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.
CITATION STYLE
Aslan, S., & Pelillo, M. (2019). Weakly supervised semantic segmentation using constrained dominant sets. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11752 LNCS, pp. 425–436). Springer Verlag. https://doi.org/10.1007/978-3-030-30645-8_39
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