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 481–490 of 2167 papers

Title	Date	Tasks	Status	Hype
An Empirical Study of Training End-to-End Vision-and-Language Transformers	Nov 3, 2021	Cross-Modal RetrievalDecoder	CodeCode Available	1
StableVQA: A Deep No-Reference Quality Assessment Model for Video Stability	Aug 9, 2023	Optical Flow EstimationVideo Quality Assessment	CodeCode Available	1
STING-BEE: Towards Vision-Language Model for Real-World X-ray Baggage Security Inspection	Apr 3, 2025	Instruction FollowingLanguage Modeling	CodeCode Available	1
Disentangling 3D Prototypical Networks For Few-Shot Concept Learning	Nov 6, 2020	3D geometry3D Object Detection	CodeCode Available	1
Hierarchical Conditional Relation Networks for Video Question Answering	Feb 25, 2020	Audio-Visual Question Answering (AVQA)Question Answering	CodeCode Available	1
Bilateral Cross-Modality Graph Matching Attention for Feature Fusion in Visual Question Answering	Dec 14, 2021	Graph MatchingQuestion Answering	CodeCode Available	1
Gemini Goes to Med School: Exploring the Capabilities of Multimodal Large Language Models on Medical Challenge Problems & Hallucinations	Feb 10, 2024	DiagnosticHallucination	CodeCode Available	1
Distilled Dual-Encoder Model for Vision-Language Understanding	Dec 16, 2021	Image to textmodel	CodeCode Available	1
Bridging the Gap between 2D and 3D Visual Question Answering: A Fusion Approach for 3D VQA	Feb 24, 2024	3D Question Answering (3D-QA)Question Answering	CodeCode Available	1
GeneAnnotator: A Semi-automatic Annotation Tool for Visual Scene Graph	Sep 6, 2021	Graph GenerationGraph Learning	CodeCode Available	1

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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