Paper Info
Title
Hybrid Contrastive Learning with Cluster Ensemble for Unsupervised Person Re-identification
Abstract
Unsupervised person re-identification (ReID) aims to match a query image of a pedestrian to the images in gallery set without supervision labels. The most popular approaches to tackle unsupervised person ReID are usually performing a clustering algorithm to yield pseudo labels at first and then exploit the pseudo labels to train a deep neural network. However, the pseudo labels are noisy and sensitive to the hyper-parameter(s) in clustering algorithm. In this paper, we propose a Hybrid Contrastive Learning (HCL) approach for unsupervised person ReID, which is based on a hybrid between instance-level and cluster-level contrastive loss functions. Moreover, we present a Multi-Granularity Clustering Ensemble based Hybrid Contrastive Learning (MGCE-HCL) approach, which adopts a multi-granularity clustering ensemble strategy to mine priority information among the pseudo positive sample pairs and defines a priority-weighted hybrid contrastive loss for better tolerating the noises in the pseudo positive samples. We conduct extensive experiments on two benchmark datasets Market-1501 and DukeMTMC-reID. Experimental results validate the effectiveness of our proposals.
Year
DOI
Venue
2021
10.1007/978-3-031-02444-3_40
Pattern Recognition
Keywords
DocType
ISSN
Unsupervised person ReID, Contrastive learning, Cluster ensemble, Multi-granularity
Conference
0302-9743
Citations 
PageRank 
References 
0
0.34
0
Authors
3
Name
Order
Citations
PageRank
He Sun100.68
Mingkun Li201.69
Chun-Guang Li331017.35