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 (FIRe2) 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 FIRe2 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.

中文翻译：

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探索换衣者重新识别的细粒度表示和重组


换衣服的人重新识别（Re-ID）是一项特别具有挑战性的任务，受到较差的判别特征和有限的训练样本两个限制。现有方法主要利用辅助信息来促进身份相关特征学习，包括形状或步态的软生物特征以及服装的附加标签。然而，这些信息在实际应用中可能不可用。在本文中，我们提出了一种新颖的细粒度表示和重组（FIRe2）框架来解决这两个限制，而无需任何辅助注释或数据。具体来说，我们首先设计一个细粒度特征挖掘（FFM）模块来单独聚类每个人的图像。鼓励具有相似的所谓细粒度属性（例如，衣服和观点）的图像聚集在一起。引入属性感知分类损失来基于聚类标签进行细粒度学习，这些标签在不同的人之间不共享，从而促进模型学习身份相关特征。此外，为了充分利用细粒度属性，我们通过在潜在空间中重组具有不同属性的图像特征，提出了细粒度属性重组（FAR）模块。它显着增强了鲁棒的特征学习。大量实验表明，FIRe2 可以在五个广泛使用的换衣人员 Re-ID 基准测试中实现最先进的性能。代码可在 https://github.com/QizaoWang/FIRe-CCReID 获取。