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 1926–1950 of 2167 papers

Title	Date	Tasks	Status	Hype
Transfer Learning via Unsupervised Task Discovery for Visual Question Answering	Oct 3, 2018	Question AnsweringTransfer Learning	CodeCode Available	0
Multimodal Differential Network for Visual Question Generation	Oct 1, 2018	Image CaptioningNatural Questions	—Unverified	0
Convolutional Neural Networks for Video Quality Assessment	Sep 26, 2018	Video Quality AssessmentVisual Question Answering (VQA)	—Unverified	0
Textually Enriched Neural Module Networks for Visual Question Answering	Sep 23, 2018	Image CaptioningQuestion Answering	—Unverified	0
The Wisdom of MaSSeS: Majority, Subjectivity, and Semantic Similarity in the Evaluation of VQA	Sep 12, 2018	Question AnsweringSemantic Similarity	—Unverified	0
Faithful Multimodal Explanation for Visual Question Answering	Sep 8, 2018	Explanatory Visual Question AnsweringQuestion Answering	CodeCode Available	1
Cascaded Mutual Modulation for Visual Reasoning	Sep 6, 2018	Question AnsweringVisual Question Answering	CodeCode Available	0
Interpretable Visual Question Answering by Reasoning on Dependency Trees	Sep 6, 2018	Question Answeringvalid	—Unverified	0
Visual Coreference Resolution in Visual Dialog using Neural Module Networks	Sep 6, 2018	Common Sense Reasoningcoreference-resolution	CodeCode Available	0
Straight to the Facts: Learning Knowledge Base Retrieval for Factual Visual Question Answering	Sep 4, 2018	Factual Visual Question AnsweringGeneral Knowledge	—Unverified	0
Deep Attention Neural Tensor Network for Visual Question Answering	Sep 1, 2018	Deep AttentionQuestion Answering	—Unverified	0
Deep Video Quality Assessor: From Spatio-temporal Visual Sensitivity to A Convolutional Neural Aggregation Network	Sep 1, 2018	SensitivityVideo Quality Assessment	—Unverified	0
Adapting Visual Question Answering Models for Enhancing Multimodal Community Q&A Platforms	Aug 29, 2018	Community Question AnsweringGeneral Classification	CodeCode Available	0
Question-Guided Hybrid Convolution for Visual Question Answering	Aug 8, 2018	Question AnsweringVisual Question Answering	—Unverified	0
A Joint Sequence Fusion Model for Video Question Answering and Retrieval	Aug 7, 2018	DecoderMultiple-choice	CodeCode Available	0
Visual Reasoning with Multi-hop Feature Modulation	Aug 3, 2018	Question AnsweringVisual Dialog	CodeCode Available	0
Visual Question Answering Dataset for Bilingual Image Understanding: A Study of Cross-Lingual Transfer Using Attention Maps	Aug 1, 2018	Cross-Lingual TransferImage Captioning	—Unverified	0
A Reinforcement Learning Framework for Natural Question Generation using Bi-discriminators	Aug 1, 2018	AttributeNatural Questions	—Unverified	0
Learning Visual Question Answering by Bootstrapping Hard Attention	Aug 1, 2018	Hard AttentionQuestion Answering	CodeCode Available	0
Interpretable Visual Question Answering by Visual Grounding from Attention Supervision Mining	Aug 1, 2018	Question AnsweringVisual Grounding	—Unverified	0
Bridge the Gap Between VQA and Human Behavior on Omnidirectional Video: A Large-Scale Dataset and a Deep Learning Model	Jul 29, 2018	Visual Question Answering (VQA)	CodeCode Available	0
Pythia v0.1: the Winning Entry to the VQA Challenge 2018	Jul 26, 2018	Data AugmentationVisual Question Answering (VQA)	CodeCode Available	3
Question Relevance in Visual Question Answering	Jul 23, 2018	Question AnsweringVisual Question Answering	—Unverified	0
Latent Alignment and Variational Attention	Jul 10, 2018	Hard AttentionMachine Translation	CodeCode Available	0
NMT-Keras: a Very Flexible Toolkit with a Focus on Interactive NMT and Online Learning	Jul 9, 2018	General ClassificationMachine Translation	—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