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 1491–1500 of 2167 papers

Title	Date	Tasks	Status
TM-PATHVQA:90000+ Textless Multilingual Questions for Medical Visual Question Answering	Jul 16, 2024	Medical Visual Question AnsweringQuestion Answering	—Unverified
TokenFocus-VQA: Enhancing Text-to-Image Alignment with Position-Aware Focus and Multi-Perspective Aggregations on LVLMs	Apr 10, 2025	Ensemble LearningPosition	—Unverified
Toward 3D Spatial Reasoning for Human-like Text-based Visual Question Answering	Sep 21, 2022	Image CaptioningOptical Character Recognition (OCR)	—Unverified
Toward Effective Reinforcement Learning Fine-Tuning for Medical VQA in Vision-Language Models	May 20, 2025	Medical Visual Question AnsweringQuestion Answering	—Unverified
Toward Robust Hyper-Detailed Image Captioning: A Multiagent Approach and Dual Evaluation Metrics for Factuality and Coverage	Dec 20, 2024	AttributeBenchmarking	—Unverified
Towards a Unified Model for Generating Answers and Explanations in Visual Question Answering	Jan 25, 2023	DecoderExplanation Generation	—Unverified
Towards Automated Error Analysis: Learning to Characterize Errors	Jan 13, 2022	Common Sense ReasoningMeta-Learning	—Unverified
Towards Causal VQA: Revealing and Reducing Spurious Correlations by Invariant and Covariant Semantic Editing	Dec 16, 2019	Question AnsweringVisual Question Answering	—Unverified
Towards Complex Document Understanding By Discrete Reasoning	Jul 25, 2022	document understandingQuestion Answering	—Unverified
Towards Developing a Multilingual and Code-Mixed Visual Question Answering System by Knowledge Distillation	Sep 10, 2021	Knowledge DistillationQuestion Answering	—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