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.
Abstract (translated)
伪装目标检测（COD）旨在识别与背景高度融合的目标。近期研究表明，偏振线索的光学特性对提升伪装目标检测效果具有重要作用。然而，现有基于偏振的方法大多依赖复杂的视觉编码器和融合机制，导致模型复杂度与计算开销增加，且未能充分探索偏振如何显式指导分层RGB表征学习。针对这些局限，我们提出CPGNet——一种RGB-偏振非对称框架，通过引入条件偏振引导机制显式调控RGB特征学习以优化伪装目标检测。具体而言，我们设计了轻量级偏振交互模块，统一建模互补线索并生成可靠的偏振引导信号。与传统特征融合策略不同，该条件引导机制利用偏振先验动态调制RGB特征，使网络聚焦伪装目标与背景间的细微差异。此外，我们提出偏振边缘引导的频率细化策略，在偏振约束下增强高频分量以有效打破伪装模式。最后，我们开发了迭代反馈解码器，通过粗到细的特征校准逐步优化伪装预测。在跨任务偏振数据集上的大量实验及非偏振数据集评估表明，CPGNet持续优于最先进方法。
URL
https://arxiv.org/abs/2603.30008
