The main focus of unsupervised person re-identification is the clustering of unlabeled samples in the target domain. However, most existing studies neglected to mine the deep semantic information of the target domain and did not consider a better combination of the source domain and the target domain. In this letter, we not only consider the changes of the target domain within its own domain but also mine the deep semantic information of the images by designing a measurement axis component. Then, the deep semantic information mined by the axis is used as the judgment basis of hard negative samples. Moreover, a new loss function is designed in this work to improve the migration ability of the network. Experimental results on two person re-identification domains show that our technology accuracy outperforms the state of the art by a large margin.

中文翻译：

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基于测量轴的无人监督再识别

无监督人员重新识别的主要重点是目标域中未标记样本的聚类。但是，大多数现有研究都忽略了挖掘目标域的深层语义信息，并且没有考虑源域和目标域的更好组合。在这封信中，我们不仅考虑目标域在其自身域内的变化，而且还通过设计测量轴组件来挖掘图像的深层语义信息。然后，将通过轴提取的深度语义信息用作硬否定样本的判断基础。而且，在这项工作中设计了一种新的丢失功能，以提高网络的迁移能力。在两个人重新识别域上的实验结果表明，我们的技术准确性大大超过了现有技术。