In this work, we address the problem of unsupervised domain adaptation for
person re-ID where annotations are available for the source domain but not for
target. Previous methods typically follow a two-stage optimization pipeline,
where the network is first pre-trained on source and then fine-tuned on target
with pseudo labels created by feature clustering. Such methods sustain two main
limitations. (1) The label noise may hinder the learning of discriminative
features for recognizing target classes. (2) The domain gap may hinder
knowledge transferring from source to target. We propose three types of
technical schemes to alleviate these issues. First, we propose a cluster-wise
contrastive learning algorithm (CCL) by iterative optimization of feature
learning and cluster refinery to learn noise-tolerant representations in the
unsupervised manner. Second, we adopt a progressive domain adaptation (PDA)
strategy to gradually mitigate the domain gap between source and target data.
Third, we propose Fourier augmentation (FA) for further maximizing the class
separability of re-ID models by imposing extra constraints in the Fourier
space. We observe that these proposed schemes are capable of facilitating the
learning of discriminative feature representations. Experiments demonstrate
that our method consistently achieves notable improvements over the
state-of-the-art unsupervised re-ID methods on multiple benchmarks, e.g.,
surpassing MMT largely by 8.1\%, 9.9\%, 11.4\% and 11.1\% mAP on the
Market-to-Duke, Duke-to-Market, Market-to-MSMT and Duke-to-MSMT tasks,
respectively.

本文提出了三种技术方案，即集群对比学习算法（CCL）、渐进领域适应策略（PDA）和傅里叶增强（FA），以解决无监督域自适应的问题。实验证明，我们的方法在多个基准测试上都实现了显著的改进。