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

EasyChair Preprint no. 7081

14 pagesDate: November 22, 2021


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 selected hyper-parameter(s) in the used 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 losses. 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 and experimental results validate the effectiveness of our proposals.

Keyphrases: Cluster Ensemble, Contrastive Learning, multi-granularity, Unsupervised Person ReID

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {He Sun and Mingkun Li and Chun-Guang Li},
  title = {Hybrid Contrastive Learning with Cluster Ensemble for Unsupervised Person Re-Identification},
  howpublished = {EasyChair Preprint no. 7081},

  year = {EasyChair, 2021}}
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