Recently unsupervised person re-identification (re-ID) has drawn much
attention due to its open-world scenario settings where limited annotated data
is available. Existing supervised methods often fail to generalize well on
unseen domains, while the unsupervised methods, mostly lack multi-granularity
information and are prone to suffer from confirmation bias. In this paper, we
aim at finding better feature representations on the unseen target domain from
two aspects, 1) performing unsupervised domain adaptation on the labeled source
domain and 2) mining potential similarities on the unlabeled target domain.
Besides, a collaborative pseudo re-labeling strategy is proposed to alleviate
the influence of confirmation bias. Firstly, a generative adversarial network
is utilized to transfer images from the source domain to the target domain.
Moreover, person identity preserving and identity mapping losses are introduced
to improve the quality of generated images. Secondly, we propose a novel
collaborative multiple feature clustering framework (CMFC) to learn the
internal data structure of target domain, including global feature and partial
feature branches. The global feature branch (GB) employs unsupervised
clustering on the global feature of person images while the Partial feature
branch (PB) mines similarities within different body regions. Finally,
extensive experiments on two benchmark datasets show the competitive
performance of our method under unsupervised person re-ID settings.

本文提出了一种基于生成对抗网络和半监督多特征聚类的联合伪标记策略，通过无监督域适应和在非标记目标域中挖掘潜在相似性，提高目标域的特征表示能力，同时通过半监督多特征聚类学习目标域的内部数据结构，取得了良好的无监督个体重新识别表现。