Person re-identification (Re-ID) models usually present a limited performance when they are trained on one dataset and tested on another dataset due to the inter-dataset bias (e.g. completely different identities and backgrounds) and the intra-dataset difference (e.g. camera and pose changes). In other words, the absence of identity labels (who the person is) and pairwise labels (whether a pair of images belongs to the same person or not) leads to failures in unsupervised person Re-ID problem. We argue that synchronous consideration of these two aspects can improve the performance of unsupervised person Re-ID model. In this work, we introduce a Classification and Latent Commonality (CLC) method based on transfer learning for the unsupervised person Re-ID problem. Our method has three characteristics: (1) proposing an imitate model to generate an imitated target domain with estimated identity labels and create a pseudo target domain to compensate the pairwise labels across camera views; (2) formulating a dual classification loss on both the source domain and imitated target domain to learn a discriminative representation and diminish the inter-domain bias; (3) investigating latent commonality and reducing the intra-domain difference by constraining triplet loss on the source domain, imitated target domain and pairwise label target domain (composed of pseudo target domain and target domain). Extensive experiments are conducted on three widely employed benchmarks, including Market-1501, DukeMTMC-reID and MSMT17, and experimental results demonstrate that the proposed method can achieve a competitive performance against other state-of-the-art unsupervised Re-ID approaches.

由于数据集间的偏差（例如，完全不同的身份和背景）和数据集内的差异（例如，人重新识别（Re-ID）模型在一个数据集上进行训练并在另一数据集上进行测试时，通常表现出有限的性能）相机和姿势更改）。换句话说，缺少身份标签（人）和成对标签（一对图像是否属于同一人）会导致无人监督的Re-ID问题失败。我们认为，这两个方面的同步考虑可以提高无监督人员Re-ID模型的性能。在这项工作中，我们介绍了一种基于转移学习的无监督人Re-ID问题分类和潜在共性（CLC）方法。我们的方法具有三个特点：（1）提出一种模仿模型，以生成带有估计身份标签的模仿目标域，并创建一个伪目标域，以补偿摄像机视图之间的成对标签；（2）在源域和被模仿的目标域上制定双重分类损失，以学习判别式表示并减少域间偏差；（3）通过限制源域，模拟目标域和成对标记目标域（由伪目标域和目标域组成）上的三重态丢失，研究潜在的共性并减少域内差异。在三个广泛使用的基准上进行了广泛的实验，包括Market-1501，DukeMTMC-reID和MSMT17，