  We propose a new and, arguably, a very simple reduction of instance
segmentation to semantic segmentation. This reduction allows to train
feed-forward non-recurrent deep instance segmentation systems in an end-to-end
fashion using architectures that have been proposed for semantic segmentation.
Our approach proceeds by introducing a fixed number of labels (colors) and then
dynamically assigning object instances to those labels during training
(coloring). A standard semantic segmentation objective is then used to train a
network that can color previously unseen images. At test time, individual
object instances can be recovered from the output of the trained convolutional
network using simple connected component analysis. In the experimental
validation, the coloring approach is shown to be capable of solving diverse
instance segmentation tasks arising in autonomous driving (the Cityscapes
benchmark), plant phenotyping (the CVPPP leaf segmentation challenge), and
high-throughput microscopy image analysis.
  The source code is publicly available:
https://github.com/kulikovv/DeepColoring.
