  Camouflaged object segmentation (COS) aims to identify objects that are
"perfectly" assimilate into their surroundings, which has a wide range of
valuable applications. The key challenge of COS is that there exist high
intrinsic similarities between the candidate objects and noise background. In
this paper, we strive to embrace challenges towards effective and efficient
COS. To this end, we develop a bio-inspired framework, termed Positioning and
Focus Network (PFNet), which mimics the process of predation in nature.
Specifically, our PFNet contains two key modules, i.e., the positioning module
(PM) and the focus module (FM). The PM is designed to mimic the detection
process in predation for positioning the potential target objects from a global
perspective and the FM is then used to perform the identification process in
predation for progressively refining the coarse prediction via focusing on the
ambiguous regions. Notably, in the FM, we develop a novel distraction mining
strategy for distraction discovery and removal, to benefit the performance of
estimation. Extensive experiments demonstrate that our PFNet runs in real-time
(72 FPS) and significantly outperforms 18 cutting-edge models on three
challenging datasets under four standard metrics.
