Abstract
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.
Abstract (translated)
多类语义分割仍然是计算机视觉中的一个核心挑战。然而，数据集的创建在时间和精力上仍然极其耗费，特别是在专业化领域中更是如此。主动学习（AL）通过战略性地选择注释的数据点来缓解这一挑战。但是，现有的基于补丁的AL方法往往忽视了边界像素的关键信息，而这些信息对于准确分割至关重要。我们提出了OREAL，这是一种专为多类语义分割设计的新颖的基于补丁的AL方法。OREAL 通过运用像素级不确定度分数的最大聚合来增强边界检测。此外，我们引入了一种新的不确定性评分函数——一对其余熵（one-vs-rest entropy），该函数在创建数据集的过程中计算类别级别的不确定度，同时实现隐式的类别平衡。广泛的实验跨越了不同的数据集和模型架构，验证了我们的假设。
URL
https://arxiv.org/abs/2412.06470
