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 1471–1480 of 2167 papers

Title	Date	Tasks	Status
Text-Conditioned Generative Model of 3D Strand-based Human Hairstyles	Jan 1, 2024	Question AnsweringVisual Question Answering	—Unverified
Text Guided Person Image Synthesis	Apr 10, 2019	AttributeImage Generation	—Unverified
TextMatch: Enhancing Image-Text Consistency Through Multimodal Optimization	Dec 24, 2024	In-Context LearningQuestion Answering	—Unverified
DuReader_vis: A Chinese Dataset for Open-domain Document Visual Question Answering	May 1, 2022	document understandingOpen-Domain Question Answering	—Unverified
TextSquare: Scaling up Text-Centric Visual Instruction Tuning	Apr 19, 2024	HallucinationHallucination Evaluation	—Unverified
Textually Enriched Neural Module Networks for Visual Question Answering	Sep 23, 2018	Image CaptioningQuestion Answering	—Unverified
TG-VQA: Ternary Game of Video Question Answering	May 17, 2023	Contrastive LearningQuestion Answering	—Unverified
The Color of the Cat is Gray: 1 Million Full-Sentences Visual Question Answering (FSVQA)	Sep 21, 2016	Question AnsweringSentence	—Unverified
The curse of language biases in remote sensing VQA: the role of spatial attributes, language diversity, and the need for clear evaluation	Nov 28, 2023	DiversityQuestion Answering	—Unverified
The Development of Multimodal Lexical Resources	Dec 1, 2016	Question AnsweringVisual Question Answering (VQA)	—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