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

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 1231–1240 of 2167 papers

Title	Date	Tasks	Status	Hype
SwapMix: Diagnosing and Regularizing the Over-Reliance on Visual Context in Visual Question Answering	Apr 5, 2022	Data AugmentationQuestion Answering	CodeCode Available	1
CLEVR-X: A Visual Reasoning Dataset for Natural Language Explanations	Apr 5, 2022	Explanation GenerationQuestion Answering	CodeCode Available	1
Question-Driven Graph Fusion Network For Visual Question Answering	Apr 3, 2022	Graph AttentionObject	—Unverified	0
Co-VQA : Answering by Interactive Sub Question Sequence	Apr 2, 2022	Question AnsweringVisual Question Answering	—Unverified	0
Perceptual Quality Assessment of UGC Gaming Videos	Mar 31, 2022	Video Quality AssessmentVisual Question Answering (VQA)	—Unverified	0
SimVQA: Exploring Simulated Environments for Visual Question Answering	Mar 31, 2022	Data AugmentationDiversity	—Unverified	0
VL-InterpreT: An Interactive Visualization Tool for Interpreting Vision-Language Transformers	Mar 30, 2022	Question AnsweringVisual Commonsense Reasoning	CodeCode Available	0
End-to-end Document Recognition and Understanding with Dessurt	Mar 30, 2022	document understandingVisual Question Answering (VQA)	CodeCode Available	1
Visual Mechanisms Inspired Efficient Transformers for Image and Video Quality Assessment	Mar 28, 2022	Image Quality AssessmentVideo Quality Assessment	—Unverified	0
Single-Stream Multi-Level Alignment for Vision-Language Pretraining	Mar 27, 2022	Image-text RetrievalQuestion Answering	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