Unsupervised domain adaptation (UDA) for person re-identication (ReID) remains a challenging task, as the trained ReID system often fails to adapting to a new dataset. Due to the lack of supervision of real labels, the performance of the UDA models suffers from inefficient feature learning and inevitable pseudo label noise. In this work, we tackle the problems by designing an effective dual-path mutual-learning framework which can capture effective information for better feature learning and mitigate the impact of label noise. Firstly, to reduce the impact of occlusion and viewpoints, we introduce the self-attention mechanism in a two-stage strategy making the models focus on the key areas of identifying people. Secondly, considering that UDA is an open-set task, we leverage density-based spatial clustering of applications with noise (DBSCAN) to avoid manually setting the number of classes of the target domain. Thirdly, for realizing joint and flexible optimization under the supervision of soft pseudo labels and hard pseudo labels, a joint and flexible loss (JFL) is proposed to train the network. Experiments on three large-scale datasets show that our model outperforms the state-of-the-art UDA methods in both mAP and top-1 evaluation protocols by large margins. Especially on task of Duke-to-Market, our method outperforms the state-of-the-art by 6.9% mAP.

用于人员重新识别（ReID）的无监督域自适应（UDA）仍然是一项艰巨的任务，因为经过训练的ReID系统通常无法适应新的数据集。由于缺乏对实际标签的监督，因此UDA模型的性能会遭受无效的特征学习和不可避免的伪标签噪声的困扰。在这项工作中，我们通过设计有效的双路径双向学习框架来解决这些问题，该框架可以捕获有效信息以更好地进行特征学习并减轻标签噪声的影响。首先，为了减少遮挡和观点的影响，我们在两阶段策略中引入了自我注意机制，使模型着重于识别人员的关键领域。其次，考虑到UDA是一项公开任务，我们利用基于密度的应用程序空间聚类与噪声（DBSCAN）来避免手动设置目标域的类数。第三，为了在软伪标签和硬伪标签的监督下实现联合和柔性优化，提出了联合和柔性损失（JFL）来训练网络。在三个大型数据集上进行的实验表明，在mAP和top-1评估协议中，我们的模型均优于最新的UDA方法。尤其是在“市场公爵”的任务上，我们的方法的mAP优于最新技术，为6.9％。在三个大型数据集上进行的实验表明，在mAP和top-1评估协议中，我们的模型均优于最新的UDA方法。尤其是在“市场公爵”的任务上，我们的方法的mAP优于最新技术，为6.9％。在三个大型数据集上进行的实验表明，在mAP和top-1评估协议中，我们的模型均优于最新的UDA方法。尤其是在“市场公爵”的任务上，我们的方法的mAP优于最新技术，为6.9％。