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 1251–1275 of 2167 papers

Title	Date	Tasks	Status	Hype
Enabling Multimodal Generation on CLIP via Vision-Language Knowledge Distillation	Mar 12, 2022	Image CaptioningKnowledge Distillation	—Unverified	0
NLX-GPT: A Model for Natural Language Explanations in Vision and Vision-Language Tasks	Mar 9, 2022	Decision MakingExplainable artificial intelligence	CodeCode Available	1
AssistQ: Affordance-centric Question-driven Task Completion for Egocentric Assistant	Mar 8, 2022	Visual Question Answering (VQA)	CodeCode Available	1
Barlow constrained optimization for Visual Question Answering	Mar 7, 2022	Question AnsweringVisual Question Answering	CodeCode Available	0
Dynamic Key-value Memory Enhanced Multi-step Graph Reasoning for Knowledge-based Visual Question Answering	Mar 6, 2022	Graph AttentionQuestion Answering	CodeCode Available	0
Modeling Coreference Relations in Visual Dialog	Mar 6, 2022	Question AnsweringVisual Dialog	—Unverified	0
Recent, rapid advancement in visual question answering architecture: a review	Mar 2, 2022	Question AnsweringVisual Question Answering	—Unverified	0
Unsupervised Vision-and-Language Pre-training via Retrieval-based Multi-Granular Alignment	Mar 1, 2022	RetrievalSentence	—Unverified	0
Joint Answering and Explanation for Visual Commonsense Reasoning	Feb 25, 2022	Knowledge DistillationQuestion Answering	CodeCode Available	0
On Modality Bias Recognition and Reduction	Feb 25, 2022	Action RecognitionMulti-modal Classification	CodeCode Available	0
Measuring CLEVRness: Blackbox testing of Visual Reasoning Models	Feb 24, 2022	BenchmarkingDiagnostic	—Unverified	0
Vision-Language Pre-Training with Triple Contrastive Learning	Feb 21, 2022	Contrastive Learningcross-modal alignment	CodeCode Available	2
OG-SGG: Ontology-Guided Scene Graph Generation. A Case Study in Transfer Learning for Telepresence Robotics	Feb 21, 2022	BIG-bench Machine LearningGraph Generation	CodeCode Available	0
RankDVQA: Deep VQA based on Ranking-inspired Hybrid Training	Feb 17, 2022	Video Quality AssessmentVisual Question Answering (VQA)	—Unverified	0
Privacy Preserving Visual Question Answering	Feb 15, 2022	Privacy PreservingQuestion Answering	—Unverified	0
Delving Deeper into Cross-lingual Visual Question Answering	Feb 15, 2022	Inductive BiasQuestion Answering	CodeCode Available	0
An experimental study of the vision-bottleneck in VQA	Feb 14, 2022	ObjectQuestion Answering	—Unverified	0
Can Open Domain Question Answering Systems Answer Visual Knowledge Questions?	Feb 9, 2022	Open-Domain Question AnsweringQuestion Answering	—Unverified	0
NEWSKVQA: Knowledge-Aware News Video Question Answering	Feb 8, 2022	Common Sense ReasoningManagement	—Unverified	0
OFA: Unifying Architectures, Tasks, and Modalities Through a Simple Sequence-to-Sequence Learning Framework	Feb 7, 2022	Image Captioningimage-classification	CodeCode Available	0
Webly Supervised Concept Expansion for General Purpose Vision Models	Feb 4, 2022	Human-Object Interaction DetectionImage Retrieval	—Unverified	0
Grounding Answers for Visual Questions Asked by Visually Impaired People	Feb 4, 2022	Question AnsweringVisual Question Answering	CodeCode Available	0
Compositionality as Lexical Symmetry	Jan 30, 2022	Data AugmentationInductive Bias	CodeCode Available	0
BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation	Jan 28, 2022	Image CaptioningImage-text matching	CodeCode Available	5
IGLUE: A Benchmark for Transfer Learning across Modalities, Tasks, and Languages	Jan 27, 2022	Cross-Modal RetrievalFew-Shot Learning	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