Visual Question Answering (VQA) on GQA Test2019

Metric: Accuracy (higher is better)

LeaderboardDataset

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Results

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#	Model↕	Accuracy▼	Extra Data	Paper	Date↕	Code
1	human	89.3	No	-	-	-
2	DREAM+Unicoder-VL (MSRA)	76.04	No	-	-	-
3	TRRNet (Ensemble)	74.03	No	-	-	-
4	MIL-nbgao	73.81	No	-	-	-
5	Kakao Brain	73.33	No	-	-	-
6	Coarse-to-Fine Reasoning, Single Model	72.14	No	-	-	-
7	270	70.23	No	-	-	-
8	NSM ensemble (updated)	67.55	No	-	-	-
9	VinVL-DPT	64.92	No	-	-	-
10	VinVL+L	64.85	No	-	-	Code
11	Single Model	64.65	No	VinVL: Revisiting Visual Representations in Visi...	2021-01-02	Code
12	Wayne	63.94	No	-	-	-
13	Single	63.2	No	-	-	-
14	NSM single (updated)	63.17	No	-	-	-
15	LXR955, Ensemble	62.71	No	LXMERT: Learning Cross-Modality Encoder Represen...	2019-08-20	Code
16	MDETR	62.45	No	-	-	-
17	1-gqa	62.44	No	-	-	-
18	UCM	61.49	No	-	-	-
19	GRN	61.22	No	Bilinear Graph Networks for Visual Question Answ...	2019-07-23	-
20	lxmert-adv-txt	61.12	No	-	-	-
21	lxmert-adv-txt	61.1	No	-	-	-
22	MSM@MSRA	61.09	No	-	-	-
23	mlmbert	61.05	No	-	-	-
24	fisher	60.98	No	-	-	-
25	ckpt 19 exp 90	60.95	No	-	-	-
26	45	60.93	No	-	-	-
27	IQA (single)	60.89	No	-	-	-
28	Ensemble10	60.87	No	-	-	-
29	Meta Module, Single	60.83	No	-	-	-
30	xpj	60.7	No	-	-	-
31	fbe20v3.json	60.67	No	-	-	-
32	LININ	60.59	No	-	-	-
33	prompt IMT-16	60.51	No	-	-	-
34	vv69	60.42	No	-	-	-
35	bert_v1	60.37	No	-	-	-
36	LXR955, Single Model	60.33	No	LXMERT: Learning Cross-Modality Encoder Represen...	2019-08-20	Code
37	IIE_Morningstar	60.28	No	-	-	-
38	full_nsp_ft_results_submit_predict.json	60.27	No	-	-	-
39	TESTOVQA007	60.18	No	-	-	-
40	test gqa	60.18	No	-	-	-
41	Future_Test_team	60.17	No	-	-	-
42	tmp	60.14	No	-	-	-
43	Inspur	60.07	No	-	-	-
44	full_nsp_mlm_ft_joint_results_submit_predict.json	60.02	No	-	-	-
45	SSRP	60.01	No	-	-	-
46	Musan	59.93	No	-	-	-
47	gaochongyang9	59.84	No	-	-	-
48	PVR	59.81	No	-	-	-
49	BgTest	59.8	No	-	-	-
50	DAM	59.72	No	-	-	-
51	DL16	59.54	No	-	-	-
52	mcmi	59.43	No	-	-	-
53	rishabh_test	59.37	No	-	-	-
54	UNITER + MAC + Graph Networks	59.29	No	-	-	-
55	LXMERT-S	59.12	No	-	-	-
56	QGCRGN	59.06	No	-	-	-
57	gbert1	58.91	No	-	-	-
58	glimple_all	58.88	No	-	-	-
59	ours-4-gqa_el_tag_v4__pretrain_rel_tag_dist_tc_v7_checkpoint-47-157510-best-4.json	58.72	No	-	-	-
60	Partial-MSP	58.42	No	-	-	-
61	UCAS-SARI	58.2	No	-	-	-
62	stu09e	58.12	No	-	-	-
63	happyTeam	58.06	No	-	-	-
64	graphRepresentation， Single	57.89	No	-	-	-
65	VqaStar-UCAS-SARI	57.79	No	-	-	-
66	REX	57.77	No	-	-	-
67	MLVQA (single)	57.65	No	-	-	-
68	rsa-14word	57.35	No	-	-	-
69	result_run_2647872_epoch11	57.21	No	-	-	-
70	DeeTee	57.14	No	-	-	-
71	BAN	57.1	No	-	-	-
72	LCGN	57.07	No	-	-	-
73	RSN (Single Model)	57.01	No	-	-	-
74	GM6_9_2_train	56.96	No	-	-	-
75	wcf-fight	56.95	No	-	-	-
76	total14	56.95	No	-	-	-
77	Testify	56.65	No	-	-	-
78	F205	56.59	No	-	-	-
79	Feb_ft2_mergeadd_weightalllstm_picklocw_box5_prep	56.38	No	-	-	-
80	MMT-VQA	56.28	No	-	-	-
81	IWantADonut	56.18	No	-	-	-
82	GIN	56.16	No	-	-	-
83	LOGNet+VLR	56.11	No	-	-	-
84	Improved SNMN	56.09	No	-	-	-
85	ST_VQA	56	No	-	-	-
86	RD	55.93	No	-	-	-
87	Deepblue_Semantics	55.7	No	-	-	-
88	LW	55.65	No	-	-	-
89	RSN (Single Model)_v6	55.57	No	-	-	-
90	nogg	55.41	No	-	-	-
91	abc_test	55.35	No	-	-	-
92	KU	55	No	-	-	-
93	Eden_test	54.94	No	-	-	-
94	HDU_ZWF	54.79	No	-	-	-
95	vips	54.15	No	-	-	-
96	MAC	54.06	No	GQA: A New Dataset for Real-World Visual Reasoni...	2019-02-25	Code
97	5TMT-qe+o	53.89	No	-	-	-
98	ZhaoLab	53.85	No	-	-	-
99	test	53.57	No	-	-	-
100	Sorbonne	53.31	No	-	-	-
101	UJCNN	52.3	No	-	-	-
102	MJ	52.19	No	-	-	-
103	mac_qin	52.02	No	-	-	-
104	Mithrandir	51.87	No	-	-	-
105	happy	51.51	No	-	-	-
106	Space Cat	51.22	No	-	-	-
107	BottomUp	49.74	No	Bottom-Up and Top-Down Attention for Image Capti...	2017-07-25	Code
108	RAM_BUGGY	49.28	No	-	-	-
109	sparsemax15	49.27	No	-	-	-
110	mfb+bert	48.97	No	-	-	-
111	RES	48.44	No	-	-	-
112	LAS	47.72	No	-	-	-
113	test	47.38	No	-	-	-
114	LSTM+CNN	46.55	No	-	-	-
115	113	45.86	No	-	-	-
116	Ediburgh-Mila-UCLA	44.06	No	-	-	-
117	bear	43.84	No	-	-	-
118	CHAIR	42.75	No	-	-	-
119	MReaL	41.63	No	-	-	-
120	LSTM	41.07	No	-	-	-
121	Academia Sinica	40.3	No	-	-	-
122	Fj	37.03	No	-	-	-
123	Mycsulb	36.75	No	-	-	-
124	LocalPrior	31.24	No	-	-	-
125	GlobalPrior	28.9	No	-	-	-
126	muc_ai	26.45	No	-	-	-
127	CNN	17.82	No	-	-	-

#1human
89.3
Accuracy
No paper
#2DREAM+Unicoder-VL (MSRA)
76.04
Accuracy
No paper
#3TRRNet (Ensemble)
74.03
Accuracy
No paper
#4MIL-nbgao
73.81
Accuracy
No paper
#5Kakao Brain
73.33
Accuracy
No paper
#6Coarse-to-Fine Reasoning, Single Model
72.14
Accuracy
No paper
#7270
70.23
Accuracy
No paper
#8NSM ensemble (updated)
67.55
Accuracy
No paper
#9VinVL-DPT
64.92
Accuracy
No paper
#10VinVL+L
64.85
Accuracy
No paperCode
#11Single ModelSOTA
64.65
Accuracy· 2021-01-02
VinVL: Revisiting Visual Representations in Vision-Language Models Code
#12Wayne
63.94
Accuracy
No paper
#13Single
63.2
Accuracy
No paper
#14NSM single (updated)
63.17
Accuracy
No paper
#15LXR955, EnsembleSOTA
62.71
Accuracy· 2019-08-20
LXMERT: Learning Cross-Modality Encoder Representations from Transformers Code
#16MDETR
62.45
Accuracy
No paper
#171-gqa
62.44
Accuracy
No paper
#18UCM
61.49
Accuracy
No paper
#19GRNSOTA
61.22
Accuracy· 2019-07-23
Bilinear Graph Networks for Visual Question Answering
#20lxmert-adv-txt
61.12
Accuracy
No paper
#21lxmert-adv-txt
61.1
Accuracy
No paper
#22MSM@MSRA
61.09
Accuracy
No paper
#23mlmbert
61.05
Accuracy
No paper
#24fisher
60.98
Accuracy
No paper
#25ckpt 19 exp 90
60.95
Accuracy
No paper
#2645
60.93
Accuracy
No paper
#27IQA (single)
60.89
Accuracy
No paper
#28Ensemble10
60.87
Accuracy
No paper
#29Meta Module, Single
60.83
Accuracy
No paper
#30xpj
60.7
Accuracy
No paper
#31fbe20v3.json
60.67
Accuracy
No paper
#32LININ
60.59
Accuracy
No paper
#33prompt IMT-16
60.51
Accuracy
No paper
#34vv69
60.42
Accuracy
No paper
#35bert_v1
60.37
Accuracy
No paper
#36LXR955, Single Model
60.33
Accuracy· 2019-08-20
LXMERT: Learning Cross-Modality Encoder Representations from Transformers Code
#37IIE_Morningstar
60.28
Accuracy
No paper
#38full_nsp_ft_results_submit_predict.json
60.27
Accuracy
No paper
#39TESTOVQA007
60.18
Accuracy
No paper
#40test gqa
60.18
Accuracy
No paper
#41Future_Test_team
60.17
Accuracy
No paper
#42tmp
60.14
Accuracy
No paper
#43Inspur
60.07
Accuracy
No paper
#44full_nsp_mlm_ft_joint_results_submit_predict.json
60.02
Accuracy
No paper
#45SSRP
60.01
Accuracy
No paper
#46Musan
59.93
Accuracy
No paper
#47gaochongyang9
59.84
Accuracy
No paper
#48PVR
59.81
Accuracy
No paper
#49BgTest
59.8
Accuracy
No paper
#50DAM
59.72
Accuracy
No paper
#51DL16
59.54
Accuracy
No paper
#52mcmi
59.43
Accuracy
No paper
#53rishabh_test
59.37
Accuracy
No paper
#54UNITER + MAC + Graph Networks
59.29
Accuracy
No paper
#55LXMERT-S
59.12
Accuracy
No paper
#56QGCRGN
59.06
Accuracy
No paper
#57gbert1
58.91
Accuracy
No paper
#58glimple_all
58.88
Accuracy
No paper
#59ours-4-gqa_el_tag_v4__pretrain_rel_tag_dist_tc_v7_checkpoint-47-157510-best-4.json
58.72
Accuracy
No paper
#60Partial-MSP
58.42
Accuracy
No paper
#61UCAS-SARI
58.2
Accuracy
No paper
#62stu09e
58.12
Accuracy
No paper
#63happyTeam
58.06
Accuracy
No paper
#64graphRepresentation， Single
57.89
Accuracy
No paper
#65VqaStar-UCAS-SARI
57.79
Accuracy
No paper
#66REX
57.77
Accuracy
No paper
#67MLVQA (single)
57.65
Accuracy
No paper
#68rsa-14word
57.35
Accuracy
No paper
#69result_run_2647872_epoch11
57.21
Accuracy
No paper
#70DeeTee
57.14
Accuracy
No paper
#71BAN
57.1
Accuracy
No paper
#72LCGN
57.07
Accuracy
No paper
#73RSN (Single Model)
57.01
Accuracy
No paper
#74GM6_9_2_train
56.96
Accuracy
No paper
#75wcf-fight
56.95
Accuracy
No paper
#76total14
56.95
Accuracy
No paper
#77Testify
56.65
Accuracy
No paper
#78F205
56.59
Accuracy
No paper
#79Feb_ft2_mergeadd_weightalllstm_picklocw_box5_prep
56.38
Accuracy
No paper
#80MMT-VQA
56.28
Accuracy
No paper
#81IWantADonut
56.18
Accuracy
No paper
#82GIN
56.16
Accuracy
No paper
#83LOGNet+VLR
56.11
Accuracy
No paper
#84Improved SNMN
56.09
Accuracy
No paper
#85ST_VQA
56
Accuracy
No paper
#86RD
55.93
Accuracy
No paper
#87Deepblue_Semantics
55.7
Accuracy
No paper
#88LW
55.65
Accuracy
No paper
#89RSN (Single Model)_v6
55.57
Accuracy
No paper
#90nogg
55.41
Accuracy
No paper
#91abc_test
55.35
Accuracy
No paper
#92KU
55
Accuracy
No paper
#93Eden_test
54.94
Accuracy
No paper
#94HDU_ZWF
54.79
Accuracy
No paper
#95vips
54.15
Accuracy
No paper
#96MACSOTA
54.06
Accuracy· 2019-02-25
GQA: A New Dataset for Real-World Visual Reasoning and Compositional Question Answering Code
#975TMT-qe+o
53.89
Accuracy
No paper
#98ZhaoLab
53.85
Accuracy
No paper
#99test
53.57
Accuracy
No paper
#100Sorbonne
53.31
Accuracy
No paper
#101UJCNN
52.3
Accuracy
No paper
#102MJ
52.19
Accuracy
No paper
#103mac_qin
52.02
Accuracy
No paper
#104Mithrandir
51.87
Accuracy
No paper
#105happy
51.51
Accuracy
No paper
#106Space Cat
51.22
Accuracy
No paper
#107BottomUpSOTA
49.74
Accuracy· 2017-07-25
Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering Code
#108RAM_BUGGY
49.28
Accuracy
No paper
#109sparsemax15
49.27
Accuracy
No paper
#110mfb+bert
48.97
Accuracy
No paper
#111RES
48.44
Accuracy
No paper
#112LAS
47.72
Accuracy
No paper
#113test
47.38
Accuracy
No paper
#114LSTM+CNN
46.55
Accuracy
No paper
#115113
45.86
Accuracy
No paper
#116Ediburgh-Mila-UCLA
44.06
Accuracy
No paper
#117bear
43.84
Accuracy
No paper
#118CHAIR
42.75
Accuracy
No paper
#119MReaL
41.63
Accuracy
No paper
#120LSTM
41.07
Accuracy
No paper
#121Academia Sinica
40.3
Accuracy
No paper
#122Fj
37.03
Accuracy
No paper
#123Mycsulb
36.75
Accuracy
No paper
#124LocalPrior
31.24
Accuracy
No paper
#125GlobalPrior
28.9
Accuracy
No paper
#126muc_ai
26.45
Accuracy
No paper
#127CNN
17.82
Accuracy
No paper