Unsupervised cross domain (UCD) person re-identification (re-ID) aims to apply a model trained on a labeled source domain to an unlabeled target domain. It faces huge challenges as the identities have no overlap between these two domains. At present, most UCD person re-ID methods perform “supervised learning” by assigning pseudo labels to the target domain, which leads to poor re-ID performance due to the pseudo label noise. To address this problem, a multi-loss optimization learning (MLOL) model is proposed for UCD person re-ID. In addition to using the information of clustering pseudo labels from the perspective of supervised learning, two losses are designed from the view of similarity exploration and adversarial learning to optimize the model. Specifically, in order to alleviate the erroneous guidance brought by the clustering error to the model, a ranking-average-based triplet loss learning and a neighbor-consistency-based loss learning are developed. Combining these losses to optimize the model results in a deep exploration of the intra-domain relation within the target domain. The proposed model is evaluated on three popular person re-ID datasets, Market-1501, DukeMTMC-reID, and MSMT17. Experimental results show that our model outperforms the state-of-the-art UCD re-ID methods with a clear advantage.

中文翻译：

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多损失优化学习的无监督跨域人员重新识别

无监督跨域（UCD）人员重新识别（re-ID）旨在将在标记源域上训练的模型应用于未标记目标域。由于身份在这两个域之间没有重叠，因此面临巨大挑战。当前，大多数UCD人员re-ID方法通过将伪标签分配给目标域来执行“监督学习”，这由于伪标签噪声而导致较差的re-ID性能。为了解决这个问题，针对UCD人员re-ID提出了多损失优化学习（MLOL）模型。除了从监督学习的角度使用聚类伪标签的信息之外，还从相似性探索和对抗学习的角度设计了两个损失，以优化模型。具体来说，为了减轻聚类错误给模型带来的错误指导，开发了基于排序平均的三元组损失学习和基于邻居一致性的损失学习。组合这些损失以优化模型可导致对目标域内域内关系的深入探索。在三个受欢迎的人re-ID数据集Market-1501，DukeMTMC-reID和MSMT17上对提出的模型进行了评估。实验结果表明，我们的模型具有明显的优势，其性能优于最新的UCD re-ID方法。在三个受欢迎的人re-ID数据集Market-1501，DukeMTMC-reID和MSMT17上对提出的模型进行了评估。实验结果表明，我们的模型具有明显的优势，其性能优于最新的UCD re-ID方法。在三个受欢迎的人re-ID数据集Market-1501，DukeMTMC-reID和MSMT17上对提出的模型进行了评估。实验结果表明，我们的模型具有明显的优势，其性能优于最新的UCD re-ID方法。