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 41–50 of 2167 papers

Title	Date	Tasks	Status	Hype	Score
All You May Need for VQA are Image Captions	May 4, 2022	AllImage Captioning	CodeCode Available	3	5
Emu: Generative Pretraining in Multimodality	Jul 11, 2023	Image CaptioningImage Generation	CodeCode Available	3	5
Evaluating Text-to-Visual Generation with Image-to-Text Generation	Apr 1, 2024	Image to textQuestion Answering	CodeCode Available	3	5
MMed-RAG: Versatile Multimodal RAG System for Medical Vision Language Models	Oct 16, 2024	DiagnosticHallucination	CodeCode Available	3	5
MDAgents: An Adaptive Collaboration of LLMs for Medical Decision-Making	Apr 22, 2024	Decision MakingMedical Diagnosis	CodeCode Available	3	5
MMSearch-R1: Incentivizing LMMs to Search	Jun 25, 2025	RAGRetrieval-augmented Generation	CodeCode Available	3	5
DriveLM: Driving with Graph Visual Question Answering	Dec 21, 2023	Autonomous DrivingQuestion Answering	CodeCode Available	3	5
Ludwig: a type-based declarative deep learning toolbox	Sep 17, 2019	DecoderDeep Learning	CodeCode Available	3	5
Lyra: An Efficient and Speech-Centric Framework for Omni-Cognition	Dec 12, 2024	EgoSchema	CodeCode Available	3	5
OCR-free Document Understanding Transformer	Nov 30, 2021	Document Image Classificationdocument understanding	CodeCode Available	3	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	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