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 126–150 of 2167 papers

Title	Date	Tasks	Status	Hype
MPDrive: Improving Spatial Understanding with Marker-Based Prompt Learning for Autonomous Driving	Apr 1, 2025	Autonomous DrivingPrompt Learning	—Unverified	0
KOFFVQA: An Objectively Evaluated Free-form VQA Benchmark for Large Vision-Language Models in the Korean Language	Mar 31, 2025	FormQuestion Answering	CodeCode Available	0
How Well Can Vison-Language Models Understand Humans' Intention? An Open-ended Theory of Mind Question Evaluation Benchmark	Mar 28, 2025	Question AnsweringVisual Question Answering	—Unverified	0
FaceBench: A Multi-View Multi-Level Facial Attribute VQA Dataset for Benchmarking Face Perception MLLMs	Mar 27, 2025	AttributeBenchmarking	CodeCode Available	1
Feature4X: Bridging Any Monocular Video to 4D Agentic AI with Versatile Gaussian Feature Fields	Mar 26, 2025	Question AnsweringVisual Question Answering	—Unverified	0
Vision-Amplified Semantic Entropy for Hallucination Detection in Medical Visual Question Answering	Mar 26, 2025	DiagnosticHallucination	—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
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
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
MAGIC-VQA: Multimodal And Grounded Inference with Commonsense Knowledge for Visual Question Answering	Mar 24, 2025	Graph Neural NetworkQuestion Answering	—Unverified	0
DiN: Diffusion Model for Robust Medical VQA with Semantic Noisy Labels	Mar 24, 2025	Medical Visual Question AnsweringQuestion 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
Progressive Prompt Detailing for Improved Alignment in Text-to-Image Generative Models	Mar 22, 2025	Question AnsweringVisual Question Answering	CodeCode Available	0
A Vision Centric Remote Sensing Benchmark	Mar 20, 2025	Question AnsweringRepresentation Learning	—Unverified	0
UPME: An Unsupervised Peer Review Framework for Multimodal Large Language Model Evaluation	Mar 19, 2025	Language Model EvaluationLanguage Modeling	—Unverified	0
TruthLens:A Training-Free Paradigm for DeepFake Detection	Mar 19, 2025	Binary ClassificationDeepFake Detection	—Unverified	0
ChatBEV: A Visual Language Model that Understands BEV Maps	Mar 18, 2025	Autonomous DrivingLanguage Modeling	—Unverified	0
Marten: Visual Question Answering with Mask Generation for Multi-modal Document Understanding	Mar 18, 2025	document understandingQuestion Answering	CodeCode Available	0
NuPlanQA: A Large-Scale Dataset and Benchmark for Multi-View Driving Scene Understanding in Multi-Modal Large Language Models	Mar 17, 2025	Question AnsweringScene Understanding	CodeCode Available	1
MicroVQA: A Multimodal Reasoning Benchmark for Microscopy-Based Scientific Research	Mar 17, 2025	ArticlesBenchmarking	CodeCode Available	1
GeoRSMLLM: A Multimodal Large Language Model for Vision-Language Tasks in Geoscience and Remote Sensing	Mar 16, 2025	Change DetectionImage Captioning	—Unverified	0
DynRsl-VLM: Enhancing Autonomous Driving Perception with Dynamic Resolution Vision-Language Models	Mar 14, 2025	Autonomous DrivingComputational Efficiency	—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	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