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 361–370 of 2167 papers

Title	Date	Tasks	Status	Hype	Score
Knowledge-Routed Visual Question Reasoning: Challenges for Deep Representation Embedding	Dec 14, 2020	Question AnsweringVisual Question Answering	CodeCode Available	1	5
AssistQ: Affordance-centric Question-driven Task Completion for Egocentric Assistant	Mar 8, 2022	Visual Question Answering (VQA)	CodeCode Available	1	5
Language Guided Visual Question Answering: Elevate Your Multimodal Language Model Using Knowledge-Enriched Prompts	Oct 31, 2023	Image CaptioningLanguage Modeling	CodeCode Available	1	5
Mimic In-Context Learning for Multimodal Tasks	Apr 11, 2025	In-Context LearningVisual Question Answering (VQA)	CodeCode Available	1	5
Learning Cooperative Visual Dialog Agents with Deep Reinforcement Learning	Mar 20, 2017	Deep Reinforcement Learningreinforcement-learning	CodeCode Available	1	5
FaceBench: A Multi-View Multi-Level Facial Attribute VQA Dataset for Benchmarking Face Perception MLLMs	Mar 27, 2025	AttributeBenchmarking	CodeCode Available	1	5
Linearly Mapping from Image to Text Space	Sep 30, 2022	Image CaptioningImage to text	CodeCode Available	1	5
Compositional Attention Networks for Machine Reasoning	Mar 8, 2018	Referring Expression ComprehensionVisual Question Answering (VQA)	CodeCode Available	1	5
Debiased Visual Question Answering from Feature and Sample Perspectives	Dec 1, 2021	Bias DetectionQuestion Answering	CodeCode Available	1	5
Debiasing Multimodal Models via Causal Information Minimization	Nov 28, 2023	Visual Question Answering (VQA)	CodeCode Available	1	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