通过多尺度解耦特征，提升知识蒸馏效率与效果。

Knowledge distillation enhances the performance of compact student networks by transferring knowledge from more powerful teacher networks without introducing additional parameters. In the feature space, local regions within an individual global feature encode distinct yet interdependent semantic information. Previous feature-based distillation methods mainly emphasize global feature alignment while neglecting the decoupling of local regions within an individual global feature, which often results in semantic confusion and suboptimal performance. Moreover, conventional contrastive representation distillation suffers from low efficiency due to its reliance on a large memory buffer to store feature samples. To address these limitations, this work proposes MSDCRD, a model-agnostic distillation framework that systematically decouples global features into multi-scale local features and leverages the resulting semantically rich feature samples with tailored sample-wise and feature-wise contrastive losses. This design enables efficient distillation using only a single batch, eliminating the dependence on external memory. Extensive experiments demonstrate that MSDCRD achieves superior performance not only in homogeneous teacher-student settings but also in heterogeneous architectures where feature discrepancies are more pronounced, highlighting its strong generalization capability.