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 621–630 of 2167 papers

Title	Date	Tasks	Status
ReXVQA: A Large-scale Visual Question Answering Benchmark for Generalist Chest X-ray Understanding	Jun 4, 2025	NegationNegation Detection	—Unverified
CoRe-MMRAG: Cross-Source Knowledge Reconciliation for Multimodal RAG	Jun 3, 2025	Answer GenerationRAG	—Unverified
Fast or Slow? Integrating Fast Intuition and Deliberate Thinking for Enhancing Visual Question Answering	Jun 1, 2025	AllMME	—Unverified
MMedAgent-RL: Optimizing Multi-Agent Collaboration for Multimodal Medical Reasoning	May 31, 2025	DiagnosticReinforcement Learning (RL)	—Unverified
Proxy-FDA: Proxy-based Feature Distribution Alignment for Fine-tuning Vision Foundation Models without Forgetting	May 30, 2025	image-classificationImage Classification	—Unverified
Vision LLMs Are Bad at Hierarchical Visual Understanding, and LLMs Are the Bottleneck	May 30, 2025	Question AnsweringVisual Question Answering	—Unverified
A Comprehensive Evaluation of Multi-Modal Large Language Models for Endoscopy Analysis	May 29, 2025	DiagnosticVisual Prompting	—Unverified
Multi-Sourced Compositional Generalization in Visual Question Answering	May 29, 2025	Question AnsweringVisual Question Answering	CodeCode Available
Synthetic Document Question Answering in Hungarian	May 29, 2025	Optical Character Recognition (OCR)Question Answering	CodeCode Available
Spoken question answering for visual queries	May 29, 2025	Question AnsweringVisual Question Answering (VQA)	—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