Camouflaged object detection (COD) aims to localize targets that exhibit minimal perceptual differences from backgrounds through physical attributes. Existing methods, constrained by the static train-then-freeze paradigm, suffer from domain rigidity and annotation dependency, limiting their adaptability to scene variations and unseen camouflage patterns. To overcome these, we propose the hierarchical consistency learning (HCL) framework, which integrates test-time adaptation for dynamic representation recalibration. Specifically, we design the hierarchical representation reconstruction (HRR) to alleviate feature entanglement by synergizing spatial reconstruction with dual-stream frequency-domain decomposition, enhancing robustness against appearance homogenization. The pixel and spectrum inference provide structural and contextual priors. We further introduce task affinity guidance (TAG) to propagate knowledge across branches via channel-wise affinity, aligning local discriminative cues and mitigating semantic drift. To ensure semantic invariance, we formulate the prototype consistency calibration (PCC), which aggregates region features into compact prototypes and establishes prototype-feature similarity. This imposes implicit and hierarchical constraints that bridge task and representation gaps. Extensive experiments across four camouflaged and four underwater object benchmarks, under three degradation settings, demonstrate that our method consistently outperforms state-of-the-art approaches, highlighting its robustness and generalization under distribution shifts.

翻译：伪装目标检测（Camouflaged Object Detection, COD）旨在通过物理属性定位与背景感知差异极小的目标。现有方法受限于静态的"先训练后冻结"范式，存在领域刚性与标注依赖性问题，限制了其对场景变化和未见伪装模式的适应性。为克服这些局限，我们提出层次一致性学习（Hierarchical Consistency Learning, HCL）框架，该框架集成测试时自适应以实现动态表征重校准。具体而言，我们设计了层次表征重建（Hierarchical Representation Reconstruction, HRR）方法，通过协同空间重建与双流频域分解来缓解特征纠缠，增强对表观均匀化的鲁棒性。像素推理与频谱推理分别提供结构先验与上下文先验。我们进一步引入任务亲和引导（Task Affinity Guidance, TAG），通过通道级亲和性在分支间传播知识，对齐局部判别线索并减轻语义漂移。为确保语义不变性，我们设计了原型一致性校准（Prototype Consistency Calibration, PCC），将区域特征聚合为紧凑原型并建立原型-特征相似度，从而施加隐式层次化约束以弥合任务鸿沟与表征鸿沟。在四个伪装目标基准与四个水下目标基准上，针对三种退化设置进行的广泛实验表明，我们的方法始终优于现有最优方法，突显了其在分布偏移下的鲁棒性与泛化能力。