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

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 1851–1875 of 2167 papers

Title	Date	Tasks	Status
DrishtiKon: Multi-Granular Visual Grounding for Text-Rich Document Images	Jun 26, 2025	document understandingOptical Character Recognition (OCR)	CodeCode Available
TAB-VCR: Tags and Attributes based Visual Commonsense Reasoning Baselines	Oct 31, 2019	AttributeQuestion Answering	CodeCode Available
Lightweight Recurrent Cross-modal Encoder for Video Question Answering	Jun 30, 2023	Action RecognitionQuestion Answering	CodeCode Available
Learning Visual Question Answering by Bootstrapping Hard Attention	Aug 1, 2018	Hard AttentionQuestion Answering	CodeCode Available
TAB-VCR: Tags and Attributes based VCR Baselines	Dec 1, 2019	AttributeQuestion Answering	CodeCode Available
Outside Knowledge Conversational Video (OKCV) Dataset -- Dialoguing over Videos	Jun 11, 2025	Question AnsweringVisual Question Answering	CodeCode Available
Learning to Reason: End-to-End Module Networks for Visual Question Answering	Apr 18, 2017	Visual DialogVisual Question Answering	CodeCode Available
Adversarial Training with OCR Modality Perturbation for Scene-Text Visual Question Answering	Mar 14, 2024	Optical Character RecognitionOptical Character Recognition (OCR)	CodeCode Available
Don't Just Assume; Look and Answer: Overcoming Priors for Visual Question Answering	Dec 1, 2017	Question AnsweringVisual Question Answering	CodeCode Available
VGAT: A Cancer Survival Analysis Framework Transitioning from Generative Visual Question Answering to Genomic Reconstruction	Mar 25, 2025	Generative Visual Question AnsweringQuestion Answering	CodeCode Available
Overcoming Language Priors in Visual Question Answering via Distinguishing Superficially Similar Instances	Sep 18, 2022	AttributeQuestion Answering	CodeCode Available
Learning to Model and Ignore Dataset Bias with Mixed Capacity Ensembles	Nov 7, 2020	Natural Language InferenceQuestion Answering	CodeCode Available
Learning to Localize Objects Improves Spatial Reasoning in Visual-LLMs	Apr 11, 2024	DescriptiveHallucination	CodeCode Available
VGA: Vision GUI Assistant -- Minimizing Hallucinations through Image-Centric Fine-Tuning	Jun 20, 2024	Image ComprehensionQuestion Answering	CodeCode Available
Learning to Count Objects in Natural Images for Visual Question Answering	Feb 15, 2018	Visual Question AnsweringVisual Question Answering (VQA)	CodeCode Available
Are VLMs Really Blind	Oct 29, 2024	Language ModelingLanguage Modelling	CodeCode Available
Learning to Collocate Visual-Linguistic Neural Modules for Image Captioning	Oct 4, 2022	Image CaptioningSentence	CodeCode Available
Learning Representations of Sets through Optimized Permutations	Dec 10, 2018	General ClassificationQuestion Answering	CodeCode Available
TallyQA: Answering Complex Counting Questions	Oct 29, 2018	AttributeObject Counting	CodeCode Available
Learning from Lexical Perturbations for Consistent Visual Question Answering	Nov 26, 2020	Question AnsweringVisual Question Answering	CodeCode Available
Document Haystacks: Vision-Language Reasoning Over Piles of 1000+ Documents	Nov 23, 2024	Question AnsweringRAG	CodeCode Available
Learning Convolutional Text Representations for Visual Question Answering	May 18, 2017	General Classificationimage-classification	CodeCode Available
Learning content and context with language bias for Visual Question Answering	Dec 21, 2020	Question AnsweringVisual Question Answering	CodeCode Available
Learning Conditioned Graph Structures for Interpretable Visual Question Answering	Jun 19, 2018	Question AnsweringVisual Question Answering	CodeCode Available
DLaVA: Document Language and Vision Assistant for Answer Localization with Enhanced Interpretability and Trustworthiness	Nov 29, 2024	Optical Character Recognition (OCR)Question Answering	CodeCode Available

Show:10 25 50

← PrevPage 75 of 87Next →

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