Visual Question Answering (VQA)

Visual Question Answering (VQA) is a task in computer vision that involves answering questions about an image. The goal of VQA is to teach machines to understand the content of an image and answer questions about it in natural language.

Image Source: visualqa.org

Papers

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Showing 1991–2000 of 2167 papers

Title	Date	Tasks	Status	Hype
Generalized Hadamard-Product Fusion Operators for Visual Question Answering	Mar 26, 2018	Neural Architecture SearchQuestion Answering	—Unverified	0
Explicit Reasoning over End-to-End Neural Architectures for Visual Question Answering	Mar 23, 2018	Question AnsweringVisual Question Answering	—Unverified	0
Attention on Attention: Architectures for Visual Question Answering (VQA)	Mar 21, 2018	GPUQuestion Answering	CodeCode Available	0
VQA-E: Explaining, Elaborating, and Enhancing Your Answers for Visual Questions	Mar 20, 2018	Explanatory Visual Question AnsweringMulti-Task Learning	—Unverified	0
Inverse Visual Question Answering: A New Benchmark and VQA Diagnosis Tool	Mar 16, 2018	Question AnsweringReinforcement Learning	—Unverified	0
A Dataset and Architecture for Visual Reasoning with a Working Memory	Mar 16, 2018	DiagnosticLogical Reasoning	CodeCode Available	0
Transparency by Design: Closing the Gap Between Performance and Interpretability in Visual Reasoning	Mar 14, 2018	Question AnsweringVisual Question Answering	CodeCode Available	0
Compositional Attention Networks for Machine Reasoning	Mar 8, 2018	Referring Expression ComprehensionVisual Question Answering (VQA)	CodeCode Available	1
VizWiz Grand Challenge: Answering Visual Questions from Blind People	Feb 22, 2018	Question AnsweringVisual Question Answering	CodeCode Available	0
Learning to Count Objects in Natural Images for Visual Question Answering	Feb 15, 2018	Visual Question AnsweringVisual Question Answering (VQA)	CodeCode Available	0

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All datasets GQA Test2019 VQA v2 test-dev VQA v2 test-std OK-VQA MSVD-QA DocVQA test MSRVTT-QA InfographicVQA GQA test-dev VizWiz 2020 VQA A-OKVQA CLEVR

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	human	Accuracy	89.3	—	Unverified
2	DREAM+Unicoder-VL (MSRA)	Accuracy	76.04	—	Unverified
3	TRRNet (Ensemble)	Accuracy	74.03	—	Unverified
4	MIL-nbgao	Accuracy	73.81	—	Unverified
5	Kakao Brain	Accuracy	73.33	—	Unverified
6	Coarse-to-Fine Reasoning, Single Model	Accuracy	72.14	—	Unverified
7	270	Accuracy	70.23	—	Unverified
8	NSM ensemble (updated)	Accuracy	67.55	—	Unverified
9	VinVL-DPT	Accuracy	64.92	—	Unverified
10	VinVL+L	Accuracy	64.85	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	PaLI	Accuracy	84.3	—	Unverified
2	BEiT-3	Accuracy	84.19	—	Unverified
3	VLMo	Accuracy	82.78	—	Unverified
4	ONE-PEACE	Accuracy	82.6	—	Unverified
5	mPLUG (Huge)	Accuracy	82.43	—	Unverified
6	CuMo-7B	Accuracy	82.2	—	Unverified
7	X2-VLM (large)	Accuracy	81.9	—	Unverified
8	MMU	Accuracy	81.26	—	Unverified
9	Lyrics	Accuracy	81.2	—	Unverified
10	InternVL-C	Accuracy	81.2	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	BEiT-3	overall	84.03	—	Unverified
2	mPLUG-Huge	overall	83.62	—	Unverified
3	ONE-PEACE	overall	82.52	—	Unverified
4	X2-VLM (large)	overall	81.8	—	Unverified
5	VLMo	overall	81.3	—	Unverified
6	SimVLM	overall	80.34	—	Unverified
7	X2-VLM (base)	overall	80.2	—	Unverified
8	VAST	overall	80.19	—	Unverified
9	VALOR	overall	78.62	—	Unverified
10	Prompt Tuning	overall	78.53	—	Unverified