Two-Step Active Learning for Instance Segmentation with Uncertainty and
  Diversity Sampling
Ke Yu Stephen Albro Giulia DeSalvo Suraj Kothawade Abdullah Rashwan Sasan Tavakkol Kayhan Batmanghelich Xiaoqi Yin
Training high-quality instance segmentation models requires an abundance of
labeled images with instance masks and classifications, which is often
expensive to procure. Active learning addresses this challenge by striving for
optimum performance with minimal labeling cost by selecting the most
informative and representative images for labeling. Despite its potential,
active learning has been less explored in instance segmentation compared to
other tasks like image classification, which require less labeling. In this
study, we propose a post-hoc active learning algorithm that integrates
uncertainty-based sampling with diversity-based sampling. Our proposed
algorithm is not only simple and easy to implement, but it also delivers
superior performance on various datasets. Its practical application is
demonstrated on a real-world overhead imagery dataset, where it increases the
labeling efficiency fivefold.