SdalsNet: Self-Distilled Attention Localization and Shift Network for Unsupervised Camouflaged Object Detection

Abstract

Unsupervised camouflaged object detection (UCOD) poses significant challenges, primarily attributed to the absence of human labels. Existing UCOD methodologies, leveraging attention mechanisms, often struggle to achieve precise localization of camouflaged objects. To overcome this limitation, we introduce a groundbreaking fully unsupervised algorithm for attention-guided camouflaged object localization, shift, and inference, termed the self-distilled attention localization and shift network (SdalsNet). In this study, we formulate an attention localization methodology aimed at accurately identifying the central coordinate of the camouflaged object. Furthermore, we propose four distinct loss functions tailored to refine the precision of attentional positioning. These loss functions effectively constrain the distances between three types of class tokens, facilitating seamless attentional shifting across the input sample. Additionally, we design a sophisticated prediction inference technique to reconstruct the binary output of an attention map, thereby providing a comprehensive understanding of the detected camouflaged objects. Experimental results on four challenging COD benchmark datasets corroborate the effectiveness of our proposed approach, demonstrating notable superiority over state-of-the-art methods.