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.

本文提出了一种名为主动点监督实例分割 (APIS) 的经济型主动学习方案，它通过使用基于框注释的监督，并迭代地对框内的点进行采样和标注，找到最理想的点来最大化分割准确性并以有限的注释预算对模型进行训练。结果表明，APIS 是一种有效的学习范式，可以在标签效率高的情况下完成实例分割任务，并取得了与其他学习策略相比的一致性性能提升。