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 11–20 of 2167 papers

Title	Date	Tasks	Status	Hype
HRIBench: Benchmarking Vision-Language Models for Real-Time Human Perception in Human-Robot Interaction	Jun 25, 2025	BenchmarkingPerson Identification	CodeCode Available	0
MMSearch-R1: Incentivizing LMMs to Search	Jun 25, 2025	RAGRetrieval-augmented Generation	CodeCode Available	3
GEMeX-ThinkVG: Towards Thinking with Visual Grounding in Medical VQA via Reinforcement Learning	Jun 22, 2025	Answer GenerationDecision Making	—Unverified	0
How Far Can Off-the-Shelf Multimodal Large Language Models Go in Online Episodic Memory Question Answering?	Jun 19, 2025	Multiple-choiceQuestion Answering	—Unverified	0
Can Common VLMs Rival Medical VLMs? Evaluation and Strategic Insights	Jun 19, 2025	Question AnsweringVisual Question Answering	—Unverified	0
MEGC2025: Micro-Expression Grand Challenge on Spot Then Recognize and Visual Question Answering	Jun 18, 2025	Multimodal ReasoningQuestion Answering	—Unverified	0
ASCD: Attention-Steerable Contrastive Decoding for Reducing Hallucination in MLLM	Jun 17, 2025	HallucinationLanguage Modeling	—Unverified	0
Adapting Lightweight Vision Language Models for Radiological Visual Question Answering	Jun 17, 2025	DiagnosticQuestion Answering	CodeCode Available	0
Connecting phases of matter to the flatness of the loss landscape in analog variational quantum algorithms	Jun 16, 2025	Visual Question Answering (VQA)	—Unverified	0
CAPO: Reinforcing Consistent Reasoning in Medical Decision-Making	Jun 15, 2025	Answer GenerationDecision Making	—Unverified	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	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