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 131–140 of 2167 papers

Title	Date	Tasks	Status	Hype
Feature4X: Bridging Any Monocular Video to 4D Agentic AI with Versatile Gaussian Feature Fields	Mar 26, 2025	Question AnsweringVisual Question Answering	—Unverified	0
LEGO-Puzzles: How Good Are MLLMs at Multi-Step Spatial Reasoning?	Mar 25, 2025	Autonomous NavigationQuestion Answering	—Unverified	0
ORION: A Holistic End-to-End Autonomous Driving Framework by Vision-Language Instructed Action Generation	Mar 25, 2025	Action GenerationAutonomous Driving	—Unverified	0
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	0
Med3DVLM: An Efficient Vision-Language Model for 3D Medical Image Analysis	Mar 25, 2025	Contrastive LearningImage-text Retrieval	CodeCode Available	2
Where is this coming from? Making groundedness count in the evaluation of Document VQA models	Mar 24, 2025	Question AnsweringVisual Question Answering	—Unverified	0
DiN: Diffusion Model for Robust Medical VQA with Semantic Noisy Labels	Mar 24, 2025	Medical Visual Question AnsweringQuestion Answering	—Unverified	0
MAGIC-VQA: Multimodal And Grounded Inference with Commonsense Knowledge for Visual Question Answering	Mar 24, 2025	Graph Neural NetworkQuestion Answering	—Unverified	0
AMD-Hummingbird: Towards an Efficient Text-to-Video Model	Mar 24, 2025	Computational EfficiencyVideo Generation	CodeCode Available	1
Expanding the Boundaries of Vision Prior Knowledge in Multi-modal Large Language Models	Mar 23, 2025	Question AnsweringVisual Question Answering	—Unverified	0

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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