论文标题
与无监督的人重新识别的集体合奏的混合对比度学习
Hybrid Contrastive Learning with Cluster Ensemble for Unsupervised Person Re-identification
论文作者
论文摘要
无监督的人重新识别（REID）旨在将行人的查询图像与没有监督标签的画廊中的图像相匹配。解决无监督的人REID的最流行方法通常是执行聚类算法以产生伪标签，然后利用伪标签来训练深层的神经网络。但是，伪标签在聚类算法中对高参数（S）嘈杂，并且敏感。在本文中，我们为无监督的人Reid提出了一种混合对比学习方法（HCL）方法，该方法基于实例级别和群集级对比损失函数之间的混合。此外，我们提出了一种基于多晶聚类集合合奏的混合对比度学习（MGCE-HCL）方法，该方法采用了多个跨性聚类集合策略来挖掘伪正面样本对之间的优先信息，并定义了优先级 - 权威的混合对比损失，以更好地容忍在Pseudo正面中的NOISS。我们在两个基准数据集Market 1501和Dukemtmc-Reid上进行了广泛的实验。实验结果证明了我们的建议的有效性。
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.
