Recently, it has been revealed that small semantic segmentation (SS) models exhibit a tendency to make errors in maintaining boundary region completeness and preserving target region connectivity, despite their effective segmentation of the main object regions. To address these errors, we propose a targeted boundary and relation distillation (BRD) strategy using knowledge distillation from large teacher models to small student models. Specifically, the boundary distillation extracts explicit object boundaries from the hierarchical feature maps of the backbone network, subsequently enhancing the student model's mask quality in boundary regions. Concurrently, the relation distillation transfers implicit relations from the teacher model to the student model using pixel-level self-relation as a bridge, ensuring that the student's mask has strong target region connectivity. The proposed BRD is designed concretely for SS and is characterized by simplicity and efficiency. Through experimental evaluations on multiple SS datasets, including Pascal VOC 2012, Cityscapes, ADE20K, and COCO-Stuff 10K, we demonstrated that BRD significantly surpasses the current methods without increasing the inference costs, generating crisp region boundaries and smooth connecting regions that are challenging for small models.

翻译：近期研究表明，小型语义分割模型虽能有效分割主体目标区域，但在保持边界区域完整性和维持目标区域连通性方面存在易错倾向。针对这些误差，我们提出了一种基于边界与关系蒸馏（BRD）的目标性策略，通过大模型教师网络向小模型学生网络的知识迁移实现优化。具体而言，边界蒸馏从骨干网络的分层特征图中提取显式目标边界，进而提升学生模型在边界区域的掩码质量；关系蒸馏则利用像素级自相关性作为桥梁，将教师模型的隐式关系传递至学生模型，确保学生模型生成的掩码具备强目标区域连通性。所提出的BRD方法专为语义分割设计，具有简洁高效的特点。通过在Pascal VOC 2012、Cityscapes、ADE20K及COCO-Stuff 10K等多个语义分割数据集上的实验评估，我们证明BRD在不增加推理成本的前提下显著超越现有方法，能够生成小型模型难以实现的清晰区域边界与平滑连接区域。