Learning from Weak and Noisy Labels for Semantic Segmentation

A weakly supervised semantic segmentation (WSSS) method aims to learn a segmentation model from weak (image-level) as opposed to strong (pixel-level) labels. By avoiding the tedious pixel-level annotation process, it can exploit the unlimited supply of user-tagged images from media-sharing sites suc...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	IEEE transactions on pattern analysis and machine intelligence Jg. 39; H. 3; S. 486 - 500
Hauptverfasser:	Lu, Zhiwu, Fu, Zhenyong, Xiang, Tao, Han, Peng, Wang, Liwei, Gao, Xin
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States IEEE 01.03.2017 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms Computational modeling Image annotation Image segmentation label noise reduction Labeling Labels Machine learning Noise measurement Noise reduction Optimization Pixels Semantic segmentation Semantics sparse learning Training weakly supervised learning
ISSN:	0162-8828, 2160-9292, 1939-3539
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Schreiben Sie den ersten Kommentar!