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 1191–1200 of 2167 papers

Title	Date	Tasks	Status	Hype
Visual Superordinate Abstraction for Robust Concept Learning	May 28, 2022	AttributeQuestion Answering	—Unverified	0
GIT: A Generative Image-to-text Transformer for Vision and Language	May 27, 2022	DecoderImage Captioning	CodeCode Available	2
V-Doc : Visual questions answers with Documents	May 27, 2022	Question AnsweringQuestion Generation	—Unverified	0
Avoiding Barren Plateaus with Classical Deep Neural Networks	May 26, 2022	Visual Question Answering (VQA)	—Unverified	0
Guiding Visual Question Answering with Attention Priors	May 25, 2022	Question AnsweringVisual Grounding	—Unverified	0
mPLUG: Effective and Efficient Vision-Language Learning by Cross-modal Skip-connections	May 24, 2022	Computational Efficiencycross-modal alignment	CodeCode Available	1
Reassessing Evaluation Practices in Visual Question Answering: A Case Study on Out-of-Distribution Generalization	May 24, 2022	Image CaptioningOut-of-Distribution Generalization	—Unverified	0
On Advances in Text Generation from Images Beyond Captioning: A Case Study in Self-Rationalization	May 24, 2022	DescriptiveImage Captioning	—Unverified	0
VQA-GNN: Reasoning with Multimodal Knowledge via Graph Neural Networks for Visual Question Answering	May 23, 2022	Knowledge GraphsQuestion Answering	—Unverified	0
PEVL: Position-enhanced Pre-training and Prompt Tuning for Vision-language Models	May 23, 2022	Language ModelingLanguage Modelling	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