辩论增强的伪标签与频率感知渐进去偏对于弱监督伪装物体检测与涂鸦注释
Debate-Enhanced Pseudo Labeling and Frequency-Aware Progressive Debiasing for Weakly-Supervised Camouflaged Object Detection with Scribble Annotations
AI总结：
本文提出${D}^{3}$ETOR框架，通过辩论增强伪标签和频率感知渐进去偏方法，提升弱监督伪装物体检测的性能，实现与全监督方法的性能接近。
摘要：
弱监督伪装物体检测（WSCOD）旨在利用稀疏监督如涂鸦注释来定位和分割被周围场景视觉隐藏的物体。尽管近期有进展，现有WSCOD方法仍远落后于全监督方法，主要受限于两个问题：（1）通用分割模型（如SAM）生成的伪掩码经规则过滤后往往不可靠，因为这些模型缺乏执行COD中有效伪标签所需的任务特定语义理解；（2）涂鸦中固有注释偏见被忽视，阻碍模型捕捉伪装物体的全局结构。为克服这些挑战，我们提出${D}^{3}$ETOR，一个两阶段WSCOD框架，包含辩论增强伪标签和频率感知渐进去偏。第一阶段引入自适应熵驱动点采样方法和多代理辩论机制，增强SAM在COD中的能力，提升伪掩码的可解释性和精度。第二阶段设计FADeNet，逐步融合多级频率感知特征，平衡全局语义理解和局部细节建模，同时动态调整区域监督强度以减轻涂鸦偏见。通过联合利用伪掩码和涂鸦语义的监督信号，${D}^{3}$ETOR显著缩小弱监督与全监督COD之间的差距，在多个基准上实现最先进的性能。
英文摘要：
Weakly-Supervised Camouflaged Object Detection (WSCOD) aims to locate and segment objects that are visually concealed within their surrounding scenes, relying solely on sparse supervision such as scribble annotations. Despite recent progress, existing WSCOD methods still lag far behind fully supervised ones due to two major limitations: (1) the pseudo masks generated by general-purpose segmentation models (e.g., SAM) and filtered via rules are often unreliable, as these models lack the task-specific semantic understanding required for effective pseudo labeling in COD; and (2) the neglect of inherent annotation bias in scribbles, which hinders the model from capturing the global structure of camouflaged objects. To overcome these challenges, we propose ${D}^{3}$ETOR, a two-stage WSCOD framework consisting of Debate-Enhanced Pseudo Labeling and Frequency-Aware Progressive Debiasing. In the first stage, we introduce an adaptive entropy-driven point sampling method and a multi-agent debate mechanism to enhance the capability of SAM for COD, improving the interpretability and precision of pseudo masks. In the second stage, we design FADeNet, which progressively fuses multi-level frequency-aware features to balance global semantic understanding with local detail modeling, while dynamically reweighting supervision strength across regions to alleviate scribble bias. By jointly exploiting the supervision signals from both the pseudo masks and scribble semantics, ${D}^{3}$ETOR significantly narrows the gap between weakly and fully supervised COD, achieving state-of-the-art performance on multiple benchmarks.
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