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 521–530 of 2167 papers

Title	Date	Tasks	Status	Hype	Score
EHRXQA: A Multi-Modal Question Answering Dataset for Electronic Health Records with Chest X-ray Images	Oct 28, 2023	Decision MakingMedical Visual Question Answering	CodeCode Available	1	5
Are Bias Mitigation Techniques for Deep Learning Effective?	Apr 1, 2021	Deep LearningQuestion Answering	CodeCode Available	1	5
Meta-Learning via Classifier(-free) Diffusion Guidance	Oct 17, 2022	Few-Shot LearningImage Generation	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
MineAnyBuild: Benchmarking Spatial Planning for Open-world AI Agents	May 26, 2025	BenchmarkingMinecraft	CodeCode Available	1	5
Efficient Vision-Language Pretraining with Visual Concepts and Hierarchical Alignment	Aug 29, 2022	cross-modal alignmentImage-text Retrieval	CodeCode Available	1	5
Enhancing Vision-Language Pre-Training with Jointly Learned Questioner and Dense Captioner	May 19, 2023	Dense CaptioningImage Captioning	CodeCode Available	1	5
MicroVQA: A Multimodal Reasoning Benchmark for Microscopy-Based Scientific Research	Mar 17, 2025	ArticlesBenchmarking	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

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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