Cloth-Changing Person Re-Identification (CC-ReID) involves recognizing individuals in images regardless of clothing status. In this paper, we empirically and experimentally demonstrate that completely eliminating or fully retaining clothing features is detrimental to the task. Existing work, either relying on clothing labels, silhouettes, or other auxiliary data, fundamentally aim to balance the learning of clothing and identity features. However, we practically find that achieving this balance is challenging and nuanced. In this study, we introduce a novel module called Diverse Norm, which expands personal features into orthogonal spaces and employs channel attention to separate clothing and identity features. A sample re-weighting optimization strategy is also introduced to guarantee the opposite optimization direction. Diverse Norm presents a simple yet effective approach that does not require additional data. Furthermore, Diverse Norm can be seamlessly integrated ResNet50 and significantly outperforms the state-of-the-art methods.

本研究解决了服装变化人物重新识别（CC-ReID）中的特征平衡问题，探讨了完全去除或保留服装特征对识别效果的影响。提出的新模块“多样化归一化”通过扩展个人特征并采用通道注意力有效地分离服装和身份特征，显著提高了识别性能，并可无缝集成至ResNet50，超越现有最先进的方法。

学习平衡：服装变化的人物重新识别中的多样化归一化