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 1901–1925 of 2167 papers

Title	Date	Tasks	Status	Hype
Dynamic Fusion with Intra- and Inter- Modality Attention Flow for Visual Question Answering	Dec 13, 2018	Question AnsweringVisual Question Answering	—Unverified	0
Learning Representations of Sets through Optimized Permutations	Dec 10, 2018	General ClassificationQuestion Answering	CodeCode Available	0
Spatial Knowledge Distillation to aid Visual Reasoning	Dec 10, 2018	DiagnosticKnowledge Distillation	—Unverified	0
Recursive Visual Attention in Visual Dialog	Dec 6, 2018	Question AnsweringVisual Dialog	CodeCode Available	0
Explainable and Explicit Visual Reasoning over Scene Graphs	Dec 5, 2018	Inductive BiasVisual Question Answering (VQA)	CodeCode Available	0
Learning to Compose Dynamic Tree Structures for Visual Contexts	Dec 5, 2018	Graph GenerationPanoptic Scene Graph Generation	CodeCode Available	2
Multi-task Learning of Hierarchical Vision-Language Representation	Dec 3, 2018	Multi-Task LearningQuestion Answering	—Unverified	0
Learning to Specialize with Knowledge Distillation for Visual Question Answering	Dec 1, 2018	General ClassificationGeneral Knowledge	—Unverified	0
Chain of Reasoning for Visual Question Answering	Dec 1, 2018	ObjectQuestion Answering	—Unverified	0
From Known to the Unknown: Transferring Knowledge to Answer Questions about Novel Visual and Semantic Concepts	Nov 30, 2018	Novel ConceptsQuestion Answering	—Unverified	0
Systematic Generalization: What Is Required and Can It Be Learned?	Nov 30, 2018	Systematic GeneralizationVisual Question Answering (VQA)	CodeCode Available	0
Visual Question Answering as Reading Comprehension	Nov 29, 2018	Common Sense ReasoningGeneral Knowledge	—Unverified	0
Visual Entailment Task for Visually-Grounded Language Learning	Nov 26, 2018	Grounded language learningNatural Language Inference	—Unverified	0
CLEAR: A Dataset for Compositional Language and Elementary Acoustic Reasoning	Nov 26, 2018	Acoustic Question AnsweringQuestion Answering	CodeCode Available	0
A dataset of clinically generated visual questions and answers about radiology images	Nov 20, 2018	Decision MakingMedical Visual Question Answering	—Unverified	0
VQA with no questions-answers training	Nov 20, 2018	Visual Question Answering (VQA)	CodeCode Available	0
Explicit Bias Discovery in Visual Question Answering Models	Nov 19, 2018	Question AnsweringVisual Question Answering	—Unverified	0
Zero-Shot Transfer VQA Dataset	Nov 2, 2018	Question AnsweringTransfer Learning	—Unverified	0
Out of the Box: Reasoning with Graph Convolution Nets for Factual Visual Question Answering	Nov 1, 2018	Factual Visual Question AnsweringGeneral Knowledge	—Unverified	0
Compositional Attention Networks for Interpretability in Natural Language Question Answering	Oct 30, 2018	Logical ReasoningQuestion Answering	—Unverified	0
Do Explanations make VQA Models more Predictable to a Human?	Oct 29, 2018	Question AnsweringVisual Question Answering	—Unverified	0
TallyQA: Answering Complex Counting Questions	Oct 29, 2018	AttributeObject Counting	CodeCode Available	0
Knowing Where to Look? Analysis on Attention of Visual Question Answering System	Oct 9, 2018	Question AnsweringVisual Question Answering	—Unverified	0
Overcoming Language Priors in Visual Question Answering with Adversarial Regularization	Oct 8, 2018	Question AnsweringVisual Grounding	—Unverified	0
Neural-Symbolic VQA: Disentangling Reasoning from Vision and Language Understanding	Oct 4, 2018	Question AnsweringRepresentation Learning	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