Unsupervised domain adaptive (UDA) person re-identification (re-ID) is a challenging task due to the missing of labels for the target domain data. To handle this problem, some recent works adopt clustering algorithms to off-line generate pseudo labels, which can then be used as the supervision signal for on-line feature learning in the target domain. However, the off-line generated labels often contain lots of noise that significantly hinders the discriminability of the on-line learned features, and thus limits the final UDA re-ID performance. To this end, we propose a novel approach, called Dual-Refinement, that jointly refines pseudo labels at the off-line clustering phase and features at the on-line training phase, to alternatively boost the label purity and feature discriminability in the target domain for more reliable re-ID. Specifically, at the off-line phase, a new hierarchical clustering scheme is proposed, which selects representative prototypes for every coarse cluster. Thus, labels can be effectively refined by using the inherent hierarchical information of person images. Besides, at the on-line phase, we propose an instant memory spread-out (IM-spread-out) regularization, that takes advantage of the proposed instant memory bank to store sample features of the entire dataset and enable spread-out feature learning over the entire training data instantly. Our Dual-Refinement method reduces the influence of noisy labels and refines the learned features within the alternative training process. Experiments demonstrate that our method outperforms the state-of-the-art methods by a large margin.

中文翻译：

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双细化：无监督域自适应行人重识别的联合标签和特征细化


由于目标域数据标签缺失，无监督域自适应 (UDA) 行人重新识别 (re-ID) 是一项具有挑战性的任务。为了解决这个问题，最近的一些工作采用聚类算法来离线生成伪标签，然后将其用作目标域中在线特征学习的监督信号。然而，离线生成的标签通常包含大量噪声，严重阻碍了在线学习特征的可辨别性，从而限制了最终的 UDA re-ID 性能。为此，我们提出了一种称为 Dual-Refinement 的新颖方法，该方法联合细化离线聚类阶段的伪标签和在线训练阶段的特征，以选择性地提高目标域中的标签纯度和特征可辨别性以获得更可靠的重新识别。具体来说，在离线阶段，提出了一种新的层次聚类方案，为每个粗聚类选择代表性原型。因此，可以利用人物图像固有的层次信息有效地细化标签。此外，在在线阶段，我们提出了一种即时内存扩展（IM-spread-out）正则化，它利用所提出的即时内存库来存储整个数据集的样本特征并实现扩展特征学习立即遍历整个训练数据。我们的双重细化方法减少了噪声标签的影响，并细化了替代训练过程中学到的特征。实验表明，我们的方法大大优于最先进的方法。