Boundary and Relation Distillation for Semantic Segmentation
Dong Zhang Pingcheng Dong Xinting Hu Long Chen Kwang-Ting Cheng
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.