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 1421–1430 of 2167 papers

Title	Date	Tasks	Status
Bridging the Gap between Recognition-level Pre-training and Commonsensical Vision-language Tasks	May 1, 2022	DiversityInformativeness	—Unverified
DuReader_vis: A Chinese Dataset for Open-domain Document Visual Question Answering	May 1, 2022	document understandingOpen-Domain Question Answering	—Unverified
Vision-Language Pretraining: Current Trends and the Future	May 1, 2022	Question AnsweringRepresentation Learning	—Unverified
Multimodal Adaptive Distillation for Leveraging Unimodal Encoders for Vision-Language Tasks	Apr 22, 2022	Question AnsweringVisual Commonsense Reasoning	—Unverified
LayoutLMv3: Pre-training for Document AI with Unified Text and Image Masking	Apr 18, 2022	cross-modal alignmentDocument AI	CodeCode Available
Attention Mechanism based Cognition-level Scene Understanding	Apr 17, 2022	Question AnsweringScene Understanding	—Unverified
Improving Cross-Modal Understanding in Visual Dialog via Contrastive Learning	Apr 15, 2022	Contrastive LearningQuestion Answering	—Unverified
Question-Driven Graph Fusion Network For Visual Question Answering	Apr 3, 2022	Graph AttentionObject	—Unverified
Co-VQA : Answering by Interactive Sub Question Sequence	Apr 2, 2022	Question AnsweringVisual Question Answering	—Unverified
Perceptual Quality Assessment of UGC Gaming Videos	Mar 31, 2022	Video Quality AssessmentVisual Question Answering (VQA)	—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