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 671–680 of 2167 papers

Title	Date	Tasks	Status
@Bench: Benchmarking Vision-Language Models for Human-centered Assistive Technology	Sep 21, 2024	BenchmarkingDepth Estimation	—Unverified
``A Distorted Skull Lies in the Bottom Center...'' Identifying Paintings from Text Descriptions	Jun 1, 2016	Question AnsweringVisual Question Answering (VQA)	—Unverified
Image Captioning and Visual Question Answering Based on Attributes and External Knowledge	Mar 9, 2016	General KnowledgeImage Captioning	—Unverified
Improving Data Augmentation for Robust Visual Question Answering with Effective Curriculum Learning	Jan 28, 2024	Data AugmentationQuestion Answering	—Unverified
InfographicVQA	Apr 26, 2021	Question AnsweringVisual Question Answering	—Unverified
Hypo3D: Exploring Hypothetical Reasoning in 3D	Feb 2, 2025	Question AnsweringVisual Question Answering	—Unverified
ICDAR 2019 Competition on Scene Text Visual Question Answering	Jun 30, 2019	Question AnsweringVisual Question Answering	—Unverified
Debating for Better Reasoning: An Unsupervised Multimodal Approach	May 20, 2025	Question AnsweringVisual Question Answering	—Unverified
Bayesian Attention Belief Networks	Jun 9, 2021	DecoderMachine Translation	—Unverified
Dealing with Missing Modalities in the Visual Question Answer-Difference Prediction Task through Knowledge Distillation	Apr 13, 2021	Knowledge DistillationTriplet	—Unverified

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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