Heterogeneous Model Knowledge Distillation via Dual Alignment for Semantic Segmentation

Current knowledge distillation methods for semantic segmentation are primarily designed for knowledge transfer within homogeneous networks, and are less effective for heterogeneous networks. The feature information output by heterogeneous networks faces several challenges, including differences in feature scales and varying capabilities to represent local and global contextual information. To address these issues, we propose a novel Heterogeneous Model Knowledge Distillation (HMKD) framework using a dual alignment method to improve distillation performance between CNN-based and Transformer-based semantic segmentation models. Specifically, we introduce the Patch-based Self-attention Alignment Module (PSAM), which computes and aligns patch-level self-attention across distinct feature map spaces, enabling the transfer of local or global contextual information between heterogeneous models. Additionally, the Heterogeneous Scale Alignment Module (HSAM) is designed to ensure consistency across heterogeneous feature scales and enrich the semantic content. We have also conducted extensive experiments on two benchmark datasets (Cityscapes and CamVid) to validate the effectiveness and superiority of our approach compared with several recent state-of-the-art (SOTA) methods. Our code is deposited at https://github.com/xumingzhu989/HMKD-ICMR. © 2025 ACM.