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 1761–1770 of 2167 papers

Title	Date	Tasks	Status
RUBi: Reducing Unimodal Biases for Visual Question Answering	Dec 1, 2019	Question AnsweringVisual Question Answering	CodeCode Available
A Free Lunch in Generating Datasets: Building a VQG and VQA System with Attention and Humans in the Loop	Nov 30, 2019	Question AnsweringQuestion Generation	—Unverified
Assessing the Robustness of Visual Question Answering Models	Nov 30, 2019	Question AnsweringVisual Question Answering	—Unverified
OptiBox: Breaking the Limits of Proposals for Visual Grounding	Nov 29, 2019	Image CaptioningVisual Grounding	—Unverified
Transfer Learning in Visual and Relational Reasoning	Nov 27, 2019	Question AnsweringRelational Reasoning	—Unverified
Unsupervised Keyword Extraction for Full-sentence VQA	Nov 23, 2019	Keyword ExtractionQuestion Answering	—Unverified
Temporal Reasoning via Audio Question Answering	Nov 21, 2019	Audio Question AnsweringDiagnostic	CodeCode Available
Explanation vs Attention: A Two-Player Game to Obtain Attention for VQA	Nov 19, 2019	Question AnsweringVisual Question Answering	—Unverified
DualVD: An Adaptive Dual Encoding Model for Deep Visual Understanding in Visual Dialogue	Nov 17, 2019	feature selectionQuestion Answering	CodeCode Available
Question-Conditioned Counterfactual Image Generation for VQA	Nov 14, 2019	counterfactualImage Generation	—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