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

• URL: http://arxiv.org/abs/2201.11995v1
• Date: Fri, 28 Jan 2022 09:15:20 GMT
• Title: Hybrid Contrastive Learning with Cluster Ensemble for Unsupervised Person Re-identification
• Authors: He Sun, Mingkun Li, Chun-Guang Li
• Abstract summary: We propose a Hybrid Contrastive Learning (HCL) approach for unsupervised person ReID. We also present a Multi-Granularity Clustering Ensemble based Hybrid Contrastive Learning (MGCE-HCL) approach.
• Score: 8.345677436382193
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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