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 1571–1580 of 2167 papers

Title	Date	Tasks	Status	Hype
WeaQA: Weak Supervision via Captions for Visual Question Answering	Dec 4, 2020	Question AnsweringVisual Question Answering	—Unverified	0
Understanding Guided Image Captioning Performance across Domains	Dec 4, 2020	DescriptiveImage Captioning	CodeCode Available	0
Towards Knowledge-Augmented Visual Question Answering	Dec 1, 2020	General KnowledgeGraph Attention	CodeCode Available	0
A Unified Framework for Multilingual and Code-Mixed Visual Question Answering	Dec 1, 2020	Question AnsweringVisual Question Answering	—Unverified	0
Open-Ended Multi-Modal Relational Reasoning for Video Question Answering	Dec 1, 2020	Question AnsweringRelational Reasoning	CodeCode Available	0
Just Ask: Learning to Answer Questions from Millions of Narrated Videos	Dec 1, 2020	Question AnsweringQuestion Generation	CodeCode Available	1
Multimodal Graph Networks for Compositional Generalization in Visual Question Answering	Dec 1, 2020	Graph Neural NetworkQuestion Answering	—Unverified	0
Patch-VQ: 'Patching Up' the Video Quality Problem	Nov 27, 2020	Video Quality AssessmentVisual Question Answering (VQA)	CodeCode Available	1
Point and Ask: Incorporating Pointing into Visual Question Answering	Nov 27, 2020	Question AnsweringVisual Question Answering	CodeCode Available	1
Learning from Lexical Perturbations for Consistent Visual Question Answering	Nov 26, 2020	Question AnsweringVisual Question Answering	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