  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.
