  Instance segmentation methods require large datasets with expensive and thus
limited instance-level mask labels. Partially supervised instance segmentation
aims to improve mask prediction with limited mask labels by utilizing the more
abundant weak box labels. In this work, we show that a class agnostic mask
head, commonly used in partially supervised instance segmentation, has
difficulties learning a general concept of foreground for the weakly annotated
classes using box supervision only. To resolve this problem we introduce an
object mask prior (OMP) that provides the mask head with the general concept of
foreground implicitly learned by the box classification head under the
supervision of all classes. This helps the class agnostic mask head to focus on
the primary object in a region of interest (RoI) and improves generalization to
the weakly annotated classes. We test our approach on the COCO dataset using
different splits of strongly and weakly supervised classes. Our approach
significantly improves over the Mask R-CNN baseline and obtains competitive
performance with the state-of-the-art, while offering a much simpler
architecture.
