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

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Showing 2071–2080 of 2167 papers

Title	Date	Tasks	Status
GradBias: Unveiling Word Influence on Bias in Text-to-Image Generative Models	Aug 29, 2024	Bias DetectionFairness	CodeCode Available
RVTBench: A Benchmark for Visual Reasoning Tasks	May 17, 2025	Reasoning SegmentationVisual Question Answering (VQA)	CodeCode Available
Attention on Attention: Architectures for Visual Question Answering (VQA)	Mar 21, 2018	GPUQuestion Answering	CodeCode Available
Ask Your Neurons: A Deep Learning Approach to Visual Question Answering	May 9, 2016	Question AnsweringVisual Question Answering	CodeCode Available
Generalizing Visual Question Answering from Synthetic to Human-Written Questions via a Chain of QA with a Large Language Model	Jan 12, 2024	Language ModelingLanguage Modelling	CodeCode Available
General Greedy De-bias Learning	Dec 20, 2021	image-classificationImage Classification	CodeCode Available
What's in a Question: Using Visual Questions as a Form of Supervision	Apr 12, 2017	Data AugmentationForm	CodeCode Available
A Neuro-Symbolic ASP Pipeline for Visual Question Answering	May 16, 2022	Question AnsweringVisual Question Answering	CodeCode Available
GAMIVAL: Video Quality Prediction on Mobile Cloud Gaming Content	May 3, 2023	Video Quality AssessmentVisual Question Answering (VQA)	CodeCode Available
An Efficient Modern Baseline for FloodNet VQA	May 30, 2022	ManagementVisual Question Answering (VQA)	CodeCode Available

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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