EReCu：面向无监督伪装检测的伪标签进化融合与多线索学习精炼
EReCu: Pseudo-label Evolution Fusion and Refinement with Multi-Cue Learning for Unsupervised Camouflage Detection
针对无监督伪装目标检测中伪标签噪声和边界模糊问题，提出多线索原生感知与伪标签进化融合框架，其教师-学生伪标签精炼和频谱张量注意力融合策略可迁移到其他弱监督/无监督分割任务。
原文依据 Unsupervised Camouflaged Object Detection (UCOD) remains a challenging task due to the high intrinsic similarity between target objects and their surroundings
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论无监督伪装目标检测（UCOD）仍是一项具有挑战性的任务，原因在于目标物体与其周围环境之间存在高度的内在相似性，以及对噪声伪标签的依赖阻碍了细粒度纹理学习。现有的精炼策略虽旨在缓解标签噪声，却往往忽略内在感知线索，导致边界溢出和结构歧义。相反，无伪标签指导的学习会产生细节损失严重的粗糙特征。为解决这些问题，我们提出一个统一的UCOD框架，同时提升伪标签的可靠性和特征的保真度。我们的方法引入多线索原生感知模块，通过整合低层纹理线索与中层语义来提取内在视觉先验，实现掩码与原生对象信息之间的精确对齐。此外，伪标签进化融合通过教师-学生交互智能地精炼标签，并利用深度可分离卷积进行高效的语义去噪。它还结合频谱张量注意力融合，通过跨多层注意力图的紧凑频谱聚合来有效平衡语义与结构信息。最后，局部伪标签精炼通过利用注意力多样性来恢复细纹理并增强边界保真度，在局部细节优化中发挥关键作用。在多个UCOD数据集上的大量实验表明，我们的方法达到了最优性能，其特点是卓越的细节感知、稳健的边界对齐以及在复杂伪装场景下的强泛化能力。
英文原文摘要 1706 字符
Unsupervised Camouflaged Object Detection (UCOD) remains a challenging task due to the high intrinsic similarity between target objects and their surroundings, as well as the reliance on noisy pseudo-labels that hinder fine-grained texture learning. While existing refinement strategies aim to alleviate label noise, they often overlook intrinsic perceptual cues, leading to boundary overflow and structural ambiguity. In contrast, learning without pseudo-label guidance yields coarse features with significant detail loss. To address these issues, we propose a unified UCOD framework that enhances both the reliability of pseudo-labels and the fidelity of features. Our approach introduces the Multi-Cue Native Perception module, which extracts intrinsic visual priors by integrating low-level texture cues with mid-level semantics, enabling precise alignment between masks and native object information. Additionally, Pseudo-Label Evolution Fusion intelligently refines labels through teacher-student interaction and utilizes depthwise separable convolution for efficient semantic denoising. It also incorporates Spectral Tensor Attention Fusion to effectively balance semantic and structural information through compact spectral aggregation across multi-layer attention maps. Finally, Local Pseudo-Label Refinement plays a pivotal role in local detail optimization by leveraging attention diversity to restore fine textures and enhance boundary fidelity. Extensive experiments on multiple UCOD datasets demonstrate that our method achieves state-of-the-art performance, characterized by superior detail perception, robust boundary alignment, and strong generalization under complex camouflage scenarios.