Conditional Polarization Guidance for Camouflaged Object Detection

Abstract

Camouflaged object detection (COD) aims to identify targets that are highly blended with their backgrounds. Recent works have shown that the optical characteristics of polarization cues play a significant role in improving camouflaged object detection. However, most existing polarization-based approaches depend on complex visual encoders and fusion mechanisms, leading to increased model complexity and computational overhead, while failing to fully explore how polarization can explicitly guide hierarchical RGB representation learning. To address these limitations, we propose CPGNet, an asymmetric RGB-polarization framework that introduces a conditional polarization guidance mechanism to explicitly regulate RGB feature learning for camouflaged object detection. Specifically, we design a lightweight polarization interaction module that jointly models these complementary cues and generates reliable polarization guidance in a unified manner. Unlike conventional feature fusion strategies, the proposed conditional guidance mechanism dynamically modulates RGB features using polarization priors, enabling the network to focus on subtle discrepancies between camouflaged objects and their backgrounds. Furthermore, we introduce a polarization edge-guided frequency refinement strategy that enhances high-frequency components under polarization constraints, effectively breaking camouflage patterns. Finally, we develop an iterative feedback decoder to perform coarse-to-fine feature calibration and progressively refine camouflage prediction. Extensive experiments on polarization datasets across multiple tasks, along with evaluations on non-polarization datasets, demonstrate that CPGNet consistently outperforms state-of-the-art methods.

Figures (10)

• Figure 1: Comparison of RGB–polarization integration paradigms. (a) Attention fusion enhances RGB features using polarization-aware attention. (b) Mid-level fusion directly merges RGB and polarization features. (c) Our method treats polarization as conditional guidance for RGB representation learning, rather than explicit feature fusion.
• Figure 2: (a) Overview of the proposed CPGNet. (b) Polarization Integration Module (PIM). (c) Polarization Guidance Enhancement (PGE). (d) Edge-guided Frequency Module (EFM). (e) Feature Refinement (FR). CPGNet progressively injects polarization guidance into hierarchical RGB features and refines predictions in a coarse-to-fine manner.
• Figure 3: Qualitative comparison with SOTA methods on PCOD-1200. CPGNet produces more complete predictions and cleaner boundaries in challenging camouflage scenes.
• Figure 4: Qualitative comparison of our CPGNet against SOTA approaches on the RGBP-Glass dataset.
• Figure 5: Qualitative comparison of our CPGNet against SOTA approaches on the RGB-Depth datasets.