Existing knowledge distillation works for semantic segmentation mainly focus on transferring high-level contextual knowledge from teacher to student. However, low-level texture knowledge is also of vital importance for characterizing the local structural pattern and global statistical property, such as boundary, smoothness, regularity and color contrast, which may not be well addressed by high-level deep features. In this paper, we are intended to take full advantage of both structural and statistical texture knowledge and propose a novel Structural and Statistical Texture Knowledge Distillation (SSTKD) framework for semantic segmentation. Specifically, for structural texture knowledge, we introduce a Contourlet Decomposition Module (CDM) that decomposes low-level features with iterative Laplacian pyramid and directional filter bank to mine the structural texture knowledge. For statistical knowledge, we propose a Denoised Texture Intensity Equalization Module (DTIEM) to adaptively extract and enhance statistical texture knowledge through heuristics iterative quantization and denoised operation. Finally, each knowledge learning is supervised by an individual loss function, forcing the student network to mimic the teacher better from a broader perspective. Experiments show that the proposed method achieves state-of-the-art performance on Cityscapes, Pascal VOC 2012 and ADE20K datasets.

本文提出了一种新颖的结构和统计纹理知识蒸馏框架（SSTKD），通过引入轮廓线分解模块（CDM）和去噪纹理亮度均衡化模块（DTIEM）来结合结构和统计纹理知识，其中，CDM通过迭代Laplacian金字塔和方向性滤波器组对低级特征进行分解，而DTIEM则通过启发式迭代量化和去噪操作来自适应地提取和增强统计纹理知识，最后，每个知识学习都由一个单独的损失函数进行监督，从更广泛的视角促使学生网络更好地模仿老师，并且实验表明，所提出的方法在Cityscapes，Pascal VOC 2012和ADE20K数据集上实现了最先进的性能。

结构和统计纹理知识蒸馏用于语义分割