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.
Aslan, Sinem (Corresponding)
|Data di pubblicazione:||2019|
|Titolo:||Weakly Supervised Semantic Segmentation Using Constrained Dominant Sets|
|Titolo del libro:||20th International Conference on Image Analysis and Processing, ICIAP 2019 - Proceedings|
|Digital Object Identifier (DOI):||http://dx.doi.org/10.1007/978-3-030-30645-8_39|
|Appare nelle tipologie:||4.1 Articolo in Atti di convegno|