  This work addresses the task of instance-aware semantic segmentation. Our key
motivation is to design a simple method with a new modelling-paradigm, which
therefore has a different trade-off between advantages and disadvantages
compared to known approaches. Our approach, we term InstanceCut, represents the
problem by two output modalities: (i) an instance-agnostic semantic
segmentation and (ii) all instance-boundaries. The former is computed from a
standard convolutional neural network for semantic segmentation, and the latter
is derived from a new instance-aware edge detection model. To reason globally
about the optimal partitioning of an image into instances, we combine these two
modalities into a novel MultiCut formulation. We evaluate our approach on the
challenging CityScapes dataset. Despite the conceptual simplicity of our
approach, we achieve the best result among all published methods, and perform
particularly well for rare object classes.
