  Most contemporary approaches to instance segmentation use complex pipelines
involving conditional random fields, recurrent neural networks, object
proposals, or template matching schemes. In our paper, we present a simple yet
powerful end-to-end convolutional neural network to tackle this task. Our
approach combines intuitions from the classical watershed transform and modern
deep learning to produce an energy map of the image where object instances are
unambiguously represented as basins in the energy map. We then perform a cut at
a single energy level to directly yield connected components corresponding to
object instances. Our model more than doubles the performance of the
state-of-the-art on the challenging Cityscapes Instance Level Segmentation
task.
