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 1851–1875 of 2167 papers

Title	Date	Tasks	Status	Hype
Grounded Word Sense Translation	Jun 1, 2019	Grounded language learningMachine Translation	—Unverified	0
ImageTTR: Grounding Type Theory with Records in Image Classification for Visual Question Answering	Jun 1, 2019	General Classificationimage-classification	—Unverified	0
Viewport Proposal CNN for 360deg Video Quality Assessment	Jun 1, 2019	Saliency PredictionVideo Quality Assessment	CodeCode Available	0
Dynamic Fusion With Intra- and Inter-Modality Attention Flow for Visual Question Answering	Jun 1, 2019	Question AnsweringVisual Question Answering	—Unverified	0
OK-VQA: A Visual Question Answering Benchmark Requiring External Knowledge	May 31, 2019	object-detectionObject Detection	CodeCode Available	1
Scene Text Visual Question Answering	May 31, 2019	Question AnsweringVisual Question Answering	CodeCode Available	1
What Can Neural Networks Reason About?	May 30, 2019	Question AnsweringVisual Question Answering	CodeCode Available	0
Vision-to-Language Tasks Based on Attributes and Attention Mechanism	May 29, 2019	Image CaptioningQuestion Answering	—Unverified	0
Leveraging Medical Visual Question Answering with Supporting Facts	May 28, 2019	DiversityMedical Visual Question Answering	—Unverified	0
Structure Learning for Neural Module Networks	May 27, 2019	Question AnsweringVisual Question Answering	—Unverified	0
Why do These Match? Explaining the Behavior of Image Similarity Models	May 26, 2019	AttributeGeneral Classification	CodeCode Available	0
Self-Critical Reasoning for Robust Visual Question Answering	May 24, 2019	Question AnsweringVisual Question Answering	CodeCode Available	0
Aligning Visual Regions and Textual Concepts for Semantic-Grounded Image Representations	May 15, 2019	Image CaptioningQuestion Answering	CodeCode Available	0
Misleading Failures of Partial-input Baselines	May 14, 2019	Natural Language InferenceVisual Question Answering (VQA)	—Unverified	0
Quantifying and Alleviating the Language Prior Problem in Visual Question Answering	May 13, 2019	Information RetrievalQuestion Answering	CodeCode Available	0
Language-Conditioned Graph Networks for Relational Reasoning	May 10, 2019	ObjectReferring Expression Comprehension	CodeCode Available	0
Visual TTR - Modelling Visual Question Answering in Type Theory with Records	May 1, 2019	Question AnsweringVisual Question Answering	—Unverified	0
Routing Networks and the Challenges of Modular and Compositional Computation	Apr 29, 2019	Language ModelingLanguage Modelling	CodeCode Available	0
The Neuro-Symbolic Concept Learner: Interpreting Scenes, Words, and Sentences From Natural Supervision	Apr 26, 2019	Image-text RetrievalObject	CodeCode Available	0
Scene Graph Prediction with Limited Labels	Apr 25, 2019	Knowledge Base CompletionPrediction	CodeCode Available	0
Progressive Attention Memory Network for Movie Story Question Answering	Apr 18, 2019	Question AnsweringVideo Story QA	—Unverified	0
Learning to Collocate Neural Modules for Image Captioning	Apr 18, 2019	DecoderImage Captioning	—Unverified	0
Towards VQA Models That Can Read	Apr 18, 2019	TextVQAVisual Question Answering (VQA)	CodeCode Available	3
Question Guided Modular Routing Networks for Visual Question Answering	Apr 17, 2019	Question AnsweringVisual Question Answering	—Unverified	0
Evaluating the Representational Hub of Language and Vision Models	Apr 12, 2019	DiagnosticQuestion 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