The unsupervised person re-identification (re-ID) has become increasingly significant in the community because it is more scalable than the supervised method when dealing with the large-scale person re-ID. However, it is difficult to learn discriminative enough features from across-camera images without labelling information. To address this problem, we propose a joint patch and instance discrimination learning (JPIL) framework for the unsupervised person re-ID. The JPIL framework exploits a patch feature extraction model to generate patch-wise features for each input image. Then the patch discrimination learning (PDL) loss is designed to guide the model to mine the patch-wise discriminative information from unlabelled person image patches. On the other hand, we introduce the instance discrimination learning (IDL) loss to provide instance-wise supervision. The IDL loss aims to pull features of the same instance under different transformations closer and push features belonging to different instances away. Finally, we combine the PDL and IDL loss to apply the joint training. Extensive experiments on Market-1501 and DukeMTMC-reID datasets demonstrate the effectiveness of the proposed method for unsupervised person re-ID.

无监督人员重新识别（re-ID）在社区中变得越来越重要，因为在处理大规模人员re-ID时，无监督人员重新识别比可监督方法更具可扩展性。但是，很难在没有标签信息的情况下从跨摄像机图像中学习足够有区别的功能。为了解决这个问题，我们为无监督人员re-ID提出了联合补丁和实例歧视学习（JPIL）框架。JPIL框架利用补丁特征提取模型为每个输入图像生成逐补丁特征。然后设计补丁识别学习（PDL）损失，以指导模型从未标记的人像补丁中挖掘补丁方式的区分信息。另一方面，我们介绍了实例歧视学习（IDL）损失，以提供实例监督。IDL损失的目的是拉近同一实例在不同变换下的要素，并将属于不同实例的要素推开。最后，我们结合PDL和IDL损失进行联合训练。在Market-1501和DukeMTMC-reID数据集上进行的大量实验证明了该方法在无人监督下进行re-ID的有效性。