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 1876–1900 of 2167 papers

Title	Date	Tasks	Status	Hype
Factor Graph Attention	Apr 11, 2019	Graph AttentionQuestion Answering	CodeCode Available	0
Text Guided Person Image Synthesis	Apr 10, 2019	AttributeImage Generation	—Unverified	0
Multi-Target Embodied Question Answering	Apr 9, 2019	Embodied Question AnsweringNavigate	CodeCode Available	0
Heterogeneous Memory Enhanced Multimodal Attention Model for Video Question Answering	Apr 8, 2019	Question AnsweringVideo Question Answering	CodeCode Available	0
Actively Seeking and Learning from Live Data	Apr 5, 2019	Domain AdaptationMeta-Learning	—Unverified	0
Can You Explain That? Lucid Explanations Help Human-AI Collaborative Image Retrieval	Apr 5, 2019	Image RetrievalQuestion Answering	—Unverified	0
MMED: A Multi-domain and Multi-modality Event Dataset	Apr 4, 2019	ArticlesQuestion Answering	—Unverified	0
Relation-Aware Graph Attention Network for Visual Question Answering	Mar 29, 2019	Graph AttentionImplicit Relations	CodeCode Available	0
Visual Query Answering by Entity-Attribute Graph Matching and Reasoning	Mar 16, 2019	AttributeGraph Matching	—Unverified	0
RAVEN: A Dataset for Relational and Analogical Visual rEasoNing	Mar 7, 2019	Object RecognitionQuestion Answering	—Unverified	0
Answer Them All! Toward Universal Visual Question Answering Models	Mar 1, 2019	AllQuestion Answering	CodeCode Available	0
MUREL: Multimodal Relational Reasoning for Visual Question Answering	Feb 25, 2019	Relational ReasoningVisual Question Answering	CodeCode Available	0
GQA: A New Dataset for Real-World Visual Reasoning and Compositional Question Answering	Feb 25, 2019	Question AnsweringVisual Question Answering (VQA)	CodeCode Available	1
Dual Attention Networks for Visual Reference Resolution in Visual Dialog	Feb 25, 2019	AI AgentQuestion Answering	CodeCode Available	0
Probabilistic Neural-symbolic Models for Interpretable Visual Question Answering	Feb 21, 2019	counterfactualQuestion Answering	—Unverified	0
Generating Natural Language Explanations for Visual Question Answering using Scene Graphs and Visual Attention	Feb 15, 2019	Explanation GenerationLanguage Modeling	—Unverified	0
Cycle-Consistency for Robust Visual Question Answering	Feb 15, 2019	Question AnsweringQuestion Generation	—Unverified	0
Taking a HINT: Leveraging Explanations to Make Vision and Language Models More Grounded	Feb 11, 2019	Image CaptioningQuestion Answering	—Unverified	0
VrR-VG: Refocusing Visually-Relevant Relationships	Feb 1, 2019	Image CaptioningQuestion Answering	—Unverified	0
BLOCK: Bilinear Superdiagonal Fusion for Visual Question Answering and Visual Relationship Detection	Jan 31, 2019	Question AnsweringRelationship Detection	CodeCode Available	0
Visual Entailment: A Novel Task for Fine-Grained Image Understanding	Jan 20, 2019	Natural Language InferenceQuestion Answering	—Unverified	0
CLEVR-Ref+: Diagnosing Visual Reasoning with Referring Expressions	Jan 3, 2019	DiagnosticImage Segmentation	CodeCode Available	0
The meaning of "most" for visual question answering models	Dec 31, 2018	Question AnsweringVisual Question Answering	—Unverified	0
Scene Graph Reasoning with Prior Visual Relationship for Visual Question Answering	Dec 23, 2018	Cross-Modal Information RetrievalInformation Retrieval	—Unverified	0
Focal Visual-Text Attention for Memex Question Answering	Dec 14, 2018	Memex Question AnsweringQuestion 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