Fuzzy Boundary-Guided Network for Camouflaged Object Detection

Camouflaged object detection (COD) is a challenging task that identifies camouflaged objects from highly similar backgrounds. Existing methods typically treat the whole object equally while neglecting the indistinguishable regions that require more attention than other regions. In this paper, we propose a Fuzzy Boundary-Guided Network (FBG-Net) for camouflaged object detection, which mimics the human behavior that pays more attention to these low-confidence regions when observing objects. Specifically, we devise two main building blocks: (1) Mixed Semantics Aggregation Module (MSAM) to integrate boundary and texture features cumulatively in the high-to-low scales, and (2) Fuzzy Boundary-Guided Module (FBGM) to locate and enhance the low-confidence regions under the guidance of fuzzy boundary. Extensive experiments demonstrate the effectiveness of FBG-Net with superior performance to existing state-of-the-art methods. Code is available at https://github.com/YAOSL98/FBG-Net.