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 531–540 of 2167 papers

Title	Date	Tasks	Status	Hype	Score
EarthVQA: Towards Queryable Earth via Relational Reasoning-Based Remote Sensing Visual Question Answering	Dec 19, 2023	ObjectObject Counting	CodeCode Available	1	5
Break It Down: A Question Understanding Benchmark	Jan 31, 2020	Open-Domain Question AnsweringQuestion Answering	CodeCode Available	1	5
Mining Fine-Grained Image-Text Alignment for Zero-Shot Captioning via Text-Only Training	Jan 4, 2024	DescriptiveImage Captioning	CodeCode Available	1	5
Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering	Jul 25, 2017	Image CaptioningVisual Question Answering	CodeCode Available	1	5
DualVGR: A Dual-Visual Graph Reasoning Unit for Video Question Answering	Jul 10, 2021	Graph AttentionQuestion Answering	CodeCode Available	1	5
eP-ALM: Efficient Perceptual Augmentation of Language Models	Mar 20, 2023	In-Context LearningVisual Question Answering (VQA)	CodeCode Available	1	5
MISS: A Generative Pretraining and Finetuning Approach for Med-VQA	Jan 10, 2024	Medical Visual Question AnsweringMulti-Task Learning	CodeCode Available	1	5
Mind Your Outliers! Investigating the Negative Impact of Outliers on Active Learning for Visual Question Answering	Jul 6, 2021	Active LearningObject Recognition	CodeCode Available	1	5
Mimic In-Context Learning for Multimodal Tasks	Apr 11, 2025	In-Context LearningVisual Question Answering (VQA)	CodeCode Available	1	5
MineAnyBuild: Benchmarking Spatial Planning for Open-world AI Agents	May 26, 2025	BenchmarkingMinecraft	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