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 1511–1520 of 2167 papers

Title	Date	Tasks	Status
GC-KBVQA: A New Four-Stage Framework for Enhancing Knowledge Based Visual Question Answering Performance	May 25, 2025	Caption GenerationQuestion Answering	—Unverified
GEMeX: A Large-Scale, Groundable, and Explainable Medical VQA Benchmark for Chest X-ray Diagnosis	Nov 25, 2024	Medical Visual Question AnsweringMultiple-choice	—Unverified
GEMeX-ThinkVG: Towards Thinking with Visual Grounding in Medical VQA via Reinforcement Learning	Jun 22, 2025	Answer GenerationDecision Making	—Unverified
Gemini Pro Defeated by GPT-4V: Evidence from Education	Dec 27, 2023	image-classificationImage Classification	—Unverified
Gender and Racial Bias in Visual Question Answering Datasets	May 17, 2022	Question AnsweringVisual Question Answering	—Unverified
Generalization Differences between End-to-End and Neuro-Symbolic Vision-Language Reasoning Systems	Oct 26, 2022	Question AnsweringVisual Question Answering	—Unverified
Generalized Hadamard-Product Fusion Operators for Visual Question Answering	Mar 26, 2018	Neural Architecture SearchQuestion Answering	—Unverified
Generate then Select: Open-ended Visual Question Answering Guided by World Knowledge	May 30, 2023	Answer SelectionQuestion Answering	—Unverified
Generating Natural Language Explanations for Visual Question Answering using Scene Graphs and Visual Attention	Feb 15, 2019	Explanation GenerationLanguage Modeling	—Unverified
Generating Natural Questions from Images for Multimodal Assistants	Nov 17, 2020	Common Sense ReasoningNatural Questions	—Unverified

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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