Abstract
Low-level texture feature/knowledge is also of vital importance for characterizing the local structural pattern and global statistical properties, such as boundary, smoothness, regularity, and color contrast, which may not be well addressed by high-level deep features. In this paper, we aim to re-emphasize the low-level texture information in deep networks for semantic segmentation and related knowledge distillation tasks. To this end, we 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, Contourlet Decomposition Module (CDM) is introduced to decompose the low-level features with iterative Laplacian pyramid and directional filter bank to mine the structural texture knowledge, and Texture Intensity Equalization Module (TIEM) is designed to extract and enhance the statistical texture knowledge with the corresponding Quantization Congruence Loss (QDL). Moreover, we propose the Co-occurrence TIEM (C-TIEM) and generic segmentation frameworks, namely STLNet++ and U-SSNet, to enable existing segmentation networks to harvest the structural and statistical texture information more effectively. Extensive experimental results on three segmentation tasks demonstrate the effectiveness of the proposed methods and their state-of-the-art performance on seven popular benchmark datasets, respectively.
Abstract (translated)
低级纹理特征/知识对于描述局部结构模式和全局统计特性（如边界、平滑度、规则性和颜色对比）至关重要，而这些特性可能无法通过高级深度特征很好地处理。在本文中，我们旨在重新强调在语义分割及相关知识蒸馏任务中的低级纹理信息的重要性。为此，我们充分利用了结构化和统计化的纹理知识，并提出了一种新颖的结构性与统计性纹理知识蒸馏（SSTKD）框架用于语义分割。 具体来说，引入了轮廓分解模块(CDM)，通过迭代拉普拉斯金字塔和方向滤波器组来分解低级特征，以挖掘结构化纹理知识；设计了纹理强度均衡模块(TIEM)并通过相应的量化一致性损失(QDL)提取并增强统计性纹理知识。此外，我们还提出了共现TIEM (C-TIEM) 和通用分割框架（即STLNet++和U-SSNet），使现有的分割网络能够更有效地获取结构化与统计性的纹理信息。 在三个分割任务上进行的大量实验结果验证了所提出方法的有效性及其在七个流行基准数据集上的先进性能。
URL
https://arxiv.org/abs/2503.08043
