CamoFocus: Enhancing Camouflage Object Detection with Split-Feature Focal Modulation and Context Refinement

Camouflage Object Detection (COD) involves the challenge of isolating a target object from a visually similar background, presenting a formidable challenge for learning algorithms. Drawing inspiration from state-of-the-art (SOTA) Focal Modulation Networks, our objective is to proficiently modulate the foreground and background components, thereby capturing the distinct features of each. We introduce a Feature Split and Modulation (FSM) module to attain this goal. This module efficiently separates the object from the background by utilizing foreground and background modulators guided by a supervisory mask. For enhanced feature refinement, we propose a Context Refinement Module (CRM), which considers features acquired from FSM across various spatial scales, leading to comprehensive enrichment and highly accurate prediction maps. Through extensive experimentation, we showcase the superiority of CamoFocus over recent SOTA COD methods. Our evaluations encompass diverse benchmark datasets, including CAMO, COD10K, CHAMELEON, and NC4K. The findings underscore the potential and significance of the proposed CamoFocus model and establish its efficacy in addressing the critical challenges of camouflage object detection.