提出首个考虑上下文的伪装目标分割评估方法，更贴近人眼判断。
Context-measure: Contextualizing Metric for Camouflage
- 基于概率像素相关性框架，引入上下文亲和力增强真值
- 在四个元度量下均超越现有主流指标
- 适合研究伪装物体检测与评价的学者使用
伪装依赖于上下文信息，但当前伪装目标分割的评估指标忽略了上下文线索。我们指出两大缺陷：一是维度缺陷——预测图包含像素标签与概率分数，而真实标签仅为一维二值；二是范围缺陷——难以捕捉全范围像素依赖关系。为此，我们提出Context-measure，一种基于概率像素相关性的上下文感知评估范式。它通过在真值中加入像素级上下文亲和力，并构建感知循环，使评估结果更符合人类感知。大量实验表明，使用四个元度量验证，所提方法全面优于现有广泛使用的指标。据我们所知，这是首个专为伪装场景设计的评估指标。代码已开源：https://github.com/pursuitxi/Context-measure。
原文摘要 · Abstract (English)
Camouflage relies heavily on context, but current metrics used in camouflaged object segmentation ignore contextual cues. We identify two major drawbacks of these metrics: first, the Dimension Flaw - a predicted foreground map usually contains both pixel labels and probability scores, whereas ground truth provides only one-dimensional binary labels; second, the Range Flaw - these metrics struggle to capture full-range pixel dependencies. Thus, we propose Context-measure, a novel context-aware evaluation paradigm built on a probabilistic pixel correlation framework. It augments the ground truth with pixel-level contextual affinity and builds a perception cycle, achieving greater consistency with human perception. Extensive experiments using four meta-measures show that our Context-measure comprehensively outperforms all widely adopted metrics for camouflaged object segmentation. To our knowledge, this is the first metric designed for camouflaged scenarios. Code is available at https://github.com/pursuitxi/Context-measure.
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