  The requirement of expensive annotations is a major burden for training a
well-performed instance segmentation model. In this paper, we present an
economic active learning setting, named active pointly-supervised instance
segmentation (APIS), which starts with box-level annotations and iteratively
samples a point within the box and asks if it falls on the object. The key of
APIS is to find the most desirable points to maximize the segmentation accuracy
with limited annotation budgets. We formulate this setting and propose several
uncertainty-based sampling strategies. The model developed with these
strategies yields consistent performance gain on the challenging MS-COCO
dataset, compared against other learning strategies. The results suggest that
APIS, integrating the advantages of active learning and point-based
supervision, is an effective learning paradigm for label-efficient instance
segmentation.
