  We present a learning approach for localization and segmentation of objects
in an image in a manner that is robust to partial occlusion. Our algorithm
produces a bounding box around the full extent of the object and labels pixels
in the interior that belong to the object. Like existing segmentation aware
detection approaches, we learn an appearance model of the object and consider
regions that do not fit this model as potential occlusions. However, in
addition to the established use of pairwise potentials for encouraging local
consistency, we use higher order potentials which capture information at the
level of im- age segments. We also propose an efficient loss function that
targets both localization and segmentation performance. Our algorithm achieves
13.52% segmentation error and 0.81 area under the false-positive per image vs.
recall curve on average over the challenging CMU Kitchen Occlusion Dataset.
This is a 42.44% decrease in segmentation error and a 16.13% increase in
localization performance compared to the state-of-the-art. Finally, we show
that the visibility labelling produced by our algorithm can make full 3D pose
estimation from a single image robust to occlusion.
