Weakly supervised semantic segmentation using constrained dominant sets
Küçük Resim Yok
Tarih
2019
Yazarlar
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