Camouflaged instance segmentation (CIS) aims to segment instances that are seamlessly embedded in their surroundings. Existing CIS methods often focus on utilizing global information but neglect local information, resulting in incomplete feature representation and reduced accuracy. To address this, we propose a global-to-local network (GLNet) for CIS, leveraging both global and local information for enhanced feature representation and segmentation. Specifically, GLNet consists of two main components: global capture and local refinement. In global capture, we introduce a novel dual-branch convo...
