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 1881–1890 of 2167 papers

Title	Date	Tasks	Status
LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding	Dec 29, 2020	Document Image ClassificationDocument Layout Analysis	CodeCode Available
Patent Figure Classification using Large Vision-language Models	Jan 22, 2025	ClassificationFew-Shot Learning	CodeCode Available
Task Formulation Matters When Learning Continually: A Case Study in Visual Question Answering	Sep 30, 2022	Continual LearningQuestion Answering	CodeCode Available
LAWS: Look Around and Warm-Start Natural Gradient Descent for Quantum Neural Networks	May 5, 2022	Combinatorial OptimizationVisual Question Answering (VQA)	CodeCode Available
Visuo-Linguistic Question Answering (VLQA) Challenge	May 1, 2020	Question AnsweringReading Comprehension	CodeCode Available
Latent Alignment and Variational Attention	Jul 10, 2018	Hard AttentionMachine Translation	CodeCode Available
ViCLEVR: A Visual Reasoning Dataset and Hybrid Multimodal Fusion Model for Visual Question Answering in Vietnamese	Oct 27, 2023	Information RetrievalNatural Language Queries	CodeCode Available
ViConsFormer: Constituting Meaningful Phrases of Scene Texts using Transformer-based Method in Vietnamese Text-based Visual Question Answering	Oct 18, 2024	Question AnsweringVisual Question Answering	CodeCode Available
Large Models in Dialogue for Active Perception and Anomaly Detection	Jan 27, 2025	Anomaly DetectionQuestion Answering	CodeCode Available
Large Language Models Understand Layout	Jul 8, 2024	Question AnsweringVisual Question Answering	CodeCode Available

Show:10 25 50

← PrevPage 189 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