We present new approaches for the Instance Segmentation problem that are based on "Semantic Instance Segmentation with a Discriminative Loss Function" (https://arxiv.org/pdf/1708.02551.pdf) framework. We propose different approaches such as: modifying the suggested loss function to take into consideration critical parts of the object (edges, center), using MRF as post-processing action in order to improve the segments' borders, and building a neural-network that is capable of segmenting a unique object within a group of similar objects.
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