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 1031–1040 of 2167 papers

Title	Date	Tasks	Status
From Shallow to Deep: Compositional Reasoning over Graphs for Visual Question Answering	Jun 25, 2022	Question AnsweringVisual Question Answering	—Unverified
From Pixels to Objects: Cubic Visual Attention for Visual Question Answering	Jun 4, 2022	ObjectQuestion Answering	—Unverified
CL-MoE: Enhancing Multimodal Large Language Model with Dual Momentum Mixture-of-Experts for Continual Visual Question Answering	Mar 1, 2025	Continual LearningLanguage Modeling	—Unverified
From Pixels to Graphs: using Scene and Knowledge Graphs for HD-EPIC VQA Challenge	Jun 10, 2025	Knowledge GraphsLanguage Modeling	—Unverified
From Known to the Unknown: Transferring Knowledge to Answer Questions about Novel Visual and Semantic Concepts	Nov 30, 2018	Novel ConceptsQuestion Answering	—Unverified
Does CLIP Benefit Visual Question Answering in the Medical Domain as Much as it Does in the General Domain?	Dec 27, 2021	ArticlesMedical Visual Question Answering	—Unverified
MedFrameQA: A Multi-Image Medical VQA Benchmark for Clinical Reasoning	May 22, 2025	DiagnosticVisual Question Answering (VQA)	—Unverified
From Image to Language: A Critical Analysis of Visual Question Answering (VQA) Approaches, Challenges, and Opportunities	Nov 1, 2023	NavigateQuestion Answering	—Unverified
CLIP-UP: CLIP-Based Unanswerable Problem Detection for Visual Question Answering	Jan 2, 2025	Multiple-choiceQuestion Answering	—Unverified
CLIP-TD: CLIP Targeted Distillation for Vision-Language Tasks	Jan 15, 2022	Question AnsweringVisual Commonsense Reasoning	—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	InternVL-C	Accuracy	81.2	—	Unverified
10	Lyrics	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