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 1011–1020 of 2167 papers

Title	Date	Tasks	Status	Score
Dynamic Key-value Memory Enhanced Multi-step Graph Reasoning for Knowledge-based Visual Question Answering	Mar 6, 2022	Graph AttentionQuestion Answering	CodeCode Available	5
Breaking Annotation Barriers: Generalized Video Quality Assessment via Ranking-based Self-Supervision	May 6, 2025	Learning-To-RankSelf-Supervised Learning	CodeCode Available	5
LLaVA-OneVision: Easy Visual Task Transfer	Aug 6, 2024	3D Question Answering (3D-QA)	CodeCode Available	5
What is the Visual Cognition Gap between Humans and Multimodal LLMs?	Jun 14, 2024	object-detectionObject Detection	CodeCode Available	5
DVQA: Understanding Data Visualizations via Question Answering	Jan 24, 2018	ArticlesChart Question Answering	CodeCode Available	5
Learning Visual Question Answering by Bootstrapping Hard Attention	Aug 1, 2018	Hard AttentionQuestion Answering	CodeCode Available	5
Lightweight Recurrent Cross-modal Encoder for Video Question Answering	Jun 30, 2023	Action RecognitionQuestion Answering	CodeCode Available	5
LININ: Logic Integrated Neural Inference Network for Explanatory Visual Question Answering	Dec 24, 2024	Explanatory Visual Question AnsweringMultimodal Reasoning	CodeCode Available	5
DrishtiKon: Multi-Granular Visual Grounding for Text-Rich Document Images	Jun 26, 2025	document understandingOptical Character Recognition (OCR)	CodeCode Available	5
DualVD: An Adaptive Dual Encoding Model for Deep Visual Understanding in Visual Dialogue	Nov 17, 2019	feature selectionQuestion Answering	CodeCode Available	5

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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