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

Multi-class semantic segmentation remains a corner-stone challenge in computer vision. Yet, dataset creation remains excessively demanding in time and effort, especially for specialized domains. Active Learning (AL) mit-igates 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 di-verse datasets and model architectures validate our hypothesis.