中山大学学报（自然科学版）（中英文）2025，Vol.64Issue(5)：43-49,7.DOI:10.13471/j.cnki.acta.snus.ZR20250070
知识蒸馏与掩码重构的域泛化行人重识别
A distillation and masked approach for domain generalizable person re-identification
摘要
Abstract
The challenge of domain generalization stems from two inherent limitations in current person re-identification benchmarks:1)significant inter-dataset domain gaps,and 2)insufficient intra-dataset diversity.While existing multi-domain joint training approaches attempt to address these issues,they often fail to fully exploit latent discriminative identity cues across datasets.To address the aforementioned limitations,our framework enhances network generalization capabilities through a dual-branch strategy:knowledge distillation employed from a large-scale pre-trained model along with mask image feature mining performed on existing multi-domain training data.Extensive experiments on popular domain generalization person ReID benchmarks demonstrate that our method can achieve superior performance.Notably,our approach achieves a 16.2%Rank-1 accuracy gain over the baseline and a 3.6%improvement over existing state-of-the-art methods under the leave-one-out protocol using Market-1501.关键词
行人重识别/域泛化/知识蒸馏/掩码图像Key words
person re-identification/domain generalizable/knowledge distillation/masked image分类
信息技术与安全科学引用本文复制引用
郑昊天,胡海峰..知识蒸馏与掩码重构的域泛化行人重识别[J].中山大学学报（自然科学版）（中英文）,2025,64(5):43-49,7.基金项目
国家自然科学基金(62076262) （62076262）
