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 521–530 of 2167 papers

Title	Date	Tasks	Status	Hype
Removing Bias in Multi-modal Classifiers: Regularization by Maximizing Functional Entropies	Oct 21, 2020	Question AnsweringVisual Question Answering	CodeCode Available	1
Bayesian Attention Modules	Oct 20, 2020	Image CaptioningMachine Translation	CodeCode Available	1
Natural Language Rationales with Full-Stack Visual Reasoning: From Pixels to Semantic Frames to Commonsense Graphs	Oct 15, 2020	Language ModelingLanguage Modelling	CodeCode Available	1
Contrast and Classify: Training Robust VQA Models	Oct 13, 2020	Contrastive LearningData Augmentation	CodeCode Available	1
X-LXMERT: Paint, Caption and Answer Questions with Multi-Modal Transformers	Sep 23, 2020	Image CaptioningImage Generation	CodeCode Available	1
MUTANT: A Training Paradigm for Out-of-Distribution Generalization in Visual Question Answering	Sep 18, 2020	Out-of-Distribution GeneralizationQuestion Answering	CodeCode Available	1
A Comparison of Pre-trained Vision-and-Language Models for Multimodal Representation Learning across Medical Images and Reports	Sep 3, 2020	Image-text RetrievalMedical Visual Question Answering	CodeCode Available	1
A Dataset and Baselines for Visual Question Answering on Art	Aug 28, 2020	Question AnsweringQuestion Generation	CodeCode Available	1
DeVLBert: Learning Deconfounded Visio-Linguistic Representations	Aug 16, 2020	Image RetrievalQuestion Answering	CodeCode Available	1
Spatially Aware Multimodal Transformers for TextVQA	Jul 23, 2020	Optical Character Recognition (OCR)Spatial Reasoning	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