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

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 1341–1350 of 2167 papers

Title	Date	Tasks	Status	Hype
Uncertainty-based Visual Question Answering: Estimating Semantic Inconsistency between Image and Knowledge Base	Nov 16, 2021	Question AnsweringSemantic Similarity	—Unverified	0
A Good Prompt Is Worth Millions of Parameters: Low-resource Prompt-based Learning for Vision-Language Models	Nov 16, 2021	Language ModelingLanguage Modelling	—Unverified	0
Question-Led Semantic Structure Enhanced Attentions for VQA	Nov 16, 2021	Question AnsweringVisual Question Answering	—Unverified	0
ViQuAE, a Dataset for Knowledge-based Visual Question Answering about Named Entities	Nov 16, 2021	ArticlesFace Recognition	CodeCode Available	0
Breaking Down Questions for Outside-Knowledge Visual Question Answering	Nov 16, 2021	Graph Neural NetworkQuestion Answering	—Unverified	0
Multi-Grained Vision Language Pre-Training: Aligning Texts with Visual Concepts	Nov 16, 2021	Cross-Modal RetrievalImage Captioning	CodeCode Available	1
Language bias in Visual Question Answering: A Survey and Taxonomy	Nov 16, 2021	Question AnsweringVisual Question Answering	—Unverified	0
Document AI: Benchmarks, Models and Applications	Nov 16, 2021	Deep LearningDocument AI	—Unverified	0
No-Reference Video Quality Assessment Based on Benford’s Law and Perceptual Features	Nov 12, 2021	No-Reference Image Quality AssessmentVideo Quality Assessment	CodeCode Available	0
Graph Relation Transformer: Incorporating pairwise object features into the Transformer architecture	Nov 11, 2021	Graph AttentionQuestion Answering	—Unverified	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