We investigate unsupervised person re-identification (Re-ID) with clothes change, a new challenging problem with more practical usability and scalability to real-world deployment. Most existing re-id methods artificially assume the clothes of every single person to be stationary across space and time. This condition is mostly valid for short-term re-id scenarios since an average person would often change the clothes even within a single day. To alleviate this assumption, several recent works have introduced the clothes change facet to re-id, with a focus on supervised learning person identity discriminative representation with invariance to clothes changes. Taking a step further towards this long-term re-id direction, we further eliminate the requirement of person identity labels, as they are significantly more expensive and more tedious to annotate in comparison to short-term person re-id datasets. Compared to conventional unsupervised short-term re-id, this new problem is drastically more challenging as different people may have similar clothes whilst the same person can wear multiple suites of clothes over different locations and times with very distinct appearance. To overcome such obstacles, we introduce a novel Curriculum Person Clustering (CPC) method that can adaptively regulate the unsupervised clustering criterion according to the clustering confidence. Experiments on three long-term person re-id datasets show that our CPC outperforms SOTA unsupervised re-id methods and even closely matches the supervised re-id models.

翻译：摘要：我们研究了衣物变化场景下无监督行人重识别（Re-ID）问题，这是一个更具实用性和可扩展性、适用于真实世界部署的新挑战。现有大多数重识别方法人为假设每个人的衣物在空间和时间上保持不变。该假设在短期重识别场景中基本成立，但普通人即使在一天内也常会更换衣物。为缓解这一假设，近期研究将衣物变化维度引入重识别，重点学习对衣物变化具有不变性的监督式行人身份判别性表征。在长期重识别方向上更进一步，我们消除了行人身份标签的需求——相较于短期行人重识别数据集，这些标签的标注成本显著更高、过程更繁琐。与传统无监督短期重识别相比，新问题面临更大挑战：不同行人可能穿着相似衣物，而同一行人在不同地点和时间可能出现多套外观差异显著的服装。为解决这些难题，我们提出新颖的课程行人聚类（CPC）方法，该方法可根据聚类置信度自适应调整无监督聚类准则。在三个长期行人重识别数据集上的实验表明，我们的CPC方法性能超越现有最先进的无监督重识别方法，甚至与监督式重识别模型性能持平。