Camouflaged Object Detection Via Cross-Level Refinement and Interaction Network

The purpose of camouflaged object detection (COD) focuses on detecting objects that seamlessly blend into their surroundings. Camouflaged objects pose a substantial challenge in the realm of computer vision due to various factors, including occlusion, limited illumination, and diminutive dimensions. In this paper, we propose a cross-level refinement and interaction network (CRI-Net) to capture camouflaged objects. Specifically, we advance the concept of a semantic amplification module (SAM), which simulates human visual processes through multi-scale parallel convolution in a way of progressive aggregation with the aim of obtaining rich semantic information. Subsequently, we propose a cross-level refinement unit (CRU), which focuses on multivariate information at different levels in an attention-induced manner to facilitate the fusion refinement of features between levels and the exploration of feature similarity. Finally, we design a semantic-texture interaction module (SIM) to facilitate the interaction between high-level semantics and low-level textures while mining rich fine-grained spatial information to improve the integrity of camouflaged objects. By conducting comprehensive experiments on four benchmark camouflaged datasets, our CRI-Net demonstrates significantly superior performance compared to 20 cutting-edge competing methods.