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 1031–1040 of 2167 papers

Title	Date	Tasks	Status	Score
Semantically Distributed Robust Optimization for Vision-and-Language Inference	Oct 14, 2021	Data AugmentationNatural Language Inference	CodeCode Available	5
Less Is More: Linear Layers on CLIP Features as Powerful VizWiz Model	Jun 10, 2022	Question AnsweringTask 2	—Unverified	0
LEGO-Puzzles: How Good Are MLLMs at Multi-Step Spatial Reasoning?	Mar 25, 2025	Autonomous NavigationQuestion Answering	—Unverified	0
Learning What Makes a Difference from Counterfactual Examples and Gradient Supervision	Apr 20, 2020	counterfactualimage-classification	—Unverified	0
Learning Visual Knowledge Memory Networks for Visual Question Answering	Jun 13, 2018	Question AnsweringVisual Question Answering	—Unverified	0
D-Rax: Domain-specific Radiologic assistant leveraging multi-modal data and eXpert model predictions	Jul 2, 2024	DiagnosticInstruction Following	—Unverified	0
An Evaluation of Image-Based Verb Prediction Models against Human Eye-Tracking Data	Jun 1, 2018	General ClassificationQuestion Answering	—Unverified	0
Learning to Specialize with Knowledge Distillation for Visual Question Answering	Dec 1, 2018	General ClassificationGeneral Knowledge	—Unverified	0
Learning to Select Question-Relevant Relations for Visual Question Answering	Jun 1, 2021	Graph AttentionQuestion Answering	—Unverified	0
Learning to Recognize the Unseen Visual Predicates	Sep 25, 2019	Question AnsweringVisual Question Answering	—Unverified	0

Show:10 25 50

← PrevPage 104 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