Person re-identification (re-ID) has recently been tremendously boosted due to the advancement of deep convolutional neural networks. Unfortunately, the majority of deep re-ID methods focus on supervised, single-domain re-ID task, while less attention is paid on unsupervised domain adaptation. Therefore, these methods always fail to generalize well to real-world scenarios, which have attracted much attention from academia. To address this challenge, we propose a joint unsupervised domain adaptive re-ID method, named HCTL, which is aided by Hierarchical Clustering and Transfer Learning. Specifically, our method performs camera invariance learning using iStarGAN by transferring style of reliable images, which is mined by hierarchical clustering, to the style of other cameras in target domain. During training stage, HCTL integrates TriHard loss on top of ResNet-50 to reduce intra-class variance among dataset and enforce connectedness simultaneously between source domain and target domain. Comprehensive experiments based on Market-1501, DukeMTMC-reID and CUHK03 are conducted, results indicate that our method robustly achieves state-of-the-art performances with only a few reliable samples in target domain and outperform any existing approaches by a large margin.

中文翻译：

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通过层次聚类和域转移对无人监督的人员进行重新识别

由于深度卷积神经网络的发展，人的重新识别（re-ID）最近得到了极大的提升。不幸的是，大多数深度re-ID方法都集中在有监督的单域re-ID任务上，而对无监督域自适应的关注则较少。因此，这些方法总是不能很好地推广到现实世界中，这引起了学术界的广泛关注。为了解决这一挑战，我们提出了一种名为HCTL的联合无监督域自适应re-ID方法，该方法借助层次聚类和转移学习进行辅助。具体来说，我们的方法通过将可靠图像的样式（通过分层聚类挖掘）转移到目标域中其他照相机的样式，从而使用iStarGAN执行照相机不变性学习。在训练阶段 HCTL在ResNet-50之上集成了TriHard损失，以减少数据集之间的类内差异，并同时在源域和目标域之间强制执行连接。进行了基于Market-1501，DukeMTMC-reID和CUHK03的综合实验，结果表明，我们的方法仅在目标域中提供了几个可靠的样本，即可稳健地实现最新性能，并且在很大程度上优于任何现有方法。