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.

本文提出一种基于知识蒸馏的语义分割方法，通过引入Inter-class Distance Distillation (IDD)模块来转移特征空间中的跨类距离，同时利用位置信息蒸馏模块来提高学生网络的位置编码能力，实验结果表明，该方法可以大幅度提高语义分割模型的准确性，达到state-of-the-art的性能水平。

利用距离亮点聚合技术提升语义分割