Current Knowledge Distillation (KD) methods for semantic segmentation often
guide the student to mimic the teacher's structured information generated from
individual data samples. However, they ignore the global semantic relations
among pixels across various images that are valuable for KD. This paper
proposes a novel Cross-Image Relational KD (CIRKD), which focuses on
transferring structured pixel-to-pixel and pixel-to-region relations among the
whole images. The motivation is that a good teacher network could construct a
well-structured feature space in terms of global pixel dependencies. CIRKD
makes the student mimic better structured semantic relations from the teacher,
thus improving the segmentation performance. Experimental results over
Cityscapes, CamVid and Pascal VOC datasets demonstrate the effectiveness of our
proposed approach against state-of-the-art distillation methods. The code is
available at this https URL

本文提出了一种跨图像关系知识蒸馏方法，通过结构化像素之间和区域之间的关系来提高学生模型的分割性能。在 Cityscapes，CamVid 和 Pascal VOC 数据集上的实验结果表明，该方法比现有的蒸馏方法效果更好。