When Specialists Meet Generalists: Segmenter-Coordinated Asymmetric Learning for Label-Deficient Concealed Object Segmentation

Existing label-deficient concealed object segmentation (LDCOS) methods either rely on consistency constraints within a meanteacher framework or employ the Segment Anything Model (SAM) as a fixed pseudo-label generator. Interestingly, task-specific segmenters and foundation models exhibit complementary failure modes: segmenters lack generalization under scarce labels, while SAM suffers from domain gaps in concealed scenes. A natural solution is to employ co-training to mutually promote both models. However, we observe that standard homogeneous co-training cannot exploit this complementarity because architecturally identical networks tend to share similar error patterns, especially when targets are heavily concealed. To address this, we present SCALER (Segmenter-Coordinated Asymmetric LEaRning), a framework that jointly optimizes a mean-teacher segmenter and a learnable SAM through two alternating phases with model-specific optimization strategies. In Phase I, the segmenter is updated under fixed SAM supervision using entropy-based image-level and uncertainty-based pixellevel weighting to suppress unreliable pseudo-label regions. In Phase II, SAM is updated via an augmentation invariance loss and a noise resistance loss, which exploit SAM’s inherent perturbation robustness rather than treating it identically to the segmenter. This asymmetric design is the key distinction from prior co-training and one-way distillation methods: each model’s learning strategy is tailored to its own inductive bias, enabling mutual enhancement. Experiments across eight LDCOS tasks demonstrate consistent gains, and SCALER improves both the lightweight segmenter and the foundation model, serving as a general plug-and-play paradigm for label-scarce conditions. The code is available at https://github.com/ChunmingHe/SCALER.