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 1111–1120 of 2167 papers

Title	Date	Tasks	Status
Weak Supervision helps Emergence of Word-Object Alignment and improves Vision-Language Tasks	Dec 6, 2019	Image RetrievalInductive Bias	—Unverified
Webly Supervised Concept Expansion for General Purpose Vision Models	Feb 4, 2022	Human-Object Interaction DetectionImage Retrieval	—Unverified
What is needed for simple spatial language capabilities in VQA?	Aug 17, 2019	DiagnosticQuestion Answering	—Unverified
What Large Language Models Bring to Text-rich VQA?	Nov 13, 2023	Image ComprehensionOptical Character Recognition (OCR)	—Unverified
What makes a good metric? Evaluating automatic metrics for text-to-image consistency	Dec 18, 2024	SensitivityVisual Question Answering (VQA)	—Unverified
When are Lemons Purple? The Concept Association Bias of Vision-Language Models	Dec 22, 2022	Attributeimage-classification	—Unverified
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

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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