Hybrid Contrastive Learning with Cluster Ensemble for Unsupervised Person Re-identification

He Sun; Mingkun Li; Chun-Guang Li

doi:10.1007/978-3-031-02444-3_40

ScienceGate Book Chapters

BOOK-CHAPTER

Hybrid Contrastive Learning with Cluster Ensemble for Unsupervised Person Re-identification

He Sun Mingkun Li Chun-Guang Li

Year: 2022 Lecture notes in computer science Pages: 532-546 Publisher: Springer Science+Business Media

DOI: 10.1007/978-3-031-02444-3_40

Get Full-Text PDF Get Analytical Report

Keywords:

Computer science Cluster analysis Artificial intelligence Ensemble learning Benchmark (surveying) Exploit Granularity Unsupervised learning Identification (biology) Sample (material) Machine learning Pattern recognition (psychology) Set (abstract data type) Data mining

Metrics

Cited By

2.81

FWCI (Field Weighted Citation Impact)

Refs

0.92

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

Hybrid Contrastive Learning with Cluster Ensemble for Unsupervised Person Re-identification

Metrics

Citation History

Topics

Related Documents

Hybrid Contrastive Learning for Unsupervised Person Re-Identification

Unsupervised person re-identification by dynamic hybrid contrastive learning

Cluster-Guided Asymmetric Contrastive Learning for Unsupervised Person Re-Identification

Hybrid Contrastive Learning with Attention Mechanism for Unsupervised Person Re-identification

Attention-based hybrid contrastive learning for unsupervised person re-identification