Visual Question Answering (VQA) on GQA Test2019

Metric: Accuracy (higher is better)

LeaderboardDataset

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Results

#	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	-	-	-

Model↕

Accuracy▼

Extra Data

Paper

Date↕

Code

human

89.3

DREAM+Unicoder-VL (MSRA)

76.04

TRRNet (Ensemble)

74.03

MIL-nbgao

73.81

Kakao Brain

73.33

Coarse-to-Fine Reasoning, Single Model

72.14

270

70.23

NSM ensemble (updated)

67.55

VinVL-DPT

64.92

VinVL+L

64.85

Code

Single Model

64.65

VinVL: Revisiting Visual Representations in Visi...

2021-01-02

Code

Wayne

63.94

Single

63.2

NSM single (updated)

63.17

LXR955, Ensemble

62.71

LXMERT: Learning Cross-Modality Encoder Represen...

2019-08-20

Code

MDETR

62.45

1-gqa

62.44

UCM

61.49

GRN

61.22

Bilinear Graph Networks for Visual Question Answ...

2019-07-23

lxmert-adv-txt

61.12

lxmert-adv-txt

61.1

MSM@MSRA

61.09

mlmbert

61.05

fisher

60.98

ckpt 19 exp 90

60.95

60.93

IQA (single)

60.89

Ensemble10

60.87

Meta Module, Single

60.83

xpj

60.7

fbe20v3.json

60.67

LININ

60.59

prompt IMT-16

60.51

vv69

60.42

bert_v1

60.37

LXR955, Single Model

60.33

LXMERT: Learning Cross-Modality Encoder Represen...

2019-08-20

Code

IIE_Morningstar

60.28

full_nsp_ft_results_submit_predict.json

60.27

TESTOVQA007

60.18

test gqa

60.18

Future_Test_team

60.17

tmp

60.14

Inspur

60.07

full_nsp_mlm_ft_joint_results_submit_predict.json

60.02

SSRP

60.01

Musan

59.93

gaochongyang9

59.84

PVR

59.81

BgTest

59.8

DAM

59.72

DL16

59.54

mcmi

59.43

rishabh_test

59.37

UNITER + MAC + Graph Networks

59.29

LXMERT-S

59.12

QGCRGN

59.06

gbert1

58.91

glimple_all

58.88

ours-4-gqa_el_tag_v4__pretrain_rel_tag_dist_tc_v7_checkpoint-47-157510-best-4.json

58.72

Partial-MSP

58.42

UCAS-SARI

58.2

stu09e

58.12

happyTeam

58.06

graphRepresentation， Single

57.89

VqaStar-UCAS-SARI

57.79

REX

57.77

MLVQA (single)

57.65

rsa-14word

57.35

result_run_2647872_epoch11

57.21

DeeTee

57.14

BAN

57.1

LCGN

57.07

RSN (Single Model)

57.01

GM6_9_2_train

56.96

wcf-fight

56.95

total14

56.95

Testify

56.65

F205

56.59

Feb_ft2_mergeadd_weightalllstm_picklocw_box5_prep

56.38

MMT-VQA

56.28

IWantADonut

56.18

GIN

56.16

LOGNet+VLR

56.11

Improved SNMN

56.09

ST_VQA

55.93

Deepblue_Semantics

55.7

55.65

RSN (Single Model)_v6

55.57

nogg

55.41

abc_test

55.35

Eden_test

54.94

HDU_ZWF

54.79

vips

54.15

MAC

54.06

GQA: A New Dataset for Real-World Visual Reasoni...

2019-02-25

Code

5TMT-qe+o

53.89

ZhaoLab

53.85

test

53.57

100

Sorbonne

53.31

101

UJCNN

52.3

102

52.19

103

mac_qin

52.02

104

Mithrandir

51.87

105

happy

51.51

106

Space Cat

51.22

107

BottomUp

49.74

Bottom-Up and Top-Down Attention for Image Capti...

2017-07-25

Code

108

RAM_BUGGY

49.28

109

sparsemax15

49.27

110

mfb+bert

48.97

111

RES

48.44

112

LAS

47.72

113

test

47.38

114

LSTM+CNN

46.55

115

113

45.86

116

Ediburgh-Mila-UCLA

44.06

117

bear

43.84

118

CHAIR

42.75

119

MReaL

41.63

120

LSTM

41.07

121

Academia Sinica

40.3

122

37.03

123

Mycsulb

36.75

124

LocalPrior

31.24

125

GlobalPrior

28.9

126

muc_ai

26.45

127

CNN

17.82