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 2101–2110 of 2167 papers

Title	Date	Tasks	Status	Hype
A Dataset for Multimodal Question Answering in the Cultural Heritage Domain	Dec 1, 2016	Question AnsweringSpeech Recognition	—Unverified	0
Modeling Relationships in Referential Expressions with Compositional Modular Networks	Nov 30, 2016	Visual Question Answering (VQA)	CodeCode Available	0
Grad-CAM: Why did you say that?	Nov 22, 2016	Image CaptioningVisual Question Answering	CodeCode Available	0
Zero-Shot Visual Question Answering	Nov 17, 2016	Question AnsweringRetrieval	—Unverified	0
Leveraging Video Descriptions to Learn Video Question Answering	Nov 12, 2016	Question AnsweringVideo Question Answering	—Unverified	0
Dual Attention Networks for Multimodal Reasoning and Matching	Nov 2, 2016	Collaborative InferenceImage-text matching	CodeCode Available	0
Proposing Plausible Answers for Open-ended Visual Question Answering	Oct 20, 2016	Graph MatchingOpen-Ended Question Answering	—Unverified	0
Hadamard Product for Low-rank Bilinear Pooling	Oct 14, 2016	Visual Question AnsweringVisual Question Answering (VQA)	CodeCode Available	0
Open-Ended Visual Question-Answering	Oct 9, 2016	Question AnsweringSentence	CodeCode Available	0
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization	Oct 7, 2016	General ClassificationImage Attribution	CodeCode Available	1

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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