Camouflaged Object Segmentation with Distraction Mining
This addresses the problem of segmenting objects that blend into their backgrounds for applications in computer vision, representing a strong specific gain rather than a foundational advance.
The paper tackles camouflaged object segmentation by proposing a bio-inspired Positioning and Focus Network (PFNet) with a distraction mining strategy, achieving real-time performance at 72 FPS and outperforming 18 state-of-the-art models on three datasets under four metrics.
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.