  This paper addresses weakly supervised amodal instance segmentation, where
the goal is to segment both visible and occluded (amodal) object parts, while
training provides only ground-truth visible (modal) segmentations. Following
prior work, we use data manipulation to generate occlusions in training images
and thus train a segmenter to predict amodal segmentations of the manipulated
data. The resulting predictions on training images are taken as the
pseudo-ground truth for the standard training of Mask-RCNN, which we use for
amodal instance segmentation of test images. For generating the pseudo-ground
truth, we specify a new Amodal Segmenter based on Boundary Uncertainty
estimation (ASBU) and make two contributions. First, while prior work uses the
occluder's mask, our ASBU uses the occlusion boundary as input. Second, ASBU
estimates an uncertainty map of the prediction. The estimated uncertainty
regularizes learning such that lower segmentation loss is incurred on regions
with high uncertainty. ASBU achieves significant performance improvement
relative to the state of the art on the COCOA and KINS datasets in three tasks:
amodal instance segmentation, amodal completion, and ordering recovery.
