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 1971–1980 of 2167 papers

Title	Date	Tasks	Status
Query and Attention Augmentation for Knowledge-Based Explainable Reasoning	Jan 1, 2022	Question AnsweringVisual Question Answering	CodeCode Available
VizWiz Grand Challenge: Answering Visual Questions from Blind People	Feb 22, 2018	Question AnsweringVisual Question Answering	CodeCode Available
Improved RAMEN: Towards Domain Generalization for Visual Question Answering	Sep 6, 2021	Domain GeneralizationQuestion Answering	CodeCode Available
Design as Desired: Utilizing Visual Question Answering for Multimodal Pre-training	Mar 30, 2024	Contrastive LearningQuestion Answering	CodeCode Available
VisFIS: Visual Feature Importance Supervision with Right-for-the-Right-Reason Objectives	Jun 22, 2022	Feature ImportanceQuestion Answering	CodeCode Available
Toward Building General Foundation Models for Language, Vision, and Vision-Language Understanding Tasks	Jan 12, 2023	Cross-Modal RetrievalOpen-Ended Question Answering	CodeCode Available
Adaptive Score Alignment Learning for Continual Perceptual Quality Assessment of 360-Degree Videos in Virtual Reality	Feb 27, 2025	Video Quality AssessmentVisual Question Answering (VQA)	CodeCode Available
Toward Multi-Granularity Decision-Making: Explicit Visual Reasoning with Hierarchical Knowledge	Jan 1, 2023	Decision MakingQuestion Answering	CodeCode Available
Applying recent advances in Visual Question Answering to Record Linkage	Jul 12, 2020	Question AnsweringVisual Question Answering	CodeCode Available
Delving Deeper into Cross-lingual Visual Question Answering	Feb 15, 2022	Inductive BiasQuestion Answering	CodeCode Available

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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