  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.
