Camouflaged Instance Segmentation From Global Capture to Local Refinement

Abstract

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 convolutional feedforward network (Dual-FFN), which aims to more effectively capture camouflaged instances in complex scenes. In local refinement, we design a U-shape feature fusion module (UFFM) and an edge-guide fusion module (EFM). These modules facilitate the fusion of multi-scale features by cascading. As a result, the network gains an enhanced ability to discern the intricate details of camouflaged instances. Experimental results demonstrate that our GLNet outperforms existing methods, with a 49.3% average precision (AP) on the COD10K-Test.