Two-step Active Learning for Instance Segmentation Neural Networks
Abstract
Training high quality instance segmentation models re-quires an abundance of labeled images containing instancemasks and classifications. Such data is often expensive toprocure. Active learning aims to achieve high performanceat minimal labeling cost by sampling only the most infor-mative and representative images to send for labeling. Ac-tive learning has been less explored for the task of instancesegmentation, than for other, less labeling intensive taskslike image classification. In this work, we propose a sim-ple, easy-to-implement, uncertainty based sampling strat-egy for instance segmentation, and an improvement whichadditionally incorporates diversity considerations.
