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.
翻译:无人监督的人重新身份识别(ReID)旨在将行人查询图像与没有监督标签的画廊图像相匹配。最受欢迎的处理无人监督的人ReID最常用的方法通常是进行群集算法,首先产生假标签,然后利用假标签来训练深神经网络。不过,假标签吵闹,对群集算法中的超参数敏感。在本文中,我们提议对无人监督的人重新身份进行混合竞争学习(HCL)方法,该方法基于试率和集群对比损失功能之间的混合。此外,我们介绍了多发群集基于聚合的混合兼容学习(MGCE-HCL)方法,采用多发群集战略,在伪阳性样品中的多发群集战略,在假阳性样品中确定优先加权的混合对比损失,以更好地调和伪正性样品中的噪音。我们对两个基准数据集市场1501和DukMCDM-RIID建议进行了广泛的实验。实验结果验证了我们两个基准数据集标定结果的有效性。