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. • A cross-level refinement and interaction network (CRI-Net) focuses on camouflaged object. • We earnestly design three key components for better study of COD missions to accurately predict camouflaged objects. The Semantic Amplification Module (SAM) is used to extract multi-scale semantic information, the Cross-level Refinement Unit (CRU) is proposed to focus on multivariate information at different levels, and the Semantic-texture Interaction Module (SIM) is designed to facilitate the interaction between high-level semantics and low-level textures. • Experimental results on four challenging COD datasets suggest that our (CRI-Net) is extremely superior in four widely used evaluation indicators of 20 state-of-the-art models. In addition, our approach is more generalized in many other visual tasks with similar COD. [ABSTRACT FROM AUTHOR]