Multi-class center dynamic contrastive learning for unsupervised domain adaptation person re-identification

Qing Tian; Xiaoxin Du

doi:10.1016/j.compeleceng.2024.109155

ScienceGate Book Chapters

JOURNAL ARTICLE

Multi-class center dynamic contrastive learning for unsupervised domain adaptation person re-identification

Qing Tian Xiaoxin Du

Year: 2024 Journal: Computers & Electrical Engineering Vol: 116 Pages: 109155-109155 Publisher: Elsevier BV

DOI: 10.1016/j.compeleceng.2024.109155

Get Full-Text PDF Get Analytical Report

Keywords:

Computer science Leverage (statistics) Artificial intelligence Cluster analysis Discriminative model Domain adaptation Domain (mathematical analysis) Class (philosophy) Pattern recognition (psychology) Identification (biology) Machine learning Classifier (UML) Mathematics

Metrics

Cited By

4.24

FWCI (Field Weighted Citation Impact)

Refs

0.89

Citation Normalized Percentile

Is in top 1%

Is in top 10%

Citation History

Topics

Video Surveillance and Tracking Methods

Physical Sciences → Computer Science → Computer Vision and Pattern Recognition

Gait Recognition and Analysis

Physical Sciences → Engineering → Biomedical Engineering

Human Pose and Action Recognition

Physical Sciences → Computer Science → Computer Vision and Pattern Recognition

Multi-class center dynamic contrastive learning for unsupervised domain adaptation person re-identification

Metrics

Citation History

Topics

Related Documents

Cluster-based Dual-branch Contrastive Learning for unsupervised domain adaptation person re-identification

Contrastive learning enhanced pseudo-labeling for unsupervised domain adaptation in person re-identification

Unsupervised person re-identification by dynamic hybrid contrastive learning

Unsupervised Multi-Source Domain Adaptation for Person Re-Identification

Correction: Contrastive learning enhanced pseudo-labeling for unsupervised domain adaptation in person re-identification