For most unsupervised person re-identification (re-ID), people often adopt unsupervised domain adaptation (UDA) method. UDA often train on the labeled source dataset and evaluate on the target dataset, which often focuses on learning differences between the source dataset and the target dataset to improve the generalization of the model. Base on these, we explore how to make use of the similarity of samples to conduct a fully unsupervised method which just trains on the unlabeled target dataset. Concretely, we propose a hierarchical clustering-guided re-ID (HCR) method. We use hierarchical clustering to generate pseudo labels and use these pseudo labels as monitors to conduct the training. In order to exclude hard examples and promote the convergence of the model, We use PK sampling in each iteration, which randomly selects a fixed number of samples from each cluster for training. We evaluate our model on Market-1501, DukeMTMC-reID and MSMT17. Results show that HCR gets the state-of-the-arts and achieves 55.3% mAP on Market-1501 and 46.8% mAP on DukeMTMC-reID. Our code will be released soon.

本研究提出了一种基于层次聚类的伪标签指导的无监督人物重识别方法，该方法可以在未标记的目标数据集上进行训练，并通过使用层次聚类生成伪标签作为监控去训练，同时将PK抽样用于排除困难样例并促进算法的收敛，实验结果表明，HCR方法可以达到非常优秀的状态，使得在Market-1501和DukeMTMC-reID上的平均精度分别为55.3％和46.8％。

硬批次三元组损失的分层聚类在人员重新识别中的应用