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

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 1971–1980 of 2167 papers

Title	Date	Tasks	Status	Hype
R-VQA: Learning Visual Relation Facts with Semantic Attention for Visual Question Answering	May 24, 2018	Question AnsweringRelation	CodeCode Available	1
Joint Image Captioning and Question Answering	May 22, 2018	Image CaptioningQuestion Answering	—Unverified	0
Bilinear Attention Networks	May 21, 2018	Visual Question AnsweringVisual Question Answering (VQA)	CodeCode Available	3
Reproducibility Report for "Learning To Count Objects In Natural Images For Visual Question Answering"	May 21, 2018	Question AnsweringVisual Question Answering	—Unverified	0
Did the Model Understand the Question?	May 14, 2018	modelQuestion Answering	CodeCode Available	0
Reciprocal Attention Fusion for Visual Question Answering	May 11, 2018	ObjectQuestion Answering	—Unverified	0
Edit me: A Corpus and a Framework for Understanding Natural Language Image Editing	May 1, 2018	Image CaptioningQuestion Answering	—Unverified	0
Augmenting Image Question Answering Dataset by Exploiting Image Captions	May 1, 2018	Data AugmentationImage Captioning	—Unverified	0
Action Verb Corpus	May 1, 2018	Action ClassificationLanguage Acquisition	—Unverified	0
Visual Choice of Plausible Alternatives: An Evaluation of Image-based Commonsense Causal Reasoning	May 1, 2018	Commonsense Causal ReasoningImage Captioning	CodeCode Available	0

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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