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 871–880 of 2167 papers

Title	Date	Tasks	Status	Score
Med-PMC: Medical Personalized Multi-modal Consultation with a Proactive Ask-First-Observe-Next Paradigm	Aug 16, 2024	Decision MakingMedical Visual Question Answering	CodeCode Available	5
MedHallTune: An Instruction-Tuning Benchmark for Mitigating Medical Hallucination in Vision-Language Models	Feb 28, 2025	Decision MakingHallucination	CodeCode Available	5
Dataset and Benchmark for Urdu Natural Scenes Text Detection, Recognition and Visual Question Answering	May 21, 2024	DiversityInformation Retrieval	CodeCode Available	5
Medical Large Vision Language Models with Multi-Image Visual Ability	May 25, 2025	Question AnsweringVisual Question Answering (VQA)	CodeCode Available	5
Exploring Modulated Detection Transformer as a Tool for Action Recognition in Videos	Sep 21, 2022	Action DetectionAction Recognition	CodeCode Available	5
Exploring Models and Data for Image Question Answering	May 8, 2015	Image Segmentationobject-detection	CodeCode Available	5
Modulating early visual processing by language	Jul 2, 2017	Question AnsweringVisual Question Answering	CodeCode Available	5
Are Red Roses Red? Evaluating Consistency of Question-Answering Models	Jul 1, 2019	Question Answeringvalid	CodeCode Available	5
Marten: Visual Question Answering with Mask Generation for Multi-modal Document Understanding	Mar 18, 2025	document understandingQuestion Answering	CodeCode Available	5
MaMMUT: A Simple Architecture for Joint Learning for MultiModal Tasks	Mar 29, 2023	Cross-Modal RetrievalDecoder	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