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 401–410 of 2167 papers

Title	Date	Tasks	Status	Hype
Reliable Visual Question Answering: Abstain Rather Than Answer Incorrectly	Apr 28, 2022	Question AnsweringVisual Question Answering	CodeCode Available	1
GRIT: General Robust Image Task Benchmark	Apr 28, 2022	Instance SegmentationKeypoint Detection	CodeCode Available	1
RelViT: Concept-guided Vision Transformer for Visual Relational Reasoning	Apr 24, 2022	Human-Object Interaction DetectionObject	CodeCode Available	1
Hypergraph Transformer: Weakly-supervised Multi-hop Reasoning for Knowledge-based Visual Question Answering	Apr 22, 2022	Question AnsweringVisual Question Answering	CodeCode Available	1
Attention in Reasoning: Dataset, Analysis, and Modeling	Apr 20, 2022	Question AnsweringVisual Question Answering	CodeCode Available	1
SwapMix: Diagnosing and Regularizing the Over-Reliance on Visual Context in Visual Question Answering	Apr 5, 2022	Data AugmentationQuestion Answering	CodeCode Available	1
CLEVR-X: A Visual Reasoning Dataset for Natural Language Explanations	Apr 5, 2022	Explanation GenerationQuestion Answering	CodeCode Available	1
End-to-end Document Recognition and Understanding with Dessurt	Mar 30, 2022	document understandingVisual Question Answering (VQA)	CodeCode Available	1
Learning to Answer Questions in Dynamic Audio-Visual Scenarios	Mar 26, 2022	audio-visual learningAudio-visual Question Answering	CodeCode Available	1
MuKEA: Multimodal Knowledge Extraction and Accumulation for Knowledge-based Visual Question Answering	Mar 17, 2022	Implicit RelationsQuestion Answering	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