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Instance segmentation methods require large datasets with expensive and thuslimited instance-level mask labels. Partially supervised instance segmentationaims to improve mask prediction with limited mask labels by utilizing the moreabundant weak box labels. In this work, we show that a class agnostic maskhead, commonly used in partially supervised instance segmentation, hasdifficulties learning a general concept of foreground for the weakly annotatedclasses using box supervision only. To resolve this problem we introduce anobject mask prior (OMP) that provides the mask head with the general concept offoreground implicitly learned by the box classification head under thesupervision of all classes. This helps the class agnostic mask head to focus onthe primary object in a region of interest (RoI) and improves generalization tothe weakly annotated classes. We test our approach on the COCO dataset usingdifferent splits of strongly and weakly supervised classes. Our approachsignificantly improves over the Mask R-CNN baseline and obtains competitiveperformance with the state-of-the-art, while offering a much simplerarchitecture.

Prior to Segment: Foreground Cues for Weakly Annotated Classes in Partially Supervised Instance Segmentation