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 1361–1370 of 2167 papers

Title	Date	Tasks	Status
Understanding Attention for Vision-and-Language Tasks	Aug 17, 2022	Image GenerationImage Retrieval	CodeCode Available
ILLUME: Rationalizing Vision-Language Models through Human Interactions	Aug 17, 2022	Image CaptioningQuestion Answering	CodeCode Available
Aesthetic Visual Question Answering of Photographs	Aug 10, 2022	Question AnsweringSentiment Analysis	—Unverified
Prompt Tuning for Generative Multimodal Pretrained Models	Aug 4, 2022	Image CaptioningVisual Entailment	—Unverified
NAPA: Intermediate-level Variational Native-pulse Ansatz for Variational Quantum Algorithms	Aug 2, 2022	Neural Architecture SearchVisual Question Answering (VQA)	—Unverified
Video Question Answering with Iterative Video-Text Co-Tokenization	Aug 1, 2022	Question AnsweringVideo Question Answering	—Unverified
Parameter-Parallel Distributed Variational Quantum Algorithm	Jul 31, 2022	Visual Question Answering (VQA)	—Unverified
Uncertainty-based Visual Question Answering: Estimating Semantic Inconsistency between Image and Knowledge Base	Jul 27, 2022	Question AnsweringSemantic Similarity	—Unverified
Towards Complex Document Understanding By Discrete Reasoning	Jul 25, 2022	document understandingQuestion Answering	—Unverified
Is GPT-3 all you need for Visual Question Answering in Cultural Heritage?	Jul 25, 2022	AllQuestion Answering	—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