCONJ
fcis-6030	156	10	the	the	DET
fcis-6030	156	11	base	base	NOUN
fcis-6030	156	12	dimension	dimension	NOUN
fcis-6030	156	13	is	be	AUX
fcis-6030	156	14	no	no	DET
fcis-6030	156	15	less	less	ADJ
fcis-6030	156	16	than	than	ADP
fcis-6030	156	17	64	64	NUM
fcis-6030	156	18	.	.	PUNCT
fcis-6030	157	1	the	the	DET
fcis-6030	157	2	performance	performance	NOUN
fcis-6030	157	3	of	of	ADP
fcis-6030	157	4	unet	unet	NOUN
fcis-6030	157	5	2	2	NUM
fcis-6030	157	6	has	have	VERB
fcis-6030	157	7	a	a	DET
fcis-6030	157	8	similar	similar	ADJ
fcis-6030	157	9	tendency	tendency	NOUN
fcis-6030	157	10	to	to	ADP
fcis-6030	157	11	those	those	PRON
fcis-6030	157	12	of	of	ADP
fcis-6030	157	13	unet	unet	NOUN
fcis-6030	157	14	1	1	NUM
fcis-6030	157	15	,	,	PUNCT
fcis-6030	157	16	according	accord	VERB
fcis-6030	157	17	to	to	ADP
fcis-6030	157	18	the	the	DET
fcis-6030	157	19	results	result	NOUN
fcis-6030	157	20	in	in	ADP
fcis-6030	157	21	table	table	NOUN
fcis-6030	157	22	2	2	NUM
fcis-6030	157	23	.	.	PUNCT
fcis-6030	157	24	compressing	compress	VERB
fcis-6030	157	25	the	the	DET
fcis-6030	157	26	network	network	NOUN
fcis-6030	157	27	only	only	ADV
fcis-6030	157	28	results	result	VERB
fcis-6030	157	29	in	in	ADP
fcis-6030	157	30	a	a	DET
fcis-6030	157	31	slight	slight	ADJ
fcis-6030	157	32	drop	drop	NOUN
fcis-6030	157	33	in	in	ADP
fcis-6030	157	34	fid	fid	NOUN
fcis-6030	157	35	when	when	SCONJ
fcis-6030	157	36	the	the	DET
fcis-6030	157	37	base	base	NOUN
fcis-6030	157	38	dimension	dimension	NOUN
fcis-6030	157	39	is	be	AUX
fcis-6030	157	40	greater	great	ADJ
fcis-6030	157	41	than	than	ADP
fcis-6030	157	42	32	32	NUM
fcis-6030	157	43	.	.	PUNCT
fcis-6030	158	1	trading	trade	VERB
fcis-6030	158	2	off	off	ADP
fcis-6030	158	3	synthesis	synthesis	NOUN
fcis-6030	158	4	quality	quality	NOUN
fcis-6030	158	5	and	and	CCONJ
fcis-6030	158	6	the	the	DET
fcis-6030	158	7	number	number	NOUN
fcis-6030	158	8	of	of	ADP
fcis-6030	158	9	parameters	parameter	NOUN
fcis-6030	158	10	,	,	PUNCT
fcis-6030	158	11	the	the	DET
fcis-6030	158	12	combination	combination	NOUN
fcis-6030	158	13	of	of	ADP
fcis-6030	158	14	a	a	DET
fcis-6030	158	15	64dimensional	64dimensional	ADJ
fcis-6030	158	16	base	base	NOUN
fcis-6030	158	17	unet	unet	NOUN
fcis-6030	158	18	and	and	CCONJ
fcis-6030	158	19	a	a	DET
fcis-6030	158	20	64	64	NUM
fcis-6030	158	21	-	-	PUNCT
fcis-6030	158	22	dimensional	dimensional	ADJ
fcis-6030	158	23	super	super	ADJ
fcis-6030	158	24	-	-	ADJ
fcis-6030	158	25	resolution	resolution	ADJ
fcis-6030	158	26	unet	unet	NOUN
fcis-6030	158	27	is	be	AUX
fcis-6030	158	28	the	the	DET
fcis-6030	158	29	best	good	ADJ
fcis-6030	158	30	choice	choice	NOUN
fcis-6030	158	31	.	.	PUNCT
fcis-6030	159	1	4.4	4.4	NUM
fcis-6030	159	2	.	.	PUNCT
fcis-6030	160	1	qualitative	qualitative	ADJ
fcis-6030	160	2	results	result	NOUN
fcis-6030	160	3	in	in	ADP
fcis-6030	160	4	this	this	DET
fcis-6030	160	5	section	section	NOUN
fcis-6030	160	6	,	,	PUNCT
fcis-6030	160	7	qualitative	qualitative	ADJ
fcis-6030	160	8	results	result	NOUN
fcis-6030	160	9	of	of	ADP
fcis-6030	160	10	the	the	DET
fcis-6030	160	11	64	64	NUM
fcis-6030	160	12	-	-	SYM
fcis-6030	160	13	64	64	NUM
fcis-6030	160	14	combination	combination	NOUN
fcis-6030	160	15	are	be	AUX
fcis-6030	160	16	demonstrated	demonstrate	VERB
fcis-6030	160	17	.	.	PUNCT
fcis-6030	161	1	such	such	DET
fcis-6030	161	2	a	a	DET
fcis-6030	161	3	combination	combination	NOUN
fcis-6030	161	4	has	have	VERB
fcis-6030	161	5	only	only	ADV
fcis-6030	161	6	89	89	NUM
fcis-6030	161	7	m	m	NOUN
fcis-6030	161	8	parameters	parameter	NOUN
fcis-6030	161	9	,	,	PUNCT
fcis-6030	161	10	while	while	SCONJ
fcis-6030	161	11	the	the	DET
fcis-6030	161	12	heaviest	heavy	ADJ
fcis-6030	161	13	320	320	NUM
fcis-6030	161	14	-	-	SYM
fcis-6030	161	15	192	192	NUM
fcis-6030	161	16	combination	combination	NOUN
fcis-6030	161	17	consists	consist	VERB
fcis-6030	161	18	of	of	ADP
fcis-6030	161	19	1173	1173	NUM
fcis-6030	161	20	m	m	NOUN
fcis-6030	161	21	parameters	parameter	NOUN
fcis-6030	161	22	.	.	PUNCT
fcis-6030	162	1	the	the	DET
fcis-6030	162	2	64	64	NUM
fcis-6030	162	3	-	-	SYM
fcis-6030	162	4	64	64	NUM
fcis-6030	162	5	combination	combination	NOUN
fcis-6030	162	6	is	be	AUX
fcis-6030	162	7	only	only	ADV
fcis-6030	162	8	7.5	7.5	NUM
fcis-6030	162	9	%	%	NOUN
fcis-6030	162	10	of	of	ADP
fcis-6030	162	11	the	the	DET
fcis-6030	162	12	heaviest	heavy	ADJ
fcis-6030	162	13	combination	combination	NOUN
fcis-6030	162	14	.	.	PUNCT
fcis-6030	163	1	as	as	SCONJ
fcis-6030	163	2	is	be	AUX
fcis-6030	163	3	shown	show	VERB
fcis-6030	163	4	in	in	ADP
fcis-6030	163	5	figure	figure	NOUN
fcis-6030	163	6	3	3	NUM
fcis-6030	163	7	,	,	PUNCT
fcis-6030	163	8	despite	despite	SCONJ
fcis-6030	163	9	the	the	DET
fcis-6030	163	10	relatively	relatively	ADV
fcis-6030	163	11	small	small	ADJ
fcis-6030	163	12	number	number	NOUN
fcis-6030	163	13	of	of	ADP
fcis-6030	163	14	parameters	parameter	NOUN
fcis-6030	163	15	,	,	PUNCT
fcis-6030	163	16	our	our	PRON
fcis-6030	163	17	model	model	NOUN
fcis-6030	163	18	can	can	AUX
fcis-6030	163	19	still	still	ADV
fcis-6030	163	20	extract	extract	VERB
fcis-6030	163	21	information	information	NOUN
fcis-6030	163	22	from	from	ADP
fcis-6030	163	23	the	the	DET
fcis-6030	163	24	challenging	challenge	VERB
fcis-6030	163	25	text	text	NOUN
fcis-6030	163	26	description	description	NOUN
fcis-6030	163	27	and	and	CCONJ
fcis-6030	163	28	synthesize	synthesize	VERB
fcis-6030	163	29	a	a	DET
fcis-6030	163	30	reasonable	reasonable	ADJ
fcis-6030	163	31	art	art	NOUN
fcis-6030	163	32	painting	painting	NOUN
fcis-6030	163	33	according	accord	VERB
fcis-6030	163	34	to	to	ADP
fcis-6030	163	35	the	the	DET
fcis-6030	163	36	description	description	NOUN
fcis-6030	163	37	.	.	PUNCT
fcis-6030	164	1	5	5	X
fcis-6030	164	2	.	.	X
fcis-6030	164	3	conclusion	conclusion	NOUN
fcis-6030	164	4	previous	previous	ADJ
fcis-6030	164	5	researches	research	NOUN
fcis-6030	164	6	on	on	ADP
fcis-6030	164	7	text	text	NOUN
fcis-6030	164	8	-	-	PUNCT
fcis-6030	164	9	to	to	ADP
fcis-6030	164	10	-	-	PUNCT
fcis-6030	164	11	image	image	NOUN
fcis-6030	164	12	generation	generation	NOUN
fcis-6030	164	13	pays	pay	VERB
fcis-6030	164	14	little	little	ADJ
fcis-6030	164	15	attention	attention	NOUN
fcis-6030	164	16	to	to	ADP
fcis-6030	164	17	classical	classical	ADJ
fcis-6030	164	18	art	art	NOUN
fcis-6030	164	19	synthesis	synthesis	NOUN
fcis-6030	164	20	,	,	PUNCT
fcis-6030	164	21	this	this	DET
fcis-6030	164	22	paper	paper	NOUN
fcis-6030	164	23	fills	fill	VERB
fcis-6030	164	24	such	such	DET
fcis-6030	164	25	a	a	DET
fcis-6030	164	26	gap	gap	NOUN
fcis-6030	164	27	.	.	PUNCT
fcis-6030	165	1	in	in	ADP
fcis-6030	165	2	this	this	DET
fcis-6030	165	3	paper	paper	NOUN
fcis-6030	165	4	,	,	PUNCT
fcis-6030	165	5	we	we	PRON
fcis-6030	165	6	propose	propose	VERB
fcis-6030	165	7	a	a	DET
fcis-6030	165	8	light	light	ADJ
fcis-6030	165	9	-	-	PUNCT
fcis-6030	165	10	weighted	weight	VERB
fcis-6030	165	11	diffusion	diffusion	NOUN
fcis-6030	165	12	model	model	NOUN
fcis-6030	165	13	t2c	t2c	PROPN
fcis-6030	165	14	for	for	ADP
fcis-6030	165	15	text	text	NOUN
fcis-6030	165	16	-	-	PUNCT
fcis-6030	165	17	to	to	ADP
fcis-6030	165	18	-	-	PUNCT
fcis-6030	165	19	classic	classic	ADJ
fcis-6030	165	20	generation	generation	NOUN
fcis-6030	165	21	.	.	PUNCT
fcis-6030	166	1	we	we	PRON
fcis-6030	166	2	adopt	adopt	VERB
fcis-6030	166	3	imagen	imagen	NOUN
fcis-6030	166	4	as	as	ADP
fcis-6030	166	5	our	our	PRON
fcis-6030	166	6	framework	framework	NOUN
fcis-6030	166	7	.	.	PUNCT
fcis-6030	167	1	given	give	VERB
fcis-6030	167	2	a	a	DET
fcis-6030	167	3	text	text	NOUN
fcis-6030	167	4	description	description	NOUN
fcis-6030	167	5	,	,	PUNCT
fcis-6030	167	6	it	it	PRON
fcis-6030	167	7	is	be	AUX
fcis-6030	167	8	first	first	ADV
fcis-6030	167	9	encoded	encode	VERB
fcis-6030	167	10	into	into	ADP
fcis-6030	167	11	high	high	ADJ
fcis-6030	167	12	-	-	PUNCT
fcis-6030	167	13	level	level	NOUN
fcis-6030	167	14	features	feature	NOUN
fcis-6030	167	15	by	by	ADP
fcis-6030	167	16	t5	t5	PROPN
fcis-6030	167	17	transformer	transformer	NOUN
fcis-6030	167	18	encoder	encoder	NOUN
fcis-6030	167	19	.	.	PUNCT
fcis-6030	168	1	these	these	DET
fcis-6030	168	2	features	feature	NOUN
fcis-6030	168	3	then	then	ADV
fcis-6030	168	4	serve	serve	VERB
fcis-6030	168	5	as	as	ADP
fcis-6030	168	6	conditions	condition	NOUN
fcis-6030	168	7	to	to	PART
fcis-6030	168	8	guide	guide	VERB
fcis-6030	168	9	the	the	DET
fcis-6030	168	10	cascaded	cascade	VERB
fcis-6030	168	11	diffusion	diffusion	NOUN
fcis-6030	168	12	-	-	PUNCT
fcis-6030	168	13	based	base	VERB
fcis-6030	168	14	decoder	decoder	NOUN
fcis-6030	168	15	.	.	PUNCT
fcis-6030	169	1	we	we	PRON
fcis-6030	169	2	compress	compress	VERB
fcis-6030	169	3	the	the	DET
fcis-6030	169	4	network	network	NOUN
fcis-6030	169	5	for	for	ADP
fcis-6030	169	6	more	more	ADV
fcis-6030	169	7	efficient	efficient	ADJ
fcis-6030	169	8	training	training	NOUN
fcis-6030	169	9	and	and	CCONJ
fcis-6030	169	10	sampling	sampling	NOUN
fcis-6030	169	11	.	.	PUNCT
fcis-6030	170	1	different	different	ADJ
fcis-6030	170	2	experiments	experiment	NOUN
fcis-6030	170	3	are	be	AUX
fcis-6030	170	4	conducted	conduct	VERB
fcis-6030	170	5	to	to	PART
fcis-6030	170	6	improve	improve	VERB
fcis-6030	170	7	the	the	DET
fcis-6030	170	8	model	model	NOUN
fcis-6030	170	9	and	and	CCONJ
fcis-6030	170	10	reduce	reduce	VERB
fcis-6030	170	11	the	the	DET
fcis-6030	170	12	computational	computational	ADJ
fcis-6030	170	13	cost	cost	NOUN
fcis-6030	170	14	.	.	PUNCT
fcis-6030	171	1	experiment	experiment	NOUN
fcis-6030	171	2	results	result	NOUN
fcis-6030	171	3	show	show	VERB
fcis-6030	171	4	that	that	SCONJ
fcis-6030	171	5	our	our	PRON
fcis-6030	171	6	lightweight	lightweight	ADJ
fcis-6030	171	7	model	model	NOUN
fcis-6030	171	8	can	can	AUX
fcis-6030	171	9	still	still	ADV
fcis-6030	171	10	achieve	achieve	VERB
fcis-6030	171	11	a	a	DET
fcis-6030	171	12	reasonable	reasonable	ADJ
fcis-6030	171	13	performance	performance	NOUN
fcis-6030	171	14	.	.	PUNCT
fcis-6030	172	1	references	reference	NOUN
fcis-6030	172	2	[	[	X
fcis-6030	172	3	1	1	NUM
fcis-6030	172	4	]	]	X
fcis-6030	172	5	saharia	saharia	NOUN
fcis-6030	172	6	,	,	PUNCT
fcis-6030	172	7	c.	c.	PROPN
fcis-6030	172	8	,	,	PUNCT
fcis-6030	172	9	chan	chan	PROPN
fcis-6030	172	10	,	,	PUNCT
fcis-6030	172	11	w.	w.	PROPN
fcis-6030	172	12	,	,	PUNCT
fcis-6030	172	13	saxena	saxena	PROPN
fcis-6030	172	14	,	,	PUNCT
fcis-6030	172	15	s.	s.	PROPN
fcis-6030	172	16	,	,	PUNCT
fcis-6030	172	17	et	et	PROPN
fcis-6030	172	18	al	al	PROPN
fcis-6030	172	19	.	.	PROPN
fcis-6030	172	20	(	(	PUNCT
fcis-6030	172	21	2022	2022	NUM
fcis-6030	172	22	)	)	PUNCT
fcis-6030	172	23	.	.	PUNCT
fcis-6030	173	1	photorealistic	photorealistic	ADJ
fcis-6030	173	2	text	text	NOUN
fcis-6030	173	3	-	-	PUNCT
fcis-6030	173	4	to	to	ADP
fcis-6030	173	5	-	-	PUNCT
fcis-6030	173	6	image	image	NOUN
fcis-6030	173	7	diffusion	diffusion	NOUN
fcis-6030	173	8	models	model	NOUN
fcis-6030	173	9	with	with	ADP
fcis-6030	173	10	deep	deep	ADJ
fcis-6030	173	11	language	language	NOUN
fcis-6030	173	12	understanding	understanding	NOUN
fcis-6030	173	13	.	.	PUNCT
fcis-6030	174	1	arxiv	arxiv	PROPN
fcis-6030	174	2	preprint	preprint	NOUN
fcis-6030	174	3	,	,	PUNCT
fcis-6030	174	4	2205.11487	2205.11487	NUM
fcis-6030	174	5	.	.	PUNCT
fcis-6030	175	1	[	[	X
fcis-6030	175	2	2	2	NUM
fcis-6030	175	3	]	]	PUNCT
fcis-6030	175	4	reed	reed	NOUN
fcis-6030	175	5	,	,	PUNCT
fcis-6030	175	6	s.	s.	PROPN
fcis-6030	175	7	,	,	PUNCT
fcis-6030	175	8	akata	akata	PROPN
fcis-6030	175	9	,	,	PUNCT
fcis-6030	175	10	z.	z.	PROPN
fcis-6030	175	11	,	,	PUNCT
fcis-6030	175	12	yan	yan	PROPN
fcis-6030	175	13	,	,	PUNCT
fcis-6030	175	14	x.	x.	PROPN
fcis-6030	175	15	,	,	PUNCT
fcis-6030	175	16	et	et	PROPN
fcis-6030	175	17	al	al	PROPN
fcis-6030	175	18	.	.	PUNCT
fcis-6030	176	1	(	(	PUNCT
fcis-6030	176	2	2016	2016	NUM
fcis-6030	176	3	)	)	PUNCT
fcis-6030	176	4	.	.	PUNCT
fcis-6030	177	1	generative	generative	ADJ
fcis-6030	177	2	adversarial	adversarial	ADJ
fcis-6030	177	3	text	text	NOUN
fcis-6030	177	4	to	to	ADP
fcis-6030	177	5	image	image	NOUN
fcis-6030	177	6	synthesis	synthesis	NOUN
fcis-6030	177	7	.	.	PUNCT
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fcis-6030	178	2	,	,	PUNCT
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fcis-6030	178	4	-	-	SYM
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fcis-6030	178	6	.	.	PUNCT
fcis-6030	179	1	[	[	X
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fcis-6030	179	3	]	]	PUNCT
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fcis-6030	179	5	,	,	PUNCT
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fcis-6030	179	7	,	,	PUNCT
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fcis-6030	179	9	,	,	PUNCT
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fcis-6030	179	11	c.	c.	PROPN
fcis-6030	179	12	b.	b.	PROPN
fcis-6030	179	13	,	,	PUNCT
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fcis-6030	179	15	,	,	PUNCT
fcis-6030	179	16	s.	s.	PROPN
fcis-6030	179	17	,	,	PUNCT
fcis-6030	179	18	et	et	PROPN
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fcis-6030	179	20	.	.	PUNCT
fcis-6030	180	1	(	(	PUNCT
fcis-6030	180	2	2017	2017	NUM
fcis-6030	180	3	)	)	PUNCT
fcis-6030	180	4	.	.	PUNCT
fcis-6030	181	1	tac	tac	NOUN
fcis-6030	181	2	-	-	ADJ
fcis-6030	181	3	gantext	gantext	NOUN
fcis-6030	181	4	conditioned	condition	VERB
fcis-6030	181	5	auxiliary	auxiliary	ADJ
fcis-6030	181	6	classifier	classifier	NOUN
fcis-6030	181	7	generative	generative	ADJ
fcis-6030	181	8	adversarial	adversarial	ADJ
fcis-6030	181	9	network	network	NOUN
fcis-6030	181	10	.	.	PUNCT
fcis-6030	182	1	arxiv	arxiv	PROPN
fcis-6030	182	2	preprint	preprint	PROPN
fcis-6030	182	3	,	,	PUNCT
fcis-6030	182	4	1703.06412	1703.06412	X
fcis-6030	182	5	.	.	PUNCT
fcis-6030	183	1	[	[	X
fcis-6030	183	2	4	4	NUM
fcis-6030	183	3	]	]	X
fcis-6030	183	4	zhang	zhang	PROPN
fcis-6030	183	5	,	,	PUNCT
fcis-6030	183	6	h.	h.	PROPN
fcis-6030	183	7	,	,	PUNCT
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fcis-6030	183	9	,	,	PUNCT
fcis-6030	183	10	t.	t.	PROPN
fcis-6030	183	11	,	,	PUNCT
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fcis-6030	183	13	,	,	PUNCT
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fcis-6030	183	15	,	,	PUNCT
fcis-6030	183	16	et	et	PROPN
fcis-6030	183	17	al	al	PROPN
fcis-6030	183	18	.	.	PUNCT
fcis-6030	183	19	(	(	PUNCT
fcis-6030	183	20	2017	2017	NUM
fcis-6030	183	21	)	)	PUNCT
fcis-6030	183	22	.	.	PUNCT
fcis-6030	184	1	stackgan	stackgan	ADJ
fcis-6030	184	2	:	:	PUNCT
fcis-6030	184	3	text	text	NOUN
fcis-6030	184	4	to	to	ADP
fcis-6030	184	5	photorealistic	photorealistic	ADJ
fcis-6030	184	6	image	image	NOUN
fcis-6030	184	7	synthesis	synthesis	NOUN
fcis-6030	184	8	with	with	ADP
fcis-6030	184	9	stacked	stack	VERB
fcis-6030	184	10	generative	generative	ADJ
fcis-6030	184	11	adversarial	adversarial	ADJ
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fcis-6030	184	13	.	.	PUNCT
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fcis-6030	185	2	of	of	ADP
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fcis-6030	185	4	ieee	ieee	NOUN
fcis-6030	185	5	international	international	PROPN
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fcis-6030	185	7	on	on	ADP
fcis-6030	185	8	computer	computer	NOUN
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fcis-6030	185	10	,	,	PUNCT
fcis-6030	185	11	5907	5907	NUM
fcis-6030	185	12	-	-	SYM
fcis-6030	185	13	5915	5915	NUM
fcis-6030	185	14	.	.	PUNCT
fcis-6030	186	1	89	89	NUM
fcis-6030	187	1	[	[	SYM
fcis-6030	187	2	5	5	NUM
fcis-6030	187	3	]	]	SYM
fcis-6030	187	4	yuan	yuan	NOUN
fcis-6030	187	5	,	,	PUNCT
fcis-6030	187	6	m.	m.	NOUN
fcis-6030	187	7	,	,	PUNCT
fcis-6030	187	8	peng	peng	PROPN
fcis-6030	187	9	,	,	PUNCT
fcis-6030	187	10	y.	y.	PROPN
fcis-6030	187	11	(	(	PUNCT
fcis-6030	187	12	2019	2019	NUM
fcis-6030	187	13	)	)	PUNCT
fcis-6030	187	14	.	.	PUNCT
fcis-6030	188	1	bridge	bridge	NOUN
fcis-6030	188	2	-	-	PUNCT
fcis-6030	188	3	gan	gan	ADJ
fcis-6030	188	4	:	:	PUNCT
fcis-6030	188	5	interpretable	interpretable	ADJ
fcis-6030	188	6	representation	representation	NOUN
fcis-6030	188	7	learning	learn	VERB
fcis-6030	188	8	for	for	ADP
fcis-6030	188	9	text	text	NOUN
fcis-6030	188	10	-	-	PUNCT
fcis-6030	188	11	to	to	ADP
fcis-6030	188	12	-	-	PUNCT
fcis-6030	188	13	image	image	NOUN
fcis-6030	188	14	synthesis	synthesis	NOUN
fcis-6030	188	15	.	.	PUNCT
fcis-6030	189	1	ieee	ieee	NOUN
fcis-6030	189	2	transactions	transaction	NOUN
fcis-6030	189	3	on	on	ADP
fcis-6030	189	4	circuits	circuit	NOUN
fcis-6030	189	5	and	and	CCONJ
fcis-6030	189	6	systems	system	NOUN
fcis-6030	189	7	for	for	ADP
fcis-6030	189	8	video	video	NOUN
fcis-6030	189	9	technology	technology	NOUN
fcis-6030	189	10	,	,	PUNCT
fcis-6030	189	11	30(11):4258	30(11):4258	NUM
fcis-6030	189	12	-	-	SYM
fcis-6030	189	13	4268	4268	NUM
fcis-6030	189	14	.	.	PUNCT
fcis-6030	190	1	[	[	X
fcis-6030	190	2	6	6	NUM
fcis-6030	190	3	]	]	SYM
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fcis-6030	190	5	,	,	PUNCT
fcis-6030	190	6	j.	j.	PROPN
fcis-6030	190	7	,	,	PUNCT
fcis-6030	190	8	jain	jain	PROPN
fcis-6030	190	9	,	,	PUNCT
fcis-6030	190	10	a.	a.	PROPN
fcis-6030	190	11	,	,	PUNCT
fcis-6030	190	12	abbeel	abbeel	NOUN
fcis-6030	190	13	,	,	PUNCT
fcis-6030	190	14	p.	p.	NOUN
fcis-6030	190	15	(	(	PUNCT
fcis-6030	190	16	2020	2020	NUM
fcis-6030	190	17	)	)	PUNCT
fcis-6030	190	18	.	.	PUNCT
fcis-6030	191	1	denoising	denoise	VERB
fcis-6030	191	2	diffusion	diffusion	NOUN
fcis-6030	191	3	probabilistic	probabilistic	ADJ
fcis-6030	191	4	models	model	NOUN
fcis-6030	191	5	.	.	PUNCT
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fcis-6030	192	7	,	,	PUNCT
fcis-6030	192	8	33	33	NUM
fcis-6030	192	9	:	:	SYM
fcis-6030	192	10	6840	6840	NUM
fcis-6030	192	11	-	-	SYM
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fcis-6030	192	13	.	.	PUNCT
fcis-6030	193	1	[	[	X
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fcis-6030	193	7	,	,	PUNCT
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fcis-6030	193	9	,	,	PUNCT
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fcis-6030	193	11	(	(	PUNCT
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fcis-6030	193	13	)	)	PUNCT
fcis-6030	193	14	.	.	PUNCT
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fcis-6030	194	2	models	model	NOUN
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fcis-6030	194	6	image	image	NOUN
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fcis-6030	194	8	.	.	PUNCT
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fcis-6030	195	7	,	,	PUNCT
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fcis-6030	195	9	-	-	NOUN
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fcis-6030	195	11	.	.	PUNCT
fcis-6030	196	1	[	[	X
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fcis-6030	196	5	,	,	PUNCT
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fcis-6030	196	7	,	,	PUNCT
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fcis-6030	196	9	,	,	PUNCT
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fcis-6030	196	11	,	,	PUNCT
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fcis-6030	196	13	,	,	PUNCT
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fcis-6030	197	4	.	.	PUNCT
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fcis-6030	198	7	image	image	NOUN
fcis-6030	198	8	generation	generation	NOUN
fcis-6030	198	9	.	.	PUNCT
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fcis-6030	199	6	,	,	PUNCT
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fcis-6030	199	8	-	-	SYM
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fcis-6030	199	10	.	.	PUNCT
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fcis-6030	200	7	,	,	PUNCT
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fcis-6030	200	16	et	et	PROPN
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fcis-6030	200	19	(	(	PUNCT
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fcis-6030	200	21	)	)	PUNCT
fcis-6030	200	22	.	.	PUNCT
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fcis-6030	201	2	:	:	PUNCT
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fcis-6030	201	12	models	model	NOUN
fcis-6030	201	13	.	.	PUNCT
fcis-6030	202	1	arxiv	arxiv	PROPN
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fcis-6030	202	3	,	,	PUNCT
fcis-6030	202	4	2112.10741	2112.10741	ADJ
fcis-6030	202	5	.	.	PUNCT
fcis-6030	203	1	[	[	X
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fcis-6030	204	1	(	(	PUNCT
fcis-6030	204	2	2017	2017	NUM
fcis-6030	204	3	)	)	PUNCT
fcis-6030	204	4	.	.	PUNCT
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fcis-6030	205	2	is	be	AUX
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fcis-6030	205	5	need	need	VERB
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fcis-6030	206	6	systems	system	NOUN
fcis-6030	206	7	,	,	PUNCT
fcis-6030	206	8	30	30	NUM
fcis-6030	206	9	.	.	PUNCT
fcis-6030	207	1	[	[	X
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fcis-6030	207	3	]	]	X
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fcis-6030	207	6	t.	t.	PROPN
fcis-6030	207	7	,	,	PUNCT
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fcis-6030	207	9	,	,	PUNCT
fcis-6030	207	10	m.	m.	NOUN
fcis-6030	207	11	,	,	PUNCT
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fcis-6030	207	14	s.	s.	PROPN
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fcis-6030	207	19	(	(	PUNCT
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fcis-6030	207	21	)	)	PUNCT
fcis-6030	207	22	.	.	PUNCT
fcis-6030	208	1	microsoft	microsoft	PROPN
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fcis-6030	208	3	:	:	PUNCT
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fcis-6030	208	8	.	.	PUNCT
fcis-6030	209	1	in	in	ADP
fcis-6030	209	2	:	:	PUNCT
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fcis-6030	209	6	eccv	eccv	ADJ
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fcis-6030	209	8	:	:	PUNCT
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fcis-6030	209	12	.	.	PUNCT
fcis-6030	210	1	zurich	zurich	PROPN
fcis-6030	210	2	,	,	PUNCT
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fcis-6030	210	4	.	.	PUNCT
fcis-6030	211	1	13:740	13:740	NUM
fcis-6030	211	2	-	-	PUNCT
fcis-6030	211	3	755	755	NUM
fcis-6030	211	4	.	.	PUNCT
fcis-6030	212	1	[	[	X
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fcis-6030	212	5	,	,	PUNCT
fcis-6030	212	6	n.	n.	NOUN
fcis-6030	212	7	,	,	PUNCT
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fcis-6030	212	9	,	,	PUNCT
fcis-6030	212	10	g.	g.	PROPN
fcis-6030	212	11	(	(	PUNCT
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fcis-6030	212	13	)	)	PUNCT
fcis-6030	212	14	.	.	PUNCT
fcis-6030	213	1	how	how	SCONJ
fcis-6030	213	2	to	to	PART
fcis-6030	213	3	read	read	VERB
fcis-6030	213	4	paintings	painting	NOUN
fcis-6030	213	5	:	:	PUNCT
fcis-6030	213	6	semantic	semantic	ADJ
fcis-6030	213	7	art	art	NOUN
fcis-6030	213	8	understanding	understanding	NOUN
fcis-6030	213	9	with	with	ADP
fcis-6030	213	10	multi	multi	ADJ
fcis-6030	213	11	-	-	ADJ
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fcis-6030	213	13	retrieval	retrieval	NOUN
fcis-6030	213	14	.	.	PUNCT
fcis-6030	214	1	proceedings	proceeding	NOUN
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fcis-6030	214	9	(	(	PUNCT
fcis-6030	214	10	eccv	eccv	ADJ
fcis-6030	214	11	)	)	PUNCT
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fcis-6030	214	13	,	,	PUNCT
fcis-6030	214	14	0	0	X
fcis-6030	214	15	-	-	SYM
fcis-6030	214	16	0.r	0.r	NUM