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 1981–1990 of 2167 papers

Title	Date	Tasks	Status
Enhancing the AI2 Diagrams Dataset Using Rhetorical Structure Theory	May 1, 2018	Question AnsweringVisual Question Answering (VQA)	CodeCode Available
Large Scale Scene Text Verification with Guided Attention	Apr 23, 2018	Question AnsweringScene Text Detection	—Unverified
ECG Heartbeat Classification: A Deep Transferable Representation	Apr 19, 2018	Arrhythmia DetectionElectrocardiography (ECG)	CodeCode Available
Question Type Guided Attention in Visual Question Answering	Apr 6, 2018	Activity RecognitionQuestion Answering	—Unverified
Improved Fusion of Visual and Language Representations by Dense Symmetric Co-Attention for Visual Question Answering	Apr 3, 2018	Visual Question AnsweringVisual Question Answering (VQA)	CodeCode Available
Differential Attention for Visual Question Answering	Apr 1, 2018	Question AnsweringVisual Question Answering	CodeCode Available
Visual Question Reasoning on General Dependency Tree	Mar 31, 2018	Question AnsweringVisual Question Answering	—Unverified
DDRprog: A CLEVR Differentiable Dynamic Reasoning Programmer	Mar 30, 2018	Question AnsweringVisual Question Answering	—Unverified
Two can play this Game: Visual Dialog with Discriminative Question Generation and Answering	Mar 29, 2018	Image CaptioningQuestion Answering	—Unverified
Motion-Appearance Co-Memory Networks for Video Question Answering	Mar 29, 2018	Question AnsweringVideo Question 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