Enhancing Generic Segmentation with Learned Region Representations
Abstract
Current successful approaches for generic (non-semantic) segmentation rely mostly on edge detection and have leveraged the strengths of deep learning mainly by improving the edge detection stage in the algorithmic pipeline. This is in contrast to semantic and instance segmentation, where DNNs are applied directly to generate pixel-wise segment representations. We propose a new method for learning a pixel-wise representation that reflects segment relatedness. This representation is combined with an edge map to yield a new segmentation algorithm. We show that the representations themselves achieve state-of-the-art segment similarity scores. Moreover, the proposed combined segmentation algorithm provides results that are either state of the art or improve upon it, for most quality measures.
Cite
@article{arxiv.1911.08564,
title = {Enhancing Generic Segmentation with Learned Region Representations},
author = {Or Isaacs and Oran Shayer and Michael Lindenbaum},
journal= {arXiv preprint arXiv:1911.08564},
year = {2020}
}
Comments
CVPR 2020. arXiv admin note: substantial text overlap with arXiv:1909.11735