  Instance segmentation is one of the actively studied research topics in
computer vision in which many objects of interest should be separated
individually. While many feed-forward networks produce high-quality
segmentation on different types of images, their results often suffer from
topological errors (merging or splitting) for segmentation of many objects,
requiring post-processing. Existing iterative methods, on the other hand,
extract a single object at a time using discriminative knowledge-based
properties (shapes, boundaries, etc.) without relying on post-processing, but
they do not scale well. To exploit the advantages of conventional
single-object-per-step segmentation methods without impairing the scalability,
we propose a novel iterative deep reinforcement learning agent that learns how
to differentiate multiple objects in parallel. Our reward function for the
trainable agent is designed to favor grouping pixels belonging to the same
object using a graph coloring algorithm. We demonstrate that the proposed
method can efficiently perform instance segmentation of many objects without
heavy post-processing.
