提升语义分割主动学习效果，重点捕捉边界像素信息。
Active Learning with Context Sampling and One-vs-Rest Entropy for Semantic Segmentation
- 用像素级不确定度聚合增强边界检测能力。
- 提出一比余熵函数，自动平衡类别标注偏好。
- 适配多类分割场景，尤其适合标注资源有限的领域。
多类语义分割仍是计算机视觉的核心挑战，但数据集构建耗时耗力，尤其在专业领域。主动学习（AL）通过有策略地选择待标注样本缓解此问题。然而，现有基于补丁的主动学习方法常忽略对分割精度至关重要的边界像素信息。本文提出OREAL，一种面向多类语义分割的新型补丁级主动学习方法。OREAL通过最大聚合像素级不确定性得分来增强边界检测能力。同时，引入一比余熵（one-vs-rest entropy），一种新颖的不确定性评分函数，在计算类别不确定性的同时实现标注过程中的隐式类别平衡。在多种数据集和模型架构上的全面实验验证了该方法的有效性。
原文摘要 · Abstract (English)
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.
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