Exploring Fine-Grained Representation and Recomposition for
  Cloth-Changing Person Re-Identification
Qizao Wang Xuelin Qian Bin Li Ying Fu Yanwei Fu Xiangyang Xue
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.