Inter-Region Affinity Distillation for Road Marking Segmentation
This work addresses efficient road marking segmentation for autonomous driving by improving knowledge distillation, though it is incremental as it builds on existing distillation methods.
The paper tackles road marking segmentation by proposing Inter-Region Affinity Knowledge Distillation (IntRA-KD) to transfer structural knowledge from a large teacher network to smaller student models, achieving consistent performance gains on benchmarks like ApolloScape, CULane, and LLAMAS.
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.