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
camouflaged object detection models. Experimental results show that our
triple-task learning framework achieves new state-of-the-art, leading to a more
explainable camouflaged object detection network. Our code, data and results
are available at:
https://github.com/JingZhang617/COD-Rank-Localize-and-Segment