MetaSAug: Meta Semantic Augmentation for Long-Tailed Visual Recognition

Real-world training data usually exhibits long-tailed distribution, where several majority classes have a significantly larger number of samples than the remaining minority classes. This imbalance degrades the performance of typical supervised learning algorithms designed for balanced training sets....

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Bibliographische Detailangaben

Veröffentlicht in:	Proceedings (IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Online) S. 5208 - 5217
Hauptverfasser:	Li, Shuang, Gong, Kaixiong, Liu, Chi Harold, Wang, Yulin, Qiao, Feng, Cheng, Xinjing
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	IEEE 01.06.2021
Schlagworte:	Benchmark testing Computer vision Semantics Supervised learning Training Training data Visualization
ISSN:	1063-6919
Online-Zugang:	Volltext
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