首页|Multiple Information Prompt Learning for Cloth-Changing Person Re-Identification

Multiple Information Prompt Learning for Cloth-Changing Person Re-Identification

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  • NETL
  • NSTL
  • IEEE
Cloth-changing person re-identification is a subject closer to the real world, which focuses on solving the problem of person re-identification after pedestrians change clothes. The primary challenge in this field is to overcome the complex interplay between intra-class and inter-class variations and to identify features that remain unaffected by changes in appearance. Sufficient data collection for model training would significantly aid in addressing this problem. However, it is challenging to gather diverse datasets in practice. Current methods focus on implicitly learning identity information from the original image or introducing additional auxiliary models, which are largely limited by the quality of the image and the performance of the additional model. To address these issues, inspired by prompt learning, we propose a novel multiple information prompt learning (MIPL) scheme for cloth-changing person ReID, which learns identity robust features through the common prompt guidance of multiple messages. Specifically, the clothing information stripping (CIS) module is designed to decouple the clothing information from the original RGB image features to counteract the influence of clothing appearance. The bio-guided attention (BGA) module is proposed to increase the learning intensity of the model for key information. A dual-length hybrid patch (DHP) module is employed to make the features have diverse coverage to minimize the impact of feature bias. Extensive experiments demonstrate that the proposed method outperforms all state-of-the-art methods on the LTCC, Celeb-reID, Celeb-reID-light, and CSCC datasets, achieving rank-1 scores of 74.8%, 73.3%, 66.0%, and 88.1%, respectively. When compared to AIM (CVPR23), ACID (TIP23), and SCNet (MM23), MIPL achieves rank-1 improvements of 11.3%, 13.8%, and 7.9%, respectively, on the PRCC dataset.

ClothingPedestriansVisualizationTrainingFeature extractionComputer visionCamerasBiological system modelingTransformersText recognition

Shengxun Wei、Zan Gao、Chunjie Ma、Yibo Zhao、Weili Guan、Shengyong Chen

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Key Laboratory of Computer Vision and System, Ministry of Education, Tianjin University of Technology, Tianjin, China

Key Laboratory of Computer Vision and System, Ministry of Education, Tianjin University of Technology, Tianjin, China|Shandong Artificial Intelligence Institute, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China

Shandong Artificial Intelligence Institute, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China

School of Electronics and Information Engineering, Harbin Institute of Technology, Shenzhen, China

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2025

10.1109/TIP.2025.3531217

IEEE transactions on image processing

IEEE transactions on image processing

ISSN：
年,卷(期)：2025.34(1)
  • 65