Person re-identification (Re-ID) has achieved great improvement with the development of deep learning. However, domain adaptation in unsupervised Re-ID has always been a challenging task. Most existing works based on clustering only cluster once, which may lead to pseudo labels of poor quality. In this letter, we propose a Pseudo Label based on Multiple Clustering (PLMC) approach, which makes full advantage of multiple clustering to obtain more robust pseudo labels. In particular, our PLMC framework consists of two stages, namely, global training stage, and local training stage. We adopt the training strategy that combines the information learned from global features, and local features by training two stages alternately. Extensive experiments are carried out on three standard benchmark datasets (e.g., Maket1501, DukeMTMC-ReID, CUHK03). The results demonstrate that our PLMC method is superior to the previous methods based on single clustering, and achieves state-of-the-art person Re-ID performance under the unsupervised cross-domain setting.

中文翻译：

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基于多重聚类的无监督跨域行人重识别伪标签

随着深度学习的发展，行人重识别（Re-ID）取得了很大的进步。然而，无监督 Re-ID 中的域适应一直是一项具有挑战性的任务。大多数基于聚类的现有作品仅聚类一次，这可能导致质量较差的伪标签。在这封信中，我们提出了一种基于多重聚类（PLMC）方法的伪标签，它充分利用了多重聚类来获得更强大的伪标签。特别是，我们的 PLMC 框架由两个阶段组成，即全局训练阶段和局部训练阶段。我们采用通过交替训练两个阶段来结合从全局特征和局部特征中学到的信息的训练策略。在三个标准基准数据集（例如，Maket1501、DukeMTMC-ReID、CUHK03）上进行了大量实验。