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 1371–1380 of 2167 papers

Title	Date	Tasks	Status
Davidsonian Scene Graph: Improving Reliability in Fine-grained Evaluation for Text-to-Image Generation	Oct 27, 2023	Image GenerationQuestion Answering	—Unverified
DCVQE: A Hierarchical Transformer for Video Quality Assessment	Oct 10, 2022	Video Quality AssessmentVisual Question Answering (VQA)	—Unverified
DDRprog: A CLEVR Differentiable Dynamic Reasoning Programmer	Mar 30, 2018	Question AnsweringVisual Question Answering	—Unverified
Dealing with Missing Modalities in the Visual Question Answer-Difference Prediction Task through Knowledge Distillation	Apr 13, 2021	Knowledge DistillationTriplet	—Unverified
Debating for Better Reasoning: An Unsupervised Multimodal Approach	May 20, 2025	Question AnsweringVisual Question Answering	—Unverified
Decouple Before Interact: Multi-Modal Prompt Learning for Continual Visual Question Answering	Jan 1, 2023	Continual LearningLanguage Modelling	—Unverified
Decoupled Box Proposal and Featurization with Ultrafine-Grained Semantic Labels Improve Image Captioning and Visual Question Answering	Sep 4, 2019	Image CaptioningObject	—Unverified
Deep Attention Neural Tensor Network for Visual Question Answering	Sep 1, 2018	Deep AttentionQuestion Answering	—Unverified
Deep Bayesian Active Learning for Multiple Correct Outputs	Dec 2, 2019	Active LearningAnswer Generation	—Unverified
Deep Equilibrium Multimodal Fusion	Jun 29, 2023	Visual Question Answering (VQA)	—Unverified

Show:10 25 50

← PrevPage 138 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