Commonality-parsing network across shape and appearance for partially supervised instance segmentation

Qi Fan, Lei Ke, Wenjie Pei, Chi-Keung Tang, Yu-Wing Tai

Abstract:

Partially supervised instance segmentation aims to perform
learning on limited mask-annotated categories of data thus eliminating
expensive and exhaustive mask annotation. The learned models are expected to be generalizable to novel categories. Existing methods either
learn a transfer function from detection to segmentation, or cluster shape
priors for segmenting novel categories. We propose to learn the underlying class-agnostic commonalities that can be generalized from maskannotated categories to novel categories. Specifically, we parse two types
of commonalities: 1) shape commonalities which are learned by performing supervised learning on instance boundary prediction; and 2) appearance commonalities which are captured by modeling pairwise affinities
among pixels of feature maps to optimize the separability between instance and the background. Incorporating both the shape and appearance commonalities, our model significantly outperforms the state-ofthe-art methods on both partially supervised setting and few-shot setting for instance segmentation on COCO dataset.