拆解分割评估指标，让评价标准更透明可解释
Beyond Pixel Overlap: A Framework for Decomposing Segmentation Evaluation Metrics
- 将评估指标分解为五个可解释阶段，暴露隐藏假设
- 揭示新旧指标如何针对性改进早期方法缺陷
- 适合需要定制化评估的分割任务研究者使用
分割评估指标是二值目标分割的核心，决定了进展的度量、比较与解读方式。本文中，'目标'指任务定义的待分割正区域，可能是显著的、伪装的、透明的、玻璃状、镜面状、阴影状、病灶状，或由特定应用语义定义。我们不再将指标视为孤立公式，而是将其视为模块化设计选择的组合。提出一个五阶段框架：预测表示、目标提取、目标匹配、分数计算、指标报告。用该框架分析代表性指标，揭示新指标如何解决旧协议的局限性。各阶段选择使指标假设显式可见。进一步探讨框架开启的设计空间及其对任务感知评估协议的意义。参考代码见 https://github.com/lartpang/PySODMetrics。
原文摘要 · Abstract (English)
Evaluation metrics are central to binary target segmentation because they determine how progress is measured, compared, and interpreted. In this paper, target denotes the task-defined positive region to be segmented rather than a generic foreground object. It may be salient, camouflaged, transparent, glass-like, mirror-like, shadow-like, lesion-like, or defined by other application-specific semantics. We treat existing metrics as compositions of modular design choices rather than isolated formulas. The proposed framework decomposes each metric into five stages covering prediction representation, target extraction, target matching, score computation, and metric reporting. We use this framework to analyze representative metrics and show how newer metrics address specific limits in earlier protocols. The stage choices keep each metric's assumptions visible. We then discuss the design space opened by the framework and its implications for task-aware evaluation protocols. Reference code is available at https://github.com/lartpang/PySODMetrics.
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