Domain adaptation in person re-identification (re-ID) has always been a
challenging task. In this work, we explore how to harness the natural similar
characteristics existing in the samples from the target domain for learning to
conduct person re-ID in an unsupervised manner. Concretely, we propose a
Self-similarity Grouping (SSG) approach, which exploits the potential
similarity (from global body to local parts) of unlabeled samples to
automatically build multiple clusters from different views. These independent
clusters are then assigned with labels, which serve as the pseudo identities to
supervise the training process. We repeatedly and alternatively conduct such a
grouping and training process until the model is stable. Despite the apparent
simplify, our SSG outperforms the state-of-the-arts by more than 4.6% (DukeMTMC
to Market1501) and 4.4% (Market1501 to DukeMTMC) in mAP, respectively. Upon our
SSG, we further introduce a clustering-guided semisupervised approach named SSG
++ to conduct the one-shot domain adaption in an open set setting (i.e. the
number of independent identities from the target domain is unknown). Without
spending much effort on labeling, our SSG ++ can further promote the mAP upon
SSG by 10.7% and 6.9%, respectively. Our Code is available at:
this https URL .

本文提出了一种自相似分组方法（SSG），用于无监督地进行个人重新识别，有效地利用目标域中未标记样本的特征，通过不断地建立多个簇并为其分配标签的方式进行模型训练并取得比现有方法更好的结果，另外，也提出了一个基于聚类和半监督学习的方法来解决开放环境下的一次性域自适应问题。