  Analyzing complex scenes with Deep Neural Networks is a challenging task,
particularly when images contain multiple objects that partially occlude each
other. Existing approaches to image analysis mostly process objects
independently and do not take into account the relative occlusion of nearby
objects. In this paper, we propose a deep network for multi-object instance
segmentation that is robust to occlusion and can be trained from bounding box
supervision only. Our work builds on Compositional Networks, which learn a
generative model of neural feature activations to locate occluders and to
classify objects based on their non-occluded parts. We extend their generative
model to include multiple objects and introduce a framework for efficient
inference in challenging occlusion scenarios. In particular, we obtain
feed-forward predictions of the object classes and their instance and occluder
segmentations. We introduce an Occlusion Reasoning Module (ORM) that locates
erroneous segmentations and estimates the occlusion order to correct them. The
improved segmentation masks are, in turn, integrated into the network in a
top-down manner to improve the image classification. Our experiments on the
KITTI INStance dataset (KINS) and a synthetic occlusion dataset demonstrate the
effectiveness and robustness of our model at multi-object instance segmentation
under occlusion. Code is publically available at
https://github.com/XD7479/Multi-Object-Occlusion.
