We study the problem of distilling knowledge from a large deep teacher network to a much smaller student network for the task of road marking segmentation. In this work, we explore a novel knowledge distillation (KD) approach that can transfer 'knowledge' on scene structure more effectively from a teacher to a student model. Our method is known as Inter-Region Affinity KD (IntRA-KD). It decomposes a given road scene image into different regions and represents each region as a node in a graph. An inter-region affinity graph is then formed by establishing pairwise relationships between nodes based on their similarity in feature distribution. To learn structural knowledge from the teacher network, the student is required to match the graph generated by the teacher. The proposed method shows promising results on three large-scale road marking segmentation benchmarks, i.e., ApolloScape, CULane and LLAMAS, by taking various lightweight models as students and ResNet-101 as the teacher. IntRA-KD consistently brings higher performance gains on all lightweight models, compared to previous distillation methods. Our code is available at https://github.com/cardwing/Codes-for-IntRA-KD.

本文介绍了一种新的知识蒸馏方法——Inter-Region Affinity KD，该方法通过构建道路场景图并匹配教师模型的图来实现从大型教师网络向较小的学生网络进行知识迁移，从而在多种轻量级模型上取得了更好的性能表现，并在 ApolloScape、CULane 和 LLAMAS 三个大型路标分割数据集上进行了验证。

路标分割的区域间亲和度蒸馏