SEED: Subject-to-Edge Exploration and Distillation for Unsupervised Camouflaged Object Detection

Unsupervised Camouflaged Object Detection (UCOD) aims to identify concealed objects without relying on pixel-level annotations. Mainstream methods rely on prior-based pseudo-labels that often misclassify background noise as foreground under high object–background similarity. Recent approaches suppress noise via iterative pseudo-label optimization or uncertaintyaware strategies. However, they face a trade-off between noise filtering and detail preservation: conservative filtering fails to remove noise that disrupts subject localization, whereas aggressive filtering sacrifices edge details for subject purification. To address these issues, we decompose UCOD into a subject localization with distillation phase and a detail exploration phase. Accordingly, we propose a Subject-to-Edge Exploration and Distillation (SEED) method based on the TeacherStudent framework, which comprises three modules: the Subject Teacher Initialization (STI) module, the Subject Consistency Distillation (SCD) module, and the Active Detail Exploration (ADE) module. For subject localization with distillation, STI establishes a purified camouflaged subject via dynamic pseudo-label, then SCD employs confidence-partitioned distillation to maintain subject consistency and mitigate confusion. For detail exploration, ADE leverages temperature rescaling to actively refine details for fine-grained prediction. Extensive experiments demonstrate that our method achieves state-of-the-art performance.