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 1741–1750 of 2167 papers

Title	Date	Tasks	Status
Multiple interaction learning with question-type prior knowledge for constraining answer search space in visual question answering	Sep 23, 2020	Question AnsweringVisual Question Answering	CodeCode Available
Multi-Image Visual Question Answering	Dec 27, 2021	Question AnsweringVisual Question Answering	CodeCode Available
Aligning Visual Regions and Textual Concepts for Semantic-Grounded Image Representations	May 15, 2019	Image CaptioningQuestion Answering	CodeCode Available
End-to-end optimization of goal-driven and visually grounded dialogue systems	Mar 15, 2017	DecoderDeep Reinforcement Learning	CodeCode Available
Multiscale Byte Language Models -- A Hierarchical Architecture for Causal Million-Length Sequence Modeling	Feb 20, 2025	DecoderGPU	CodeCode Available
Multi-Sourced Compositional Generalization in Visual Question Answering	May 29, 2025	Question AnsweringVisual Question Answering	CodeCode Available
Multi-Target Embodied Question Answering	Apr 9, 2019	Embodied Question AnsweringNavigate	CodeCode Available
End-to-End Instance Segmentation with Recurrent Attention	May 30, 2016	Autonomous DrivingImage Captioning	CodeCode Available
Multiview Contrastive Learning for Completely Blind Video Quality Assessment of User Generated Content	Jul 13, 2022	Contrastive LearningOptical Flow Estimation	CodeCode Available
MUREL: Multimodal Relational Reasoning for Visual Question Answering	Feb 25, 2019	Relational ReasoningVisual Question Answering	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