Camouflage is a key defence mechanism across species that is critical to
survival. Common strategies for camouflage include background matching,
imitating the color and pattern of the environment, and disruptive coloration,
disguising body outlines [35]. Camouflaged object detection (COD) aims to
segment camouflaged objects hiding in their surroundings. Existing COD models
are built upon binary ground truth to segment the camouflaged objects without
illustrating the level of camouflage. In this paper, we revisit this task and
argue that explicitly modeling the conspicuousness of camouflaged objects
against their particular backgrounds can not only lead to a better
understanding about camouflage and evolution of animals, but also provide
guidance to design more sophisticated camouflage techniques. Furthermore, we
observe that it is some specific parts of the camouflaged objects that make
them detectable by predators. With the above understanding about camouflaged
objects, we present the first ranking based COD network (Rank-Net) to
simultaneously localize, segment and rank camouflaged objects. The localization
model is proposed to find the discriminative regions that make the camouflaged
object obvious. The segmentation model segments the full scope of the
camouflaged objects. And, the ranking model infers the detectability of
different camouflaged objects. Moreover, we contribute a large COD testing set
to evaluate the generalization ability of COD models. Experimental results show
that our model achieves new state-of-the-art, leading to a more interpretable
COD network.

本研究通过建立 Rank-Net 模型，结合 camouflaged object detection 技术和 conspicuousness 的概念，来探讨动物进化中的 Camouflage 技术，并提出一个可以定位、分割和排名 Camouflaged 目标的方法。实验结果表明，该模型取得了新的最先进水平，使 Camouflaged 目标的检测更具可解释性。