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 676–700 of 2167 papers

Title	Date	Tasks	Status	Score
AutoBench-V: Can Large Vision-Language Models Benchmark Themselves?	Oct 28, 2024	BenchmarkingQuestion Answering	CodeCode Available	5
Cross-Lingual Text-Rich Visual Comprehension: An Information Theory Perspective	Dec 23, 2024	Question AnsweringVisual Question Answering	CodeCode Available	5
A Unified Hallucination Mitigation Framework for Large Vision-Language Models	Sep 24, 2024	HallucinationQuestion Answering	CodeCode Available	5
Multiscale Byte Language Models -- A Hierarchical Architecture for Causal Million-Length Sequence Modeling	Feb 20, 2025	DecoderGPU	CodeCode Available	5
CRIPP-VQA: Counterfactual Reasoning about Implicit Physical Properties via Video Question Answering	Nov 7, 2022	Add - POAdd - PQ	CodeCode Available	5
Augmenting Visual Question Answering with Semantic Frame Information in a Multitask Learning Approach	Jan 31, 2020	Question AnsweringVisual Question Answering	CodeCode Available	5
Multimodal Hypothetical Summary for Retrieval-based Multi-image Question Answering	Dec 19, 2024	Contrastive LearningLanguage Modeling	CodeCode Available	5
Multimodal Residual Learning for Visual QA	Jun 5, 2016	Multiple-choiceQuestion Answering	CodeCode Available	5
Multi-Sourced Compositional Generalization in Visual Question Answering	May 29, 2025	Question AnsweringVisual Question Answering	CodeCode Available	5
A Dataset and Architecture for Visual Reasoning with a Working Memory	Mar 16, 2018	DiagnosticLogical Reasoning	CodeCode Available	5
Counting Everyday Objects in Everyday Scenes	Apr 12, 2016	ObjectObject Counting	CodeCode Available	5
Multimodal Compact Bilinear Pooling for Visual Question Answering and Visual Grounding	Jun 6, 2016	Phrase GroundingVisual Grounding	CodeCode Available	5
Attribute Diversity Determines the Systematicity Gap in VQA	Nov 15, 2023	AttributeDiagnostic	CodeCode Available	5
Multimodal Explanations: Justifying Decisions and Pointing to the Evidence	Feb 15, 2018	Activity RecognitionExplainable Models	CodeCode Available	5
Multi-modal Factorized Bilinear Pooling with Co-Attention Learning for Visual Question Answering	Aug 4, 2017	Question AnsweringVisual Question Answering	CodeCode Available	5
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	5
Copy-Move Forgery Detection and Question Answering for Remote Sensing Image	Dec 3, 2024	Question AnsweringVisual Question Answering	CodeCode Available	5
Attention on Attention: Architectures for Visual Question Answering (VQA)	Mar 21, 2018	GPUQuestion Answering	CodeCode Available	5
Convincing Rationales for Visual Question Answering Reasoning	Feb 6, 2024	Question AnsweringVisual Question Answering	CodeCode Available	5
Multi-Image Visual Question Answering	Dec 27, 2021	Question AnsweringVisual Question Answering	CodeCode Available	5
Contrastive Visual-Linguistic Pretraining	Jul 26, 2020	Contrastive Learningregression	CodeCode Available	5
Continual VQA for Disaster Response Systems	Sep 21, 2022	Disaster ResponseManagement	CodeCode Available	5
Context-VQA: Towards Context-Aware and Purposeful Visual Question Answering	Jul 28, 2023	Question AnsweringVisual Question Answering	CodeCode Available	5
Multi-Target Embodied Question Answering	Apr 9, 2019	Embodied Question AnsweringNavigate	CodeCode Available	5
On Modality Bias in the TVQA Dataset	Dec 18, 2020	Question AnsweringVideo Question Answering	CodeCode Available	5

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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