Semantic Instance Segmentation via Deep Metric Learning

Understand

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.