  Obtaining precise instance segmentation masks is of high importance in many
modern applications such as robotic manipulation and autonomous driving.
Currently, many state of the art models are based on the Mask R-CNN framework
which, while very powerful, outputs masks at low resolutions which could result
in imprecise boundaries. On the other hand, classic variational methods for
segmentation impose desirable global and local data and geometry constraints on
the masks by optimizing an energy functional. While mathematically elegant,
their direct dependence on good initialization, non-robust image cues and
manual setting of hyperparameters renders them unsuitable for modern
applications. We propose LevelSet R-CNN, which combines the best of both worlds
by obtaining powerful feature representations that are combined in an
end-to-end manner with a variational segmentation framework. We demonstrate the
effectiveness of our approach on COCO and Cityscapes datasets.
