Active Learning with Context Sampling and One-vs-Rest Entropy for Semantic Segmentation

Fei Wu, Pablo Márquez Neila, Hedyeh Rafii-Tari, Raphael Sznitman; Proceedings of the Winter Conference on Applications of Computer Vision (WACV), 2025, pp. 869-878

Multi-class semantic segmentation remains a cornerstone challenge in computer vision. Yet dataset creation remains excessively demanding in time and effort especially for specialized domains. Active Learning (AL) mitigates this challenge by selecting data points for annotation strategically. However existing patch-based AL methods often overlook boundary pixels' critical information essential for accurate segmentation. We present OREAL a novel patch-based AL method designed for multi-class semantic segmentation. OREAL enhances boundary detection by employing maximum aggregation of pixel-wise uncertainty scores. Additionally we introduce one-vs-rest entropy a novel uncertainty score function that computes class-wise uncertainties while achieving implicit class balancing during dataset creation. Comprehensive experiments across diverse datasets and model architectures validate our hypothesis.