Person re-identification (Re-ID) is a critical technique in the video surveillance system, which has achieved significant success in the supervised setting. However, it is difficult to directly apply the supervised model to arbitrary unseen domains due to the domain gap between the available source domains and unseen target domains. In this paper, we propose a novel label distribution learning (LDL) method to address generalizable multi-source person Re-ID task ( i.e. , there are multiple available source domains, and the testing domain is unseen during training), which aims to explore the relation of different classes and mitigate the domain-shift across different domains so as to improve the discrimination of the model and learn the domain-invariant feature, simultaneously. Specifically, during the training process, we produce the label distribution via the online manner to mine the relation information of different classes, thus it is beneficial for extracting the discriminative feature. Besides, for the label distribution of each class, we further revise it to give more and equal attention to the other domains that the class does not belong to, which can effectively reduce the domain gap across different domains and obtain the domain-invariant feature. Furthermore, we also give the theoretical analysis to demonstrate that the proposed method can effectively deal with the domain-shift issue. Extensive experiments on multiple benchmark datasets validate the effectiveness of the proposed method and show that the proposed method can outperform the state-of-the-art methods. Besides, further analysis also reveals the superiority of the proposed method.

中文翻译：

---

用于泛化多源行人重新识别的标签分布学习


人员重新识别（Re-ID）是视频监控系统中的一项关键技术，在监督环境中取得了显着的成功。然而，由于可用源域和不可见目标域之间的域差距，很难将监督模型直接应用于任意不可见域。在本文中，我们提出了一种新的标签分布学习（LDL）方法来解决可泛化的多源行人重识别任务（即有多个可用的源域，并且在训练过程中测试域是看不见的），其目的是探索不同类别之间的关系并减轻不同领域之间的领域转移，从而提高模型的辨别力并同时学习领域不变特征。具体来说，在训练过程中，我们通过在线方式产生标签分布，以挖掘不同类别的关系信息，从而有利于提取判别性特征。此外，对于每个类的标签分布，我们进一步修改它以给予该类不属于的其他域更多且平等的关注，这可以有效地减少不同域之间的域差距并获得域不变特征。此外，我们还给出了理论分析，证明所提出的方法可以有效地处理域转移问题。对多个基准数据集的大量实验验证了所提出方法的有效性，并表明所提出的方法可以优于最先进的方法。此外，进一步的分析也揭示了该方法的优越性。