Preys in the wild evolve to be camouflaged to avoid being recognized by predators. In this way, camouflage acts as a key defence mechanism across species that is critical to survival. To detect and segment the whole scope of a camouflaged object, camouflaged object detection (COD) is introduced as a binary segmentation task, with the binary ground truth camouflage map indicating the exact regions of the camouflaged objects. In this paper, we revisit this task and argue that the binary segmentation setting fails to fully understand the concept of camouflage. We find that explicitly modeling the conspicuousness of camouflaged objects against their particular backgrounds can not only lead to a better understanding about camouflage, but also provide guidance to designing more sophisticated camouflage techniques. Furthermore, we observe that it is some specific parts of camouflaged objects that make them detectable by predators. With the above understanding about camouflaged objects, we present the first triple-task learning framework to simultaneously localize, segment, and rank camouflaged objects, indicating the conspicuousness level of camouflage. As no corresponding datasets exist for either the localization model or the ranking model, we generate localization maps with an eye tracker, which are then processed according to the instance level labels to generate our ranking-based training and testing dataset. We also contribute the largest COD testing set to comprehensively analyse performance of the COD models. Experimental results show that our triple-task learning framework achieves new state-of-the-art, leading to a more explainable COD network. Our code, data, and results are available at: \url{https://github.com/JingZhang617/COD-Rank-Localize-and-Segment}.

翻译：野外的野生原始变异会被伪装, 以避免被掠食者认出来。 这样, 迷彩会作为整个物种中对于生存至关重要的关键防御机制。 为了检测和分割迷彩对象的整个范围, 迷彩对象探测( COD) 被引入为二进制分解任务, 由二进制的地面真相迷彩图显示伪装对象的确切区域 。 在本文中, 我们重新审视这项任务, 并争论二进制分解设置不能完全理解迷彩的理念 。 我们发现, 明确模拟伪装对象的显眼性能不仅能导致更好地了解迷彩色, 而且还能为设计更精密的伪装对象提供指南 。 此外, 我们观察到, 迷彩的物体探测( COD) 的某些特定部分是迷彩色对象。 我们的三进化数据学习框架, 我们的CODA 正在进行最新的 COD 测试。