Exploring Fine-Grained Representation and Recomposition for Cloth-Changing Person Re-Identification

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 softbiometrics 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 FInegrained 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 attributeaware 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.