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.

翻译：多类别语义分割始终是计算机视觉领域的核心挑战。然而，数据集构建过程仍需要耗费大量时间和精力，尤其在专业领域更为突出。主动学习通过策略性地选择待标注数据点来缓解这一难题。然而，现有的基于图像块的主动学习方法常常忽略对分割精度至关重要的边界像素信息。本文提出OREAL——一种专为多类别语义分割设计的新型基于图像块的主动学习方法。OREAL通过采用像素级不确定性得分的最大化聚合来增强边界检测能力。此外，我们引入了一对多熵这一新型不确定性评分函数，该函数在计算类别级不确定性的同时，能够在数据集构建过程中实现隐式的类别平衡。通过在多样化数据集和模型架构上的综合实验，我们的假设得到了充分验证。