Learning Compact Representations With an Information Bottleneck for Camouflaged Object Detection

Frequency domain-based methods have demonstrated promising performance in Camouflaged Object Detection (COD) tasks because of their enhanced power for distinguishing between objects and the background in the frequency domain. However, these methods often overlook the interference caused by task-irrelevant cues such as background textures. These extraneous factors are learned alongside task-relevant features by the employed network, increasing the number of false positives. Therefore, we propose a camouflaged object detection method based on the Information Bottleneck (IB) theory. The aim is to obtain a robust representation that retains the essential features needed for prediction while minimizing the redundant information derived from both the RGB and frequency domains. Specifically, we propose a Feature Selection Information Bottleneck Module (FSIBM). By explicit supervision, this module minimizes the mutual information between the fused feature from two domains and the predictive features, thereby weakening task-irrelated information. Simultaneously, the FSIBM maximizes the mutual information between the predictive features and the ground truth (i.e., emphasizing task-related elements). Additionally, we introduce a Cross-Domain Awareness Interaction Module (CDAIM), which establishes self-reinforcement for the object attributes within each domain and facilitates cross-domain complementarity. This enables the capture of sufficient discriminative features from both domains. To verify the generalization ability of the proposed method, we applied it to three benchmark datasets, on which our method outperformed the corresponding state-of-the-art methods.