Weakly supervised semantic segmentation using constrained dominant sets

Küçük Resim Yok

Tarih

2019

Dergi Başlığı

Dergi ISSN

Cilt Başlığı

Yayıncı

Springer Verlag

Erişim Hakkı

info:eu-repo/semantics/closedAccess

Özet

The availability of large-scale data sets is an essential prerequisite for deep learning based semantic segmentation schemes. Since obtaining pixel-level labels is extremely expensive, supervising deep semantic segmentation networks using low-cost weak annotations has been an attractive research problem in recent years. In this work, we explore the potential of Constrained Dominant Sets (CDS) for generating multi-labeled full mask predictions to train a fully convolutional network (FCN) for semantic segmentation. Our experimental results show that using CDS’s yields higher-quality mask predictions compared to methods that have been adopted in the literature for the same purpose. © Springer Nature Switzerland AG 2019.

Açıklama

20th International Conference on Image Analysis and Processing, ICIAP 2019 -- 9 September 2019 through 13 September 2019 -- 231579

Anahtar Kelimeler

Constrained Dominant Sets, Dominant sets, Semantic image segmentation, Weak training set annotations, Weakly supervised semantic segmentation

Kaynak

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

WoS Q Değeri

Scopus Q Değeri

Q3

Cilt

11752 LNCS

Sayı

Künye