Cloth-changing person Re-IDentification (Re-ID) is a particularly challenging task, suffering from two limitations of inferior identity-relevant features and limited training samples. Existing methods mainly leverage auxiliary information to facilitate discriminative feature learning, including soft-biometrics features of shapes and gaits, and additional labels of clothing. However, these information may be unavailable in real-world applications. In this paper, we propose a novel FIne-grained Representation and Recomposition (FIRe$^{2}$) framework to tackle both limitations without any auxiliary information. Specifically, we first design a Fine-grained Feature Mining (FFM) module to separately cluster images of each person. Images with similar so-called fine-grained attributes (e.g., clothes and viewpoints) are encouraged to cluster together. An attribute-aware classification loss is introduced to perform fine-grained learning based on cluster labels, which are not shared among different people, promoting the model to learn identity-relevant features. Furthermore, by taking full advantage of the clustered fine-grained attributes, we present a Fine-grained Attribute Recomposition (FAR) module to recompose image features with different attributes in the latent space. It can significantly enhance representations for robust feature learning. Extensive experiments demonstrate that FIRe$^{2}$ can achieve state-of-the-art performance on five widely-used cloth-changing person Re-ID benchmarks.

翻译：换衣行人重识别是一项极具挑战性的任务，面临身份相关特征质量低下与训练样本有限两大局限。现有方法主要借助辅助信息促进判别性特征学习，包括体型与步态等软生物特征以及服饰标签等额外标注。然而，这些信息在实际应用中可能无法获取。本文提出一种新颖的细粒度表征与重组框架（FIRe$^{2}$），无需任何辅助信息即可同时应对上述两种局限。具体而言，我们首先设计细粒度特征挖掘模块，对每个行人的图像分别进行聚类。具有相似细粒度属性（如着装和视角）的图像被聚为一类。通过引入属性感知分类损失，基于聚类标签（不同行人之间不共享）进行细粒度学习，促使模型关注身份相关特征。此外，充分利用聚类后的细粒度属性，我们提出细粒度属性重组模块，在隐空间中对具有不同属性的图像特征进行重组。该方法能显著增强表征能力，实现鲁棒的特征学习。大量实验表明，FIRe$^{2}$在五个广泛使用的换衣行人重识别基准上均达到当前最优性能。