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 701–725 of 2167 papers

Title	Date	Tasks	Status	Score
MUREL: Multimodal Relational Reasoning for Visual Question Answering	Feb 25, 2019	Relational ReasoningVisual Question Answering	CodeCode Available	5
Neural Module Networks	Nov 9, 2015	Visual Question AnsweringVisual Question Answering (VQA)	CodeCode Available	5
Diversify, Rationalize, and Combine: Ensembling Multiple QA Strategies for Zero-shot Knowledge-based VQA	Jun 18, 2024	Question AnsweringVisual Question Answering	CodeCode Available	5
Contextual Dropout: An Efficient Sample-Dependent Dropout Module	Mar 6, 2021	image-classificationImage Classification	CodeCode Available	5
Alignment Attention by Matching Key and Query Distributions	Oct 25, 2021	Graph AttentionQuestion Answering	CodeCode Available	5
Adaptively Clustering Neighbor Elements for Image-Text Generation	Jan 5, 2023	ClusteringDecoder	CodeCode Available	5
Aligning Visual Regions and Textual Concepts for Semantic-Grounded Image Representations	May 15, 2019	Image CaptioningQuestion Answering	CodeCode Available	5
Multimodal Residual Learning for Visual QA	Jun 5, 2016	Multiple-choiceQuestion Answering	CodeCode Available	5
Adaptive loose optimization for robust question answering	May 6, 2023	Extractive Question-AnsweringMachine Reading Comprehension	CodeCode Available	5
Compressing And Debiasing Vision-Language Pre-Trained Models for Visual Question Answering	Oct 26, 2022	Question AnsweringVisual Question Answering	CodeCode Available	5
Composition Vision-Language Understanding via Segment and Depth Anything Model	Jun 7, 2024	Question AnsweringVisual Question Answering (VQA)	CodeCode Available	5
Multimodal Compact Bilinear Pooling for Visual Question Answering and Visual Grounding	Jun 6, 2016	Phrase GroundingVisual Grounding	CodeCode Available	5
Compositionality as Lexical Symmetry	Jan 30, 2022	Data AugmentationInductive Bias	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
Multimodal Hypothetical Summary for Retrieval-based Multi-image Question Answering	Dec 19, 2024	Contrastive LearningLanguage Modeling	CodeCode Available	5
Multi-Image Visual Question Answering	Dec 27, 2021	Question AnsweringVisual Question Answering	CodeCode Available	5
Compact Trilinear Interaction for Visual Question Answering	Sep 26, 2019	BenchmarkingKnowledge Distillation	CodeCode Available	5
CommVQA: Situating Visual Question Answering in Communicative Contexts	Feb 22, 2024	Question AnsweringVisual Question Answering	CodeCode Available	5
MQA: Answering the Question via Robotic Manipulation	Mar 10, 2020	Imitation LearningQuestion Answering	CodeCode Available	5
Modulating early visual processing by language	Jul 2, 2017	Question AnsweringVisual Question Answering	CodeCode Available	5
COLUMBUS: Evaluating COgnitive Lateral Understanding through Multiple-choice reBUSes	Sep 6, 2024	Multiple-choiceQuestion Answering	CodeCode Available	5
Modeling Relationships in Referential Expressions with Compositional Modular Networks	Nov 30, 2016	Visual Question Answering (VQA)	CodeCode Available	5
Modularized Zero-shot VQA with Pre-trained Models	May 27, 2023	object-detectionObject Detection	CodeCode Available	5
Adapting Lightweight Vision Language Models for Radiological Visual Question Answering	Jun 17, 2025	DiagnosticQuestion 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