  Traditional Scene Understanding problems such as Object Detection and
Semantic Segmentation have made breakthroughs in recent years due to the
adoption of deep learning. However, the former task is not able to localise
objects at a pixel level, and the latter task has no notion of different
instances of objects of the same class. We focus on the task of Instance
Segmentation which recognises and localises objects down to a pixel level. Our
model is based on a deep neural network trained for semantic segmentation. This
network incorporates a Conditional Random Field with end-to-end trainable
higher order potentials based on object detector outputs. This allows us to
reason about instances from an initial, category-level semantic segmentation.
Our simple method effectively leverages the great progress recently made in
semantic segmentation and object detection. The accurate instance-level
segmentations that our network produces is reflected by the considerable
improvements obtained over previous work.
