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 1661–1670 of 2167 papers

Title	Date	Tasks	Status
Where is this coming from? Making groundedness count in the evaluation of Document VQA models	Mar 24, 2025	Question AnsweringVisual Question Answering	—Unverified
Where To Look: Focus Regions for Visual Question Answering	Nov 23, 2015	Question AnsweringVisual Question Answering	—Unverified
Which Client is Reliable?: A Reliable and Personalized Prompt-based Federated Learning for Medical Image Question Answering	Oct 23, 2024	Federated LearningMedical Visual Question Answering	—Unverified
Why context matters in VQA and Reasoning: Semantic interventions for VLM input modalities	Oct 2, 2024	Question AnsweringVisual Question Answering	—Unverified
Why Does a Visual Question Have Different Answers?	Aug 12, 2019	Question AnsweringVisual Question Answering	—Unverified
Why Does the VQA Model Answer No?: Improving Reasoning through Visual and Linguistic Inference	Sep 25, 2019	Common Sense ReasoningQuestion Answering	—Unverified
WoLF: Wide-scope Large Language Model Framework for CXR Understanding	Mar 19, 2024	AnatomyInstruction Following	—Unverified
Workshop on Document Intelligence Understanding	Jul 31, 2023	document understandingVisual Question Answering (VQA)	—Unverified
WSI-LLaVA: A Multimodal Large Language Model for Whole Slide Image	Dec 3, 2024	DiagnosticLanguage Modeling	—Unverified
WuDaoMM: A large-scale Multi-Modal Dataset for Pre-training models	Mar 22, 2022	Image CaptioningImage Generation	—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