  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.
