Semantic Instance Segmentation via Deep Metric Learning

2017-03-30 · Alireza Fathi, Zbigniew Wojna, Vivek Rathod, Peng Wang, Hyun Oh Song, Sergio Guadarrama, Kevin P. Murphy

We propose a new method for semantic instance segmentation, by first computing how likely two pixels are to belong to the same object, and then by grouping similar pixels together. Our similarity metric is based on a deep, fully convolutional embedding model. Our grouping method is based on selecting all points that are sufficiently similar to a set of "seed points", chosen from a deep, fully convolutional scoring model. We show competitive results on the Pascal VOC instance segmentation benchmark.

📄 PDF Abstract BibTeX arXiv:1703.10277

Code (1)

alicranck/instance-seg pytorch

Tasks

Instance Segmentation Metric Learning Object Proposal Generation Segmentation Semantic Segmentation

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