电子科技大学学报2026，Vol.28Issue(4)：518-529,12.DOI:10.12178/1001-0548.2025233
文图跨模态知识蒸馏的行人检索算法
Research on person retrieval algorithm based on text-to-image cross-modal knowledge distillation
摘要
Abstract
Current text-to-image cross-modal person retrieval algorithms overly rely on vision-language pre-trained large models to improve accuracy.This leads to issues such as large model parameter sizes and high computational requirements,making it difficult to meet the practical application requirements such as edge deployment.In light of this,this paper focuses on the lightweight aspect of vision-language pre-trained large models.It introduces a multi-modal joint distillation and complementary supervision strategy.Based on Huawei Ascend platform,the paper proposes a text-image cross-modal pedestrian retrieval algorithm based on a three-stage progressive knowledge distillation approach.Unlike the traditional two-stage approach that only performs independent distillation for each modality,this method first conducts distillation within the image and text modalities separately,and finally performs collaborative distillation for cross-modal semantic association in the shared latent space.Experimental results show that the student model has only 14.77%of the parameters of the teacher model.Compared with existing lightweight methods,it achieves effective improvements in mAP on the CUHK-PEDES,ICFG-PEDES,and RSTPReid datasets.It also realizes real-time inference performance with an inference time of approximately 23 ms for processing a single text-image pair on edge devices.This study confirms the effectiveness of the three-stage progressive knowledge distillation method and provides an effective pathway for the domestic implementation in scenarios such as personnel screening and intelligent security.关键词
文图跨模态/行人检索/知识蒸馏/边缘设备Key words
text-to-image cross-modal/person retrieval/knowledge distillation/edge devices分类
信息技术与安全科学引用本文复制引用
林云博,丁金,陆庚有,周雪,邹见效..文图跨模态知识蒸馏的行人检索算法[J].电子科技大学学报,2026,28(4):518-529,12.基金项目
司法部年度法治建设与法学理论研究部级科研项目(25SFB4012) （25SFB4012）
教育部青年项目(23YJC820056). （23YJC820056）
