  Knowledge distillation is widely adopted in semantic segmentation to reduce
the computation cost.The previous knowledge distillation methods for semantic
segmentation focus on pixel-wise feature alignment and intra-class feature
variation distillation, neglecting to transfer the knowledge of the inter-class
distance in the feature space, which is important for semantic segmentation. To
address this issue, we propose an Inter-class Distance Distillation (IDD)
method to transfer the inter-class distance in the feature space from the
teacher network to the student network. Furthermore, semantic segmentation is a
position-dependent task,thus we exploit a position information distillation
module to help the student network encode more position information. Extensive
experiments on three popular datasets: Cityscapes, Pascal VOC and ADE20K show
that our method is helpful to improve the accuracy of semantic segmentation
models and achieves the state-of-the-art performance. E.g. it boosts the
benchmark model("PSPNet+ResNet18") by 7.50% in accuracy on the Cityscapes
dataset.
