On Power Jaccard Losses for Semantic Segmentation

被引：20

作者：

Duque-Arias, David ^{[1
]}

Velasco-Forero, Santiago ^{[1
]}

Deschaud, Jean-Emmanuel ^{[1
]}

Goulette, Francois ^{[1
]}

Serna, Andres ^{[2
]}

Decenciere, Etienne ^{[1
]}

Marcotegui, Beatriz ^{[1
]}

机构：

[1] PSL Res Univ, MINES ParisTech, Paris, France

[2] Terra3D Res, Paris, France

来源：

VISAPP: PROCEEDINGS OF THE 16TH INTERNATIONAL JOINT CONFERENCE ON COMPUTER VISION, IMAGING AND COMPUTER GRAPHICS THEORY AND APPLICATIONS - VOL. 5: VISAPP | 2021年

关键词：

Loss Functions; Image Segmentation; Jaccard Loss; Deep Learning; U-Net Architecture;

D O I：

10.5220/0010304005610568

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this work, a new generalized loss function is proposed called power Jaccard to perform semantic segmentation tasks. It is compared with classical loss functions in different scenarios, including gray level and color image segmentation, as well as 3D point cloud segmentation. The results show improved performance, stability and convergence. We made available the code with our proposal with a demonstrative example.

引用

页码：561 / 568

页数：8

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