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Training high-quality instance segmentation models requires an abundance oflabeled images with instance masks and classifications, which is oftenexpensive to procure. Active learning addresses this challenge by striving foroptimum performance with minimal labeling cost by selecting the mostinformative and representative images for labeling. Despite its potential,active learning has been less explored in instance segmentation compared toother tasks like image classification, which require less labeling. In thisstudy, we propose a post-hoc active learning algorithm that integratesuncertainty-based sampling with diversity-based sampling. Our proposedalgorithm is not only simple and easy to implement, but it also deliverssuperior performance on various datasets. Its practical application isdemonstrated on a real-world overhead imagery dataset, where it increases thelabeling efficiency fivefold.

Two-Step Active Learning for Instance Segmentation with Uncertainty and Diversity Sampling