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 2091–2100 of 2167 papers

Title	Date	Tasks	Status	Hype
Tree Memory Networks for Modelling Long-term Temporal Dependencies	Mar 12, 2017	Machine TranslationPart-Of-Speech Tagging	—Unverified	0
Task-driven Visual Saliency and Attention-based Visual Question Answering	Feb 22, 2017	Question AnsweringVisual Question Answering	—Unverified	0
Vision and Language Integration: Moving beyond Objects	Jan 1, 2017	Action ClassificationImage Captioning	—Unverified	0
CLEVR: A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoning	Dec 20, 2016	DiagnosticQuestion Answering	CodeCode Available	1
The VQA-Machine: Learning How to Use Existing Vision Algorithms to Answer New Questions	Dec 16, 2016	BIG-bench Machine LearningQuestion Answering	—Unverified	0
Attentive Explanations: Justifying Decisions and Pointing to the Evidence	Dec 14, 2016	Decision MakingQuestion Answering	—Unverified	0
VIBIKNet: Visual Bidirectional Kernelized Network for Visual Question Answering	Dec 12, 2016	Question AnsweringVisual Question Answering	CodeCode Available	0
Making the V in VQA Matter: Elevating the Role of Image Understanding in Visual Question Answering	Dec 2, 2016	Visual Question AnsweringVisual Question Answering (VQA)	CodeCode Available	0
Visual Question Answering with Question Representation Update (QRU)	Dec 1, 2016	Question AnsweringVisual Question Answering	—Unverified	0
The Development of Multimodal Lexical Resources	Dec 1, 2016	Question AnsweringVisual Question Answering (VQA)	—Unverified	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