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 51–75 of 2167 papers

Title	Date	Tasks	Status	Hype
MDAgents: An Adaptive Collaboration of LLMs for Medical Decision-Making	Apr 22, 2024	Decision MakingMedical Diagnosis	CodeCode Available	3
Benchmarking Multimodal Retrieval Augmented Generation with Dynamic VQA Dataset and Self-adaptive Planning Agent	Nov 5, 2024	BenchmarkingHallucination	CodeCode Available	3
Pythia v0.1: the Winning Entry to the VQA Challenge 2018	Jul 26, 2018	Data AugmentationVisual Question Answering (VQA)	CodeCode Available	3
Florence-VL: Enhancing Vision-Language Models with Generative Vision Encoder and Depth-Breadth Fusion	Dec 5, 2024	Contrastive LearningHallucination	CodeCode Available	3
Evaluating Text-to-Visual Generation with Image-to-Text Generation	Apr 1, 2024	Image to textQuestion Answering	CodeCode Available	3
Lyra: An Efficient and Speech-Centric Framework for Omni-Cognition	Dec 12, 2024	EgoSchema	CodeCode Available	3
AutoHallusion: Automatic Generation of Hallucination Benchmarks for Vision-Language Models	Jun 16, 2024	HallucinationHallucination Evaluation	CodeCode Available	3
Vision-Language Pre-training: Basics, Recent Advances, and Future Trends	Oct 17, 2022	Few-Shot LearningImage Captioning	CodeCode Available	3
Emu: Generative Pretraining in Multimodality	Jul 11, 2023	Image CaptioningImage Generation	CodeCode Available	3
ONE-PEACE: Exploring One General Representation Model Toward Unlimited Modalities	May 18, 2023	1 Image, 2*2 StitchiAction Classification	CodeCode Available	3
OCR-free Document Understanding Transformer	Nov 30, 2021	Document Image Classificationdocument understanding	CodeCode Available	3
DriveLM: Driving with Graph Visual Question Answering	Dec 21, 2023	Autonomous DrivingQuestion Answering	CodeCode Available	3
Common Sense Reasoning for Deepfake Detection	Jan 31, 2024	Binary ClassificationCommon Sense Reasoning	CodeCode Available	3
All You May Need for VQA are Image Captions	May 4, 2022	AllImage Captioning	CodeCode Available	3
An Empirical Study on Prompt Compression for Large Language Models	Apr 24, 2025	ArticlesMath	CodeCode Available	3
MedTrinity-25M: A Large-scale Multimodal Dataset with Multigranular Annotations for Medicine	Aug 6, 2024	Medical Visual Question AnsweringOrgan Detection	CodeCode Available	3
Bilinear Attention Networks	May 21, 2018	Visual Question AnsweringVisual Question Answering (VQA)	CodeCode Available	3
PreFLMR: Scaling Up Fine-Grained Late-Interaction Multi-modal Retrievers	Feb 13, 2024	Question AnsweringRetrieval	CodeCode Available	3
MMedPO: Aligning Medical Vision-Language Models with Clinical-Aware Multimodal Preference Optimization	Dec 9, 2024	Visual Question Answering (VQA)	CodeCode Available	2
MiniGPT-Med: Large Language Model as a General Interface for Radiology Diagnosis	Jul 4, 2024	DiagnosticLanguage Modeling	CodeCode Available	2
MedPromptX: Grounded Multimodal Prompting for Chest X-ray Diagnosis	Mar 22, 2024	Medical DiagnosisMedical Visual Question Answering	CodeCode Available	2
Med-Flamingo: a Multimodal Medical Few-shot Learner	Jul 27, 2023	Medical Visual Question AnsweringQuestion Answering	CodeCode Available	2
CausalVQA: A Physically Grounded Causal Reasoning Benchmark for Video Models	Jun 11, 2025	counterfactualDescriptive	CodeCode Available	2
MDETR - Modulated Detection for End-to-End Multi-Modal Understanding	Jan 1, 2021	Phrase GroundingQuestion Answering	CodeCode Available	2
Med3DVLM: An Efficient Vision-Language Model for 3D Medical Image Analysis	Mar 25, 2025	Contrastive LearningImage-text Retrieval	CodeCode Available	2

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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	InternVL-C	Accuracy	81.2	—	Unverified
10	Lyrics	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