Structural and Statistical Texture Knowledge Distillation and Learning for Segmentation

• URL: http://arxiv.org/abs/2503.08043v2
• Date: Sun, 16 Mar 2025 11:26:26 GMT
• Title: Structural and Statistical Texture Knowledge Distillation and Learning for Segmentation
• Authors: Deyi Ji, Feng Zhao, Hongtao Lu, Feng Wu, Jieping Ye,
• Abstract summary: We re-emphasize the low-level texture information in deep networks for semantic segmentation and related knowledge distillation tasks.<n>We propose a novel Structural and Statistical Texture Knowledge Distillation (SSTKD) framework for semantic segmentation.<n>Specifically, Contourlet Decomposition Module (CDM) is introduced to decompose the low-level features.<n> Texture Intensity Equalization Module (TIEM) is designed to extract and enhance the statistical texture knowledge.
• Score: 70.15341084443236
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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