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 1941–1950 of 2167 papers

Title	Date	Tasks	Status
Viewport Proposal CNN for 360deg Video Quality Assessment	Jun 1, 2019	Saliency PredictionVideo Quality Assessment	CodeCode Available
InternLM-XComposer: A Vision-Language Large Model for Advanced Text-image Comprehension and Composition	Sep 26, 2023	ArticlesImage Comprehension	CodeCode Available
VIGNETTE: Socially Grounded Bias Evaluation for Vision-Language Models	May 28, 2025	Decision MakingQuestion Answering	CodeCode Available
BinaryVQA: A Versatile Test Set to Evaluate the Out-of-Distribution Generalization of VQA Models	Jan 28, 2023	Out-of-Distribution GeneralizationQuestion Answering	CodeCode Available
The Neuro-Symbolic Concept Learner: Interpreting Scenes, Words, and Sentences From Natural Supervision	Apr 26, 2019	Image-text RetrievalObject	CodeCode Available
Integrating Image Features with Convolutional Sequence-to-sequence Network for Multilingual Visual Question Answering	Mar 22, 2023	Question AnsweringVisual Question Answering	CodeCode Available
The Promise of Premise: Harnessing Question Premises in Visual Question Answering	May 1, 2017	Question AnsweringRelevance Detection	CodeCode Available
Proximity QA: Unleashing the Power of Multi-Modal Large Language Models for Spatial Proximity Analysis	Jan 31, 2024	Multi-Task LearningQuestion Answering	CodeCode Available
Are Red Roses Red? Evaluating Consistency of Question-Answering Models	Jul 1, 2019	Question Answeringvalid	CodeCode Available
Differentiable Outlier Detection Enable Robust Deep Multimodal Analysis	Feb 11, 2023	Image-text RetrievalKnowledge Graphs	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