Cloth-changing person Re-IDentification (Re-ID) is a particularly challenging task, suffering from two limitations of inferior discriminative features and limited training samples. Existing methods mainly leverage auxiliary information to facilitate identity-relevant feature learning, including soft-biometrics features of shapes or gaits, and additional labels of clothing. However, this 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 annotation or data. 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, to take full advantage of fine-grained attributes, we present a Fine-grained Attribute Recomposition (FAR) module by recomposing image features with different attributes in the latent space. It significantly enhances 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. The code is available at https://github.com/QizaoWang/FIRe-CCReID.

翻译：换装行人重识别是一项极具挑战性的任务，主要受限于判别性特征不足与训练样本稀缺两大问题。现有方法大多依赖辅助信息来促进身份相关特征的学习，包括体型或步态等软生物特征以及服装附加标签。然而，在实际应用场景中，此类信息往往难以获取。本文提出一种新颖的细粒度表示与重组框架，在无需任何辅助标注或数据的情况下同时解决上述两个局限。具体而言，我们首先设计细粒度特征挖掘模块，对每个行人的图像进行独立聚类。该模块促使具有相似细粒度属性（如服装与视角）的图像聚集于同一簇。通过引入属性感知分类损失函数，基于不跨身份共享的簇标签进行细粒度学习，从而推动模型学习身份相关特征。此外，为充分利用细粒度属性，我们提出细粒度属性重组模块，通过在隐空间重组具有不同属性的图像特征，显著增强了鲁棒特征学习能力。大量实验表明，该框架在五个广泛使用的换装行人重识别基准数据集上均能达到最先进的性能。代码已开源：https://github.com/QizaoWang/FIRe-CCReID。