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 1281–1290 of 2167 papers

Title	Date	Tasks	Status
Breaking Common Sense: WHOOPS! A Vision-and-Language Benchmark of Synthetic and Compositional Images	Mar 13, 2023	Common Sense ReasoningExplanation Generation	—Unverified
Breaking Down Questions for Outside-Knowledge VQA	Sep 29, 2021	Graph Neural NetworkQuestion Answering	—Unverified
Breaking Down Questions for Outside-Knowledge Visual Question Answering	Nov 16, 2021	Graph Neural NetworkQuestion Answering	—Unverified
Bridge Damage Cause Estimation Using Multiple Images Based on Visual Question Answering	Feb 18, 2023	Question AnsweringVisual Question Answering	—Unverified
Bridging the Gap between Recognition-level Pre-training and Commonsensical Vision-language Tasks	May 1, 2022	DiversityInformativeness	—Unverified
Bridging the Gap: Exploring the Capabilities of Bridge-Architectures for Complex Visual Reasoning Tasks	Jul 31, 2023	Image RetrievalObject	—Unverified
Bridging the Semantic Gaps: Improving Medical VQA Consistency with LLM-Augmented Question Sets	Apr 16, 2025	DiversityMedical Visual Question Answering	—Unverified
Bridging Video Quality Scoring and Justification via Large Multimodal Models	Jun 26, 2025	Video Quality AssessmentVisual Question Answering (VQA)	—Unverified
Bring Remote Sensing Object Detect Into Nature Language Model: Using SFT Method	Mar 11, 2025	Language ModelingLanguage Modelling	—Unverified
BuDDIE: A Business Document Dataset for Multi-task Information Extraction	Apr 5, 2024	Document Classificationdocument understanding	—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