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 121–130 of 2167 papers

Title	Date	Tasks	Status	Hype
QIRL: Boosting Visual Question Answering via Optimized Question-Image Relation Learning	Apr 4, 2025	Data AugmentationImage Generation	—Unverified	0
Hierarchical Modeling for Medical Visual Question Answering with Cross-Attention Fusion	Apr 4, 2025	DiagnosticMedical Visual Question Answering	—Unverified	0
STING-BEE: Towards Vision-Language Model for Real-World X-ray Baggage Security Inspection	Apr 3, 2025	Instruction FollowingLanguage Modeling	CodeCode Available	1
SocialGesture: Delving into Multi-person Gesture Understanding	Apr 3, 2025	Gesture RecognitionQuestion Answering	—Unverified	0
Reasoning LLMs for User-Aware Multimodal Conversational Agents	Apr 2, 2025	RAGRetrieval-augmented Generation	—Unverified	0
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

Show:10 25 50

← PrevPage 13 of 217Next →

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