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.
Autori: | |
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 |