Abstract
State-of-the-art person re-identification methods achieve impressive accuracy but remain largely opaque, leaving open the question: which high-level semantic attributes do these models actually rely on? We propose MoSAIC-ReID, a Mixture-of-Experts framework that systematically quantifies the importance of pedestrian attributes for re-identification. Our approach uses LoRA-based experts, each linked to a single attribute, and an oracle router that enables controlled attribution analysis. While MoSAIC-ReID achieves competitive performance on Market-1501 and DukeMTMC under the assumption that attribute annotations are available at test time, its primary value lies in providing a large-scale, quantitative study of attribute importance across intrinsic and extrinsic cues. Using generalized linear models, statistical tests, and feature-importance analyses, we reveal which attributes, such as clothing colors and intrinsic characteristics, contribute most strongly, while infrequent cues (e.g. accessories) have limited effect. This work offers a principled framework for interpretable ReID and highlights the requirements for integrating explicit semantic knowledge in practice. Code is available at this https URL
Abstract (translated)
最先进的人员重新识别方法实现了令人印象深刻的准确度，但其内部运作仍相对不透明，这引发了一个问题：这些模型实际上依赖哪些高层次的语义属性？我们提出了MoSAIC-ReID，这是一种Mixture-of-Experts（专家混合）框架，能够系统地量化行人属性在重新识别中的重要性。我们的方法使用基于LoRA（低秩适应）的专家模块，每个模块与单一属性相关联，并且采用了一个oracle router（预言路由器），它能进行受控归因分析。 尽管假设测试时可以获取到属性标注的情况下，MoSAIC-ReID在Market-1501和DukeMTMC数据集上达到了竞争力的性能，但其主要价值在于通过大规模、定量研究来揭示跨内在和外在线索的各种属性的重要性。我们利用广义线性模型、统计测试以及特征重要度分析方法，揭示了哪些属性（如衣物颜色及内在特性）贡献最为显著，而那些不常见的提示（例如配饰）则影响有限。 这项工作为可解释的人员重新识别提供了一个原则性的框架，并强调了在实践中整合显式语义知识的需求。代码可以在提供的网址上获取。
URL
https://arxiv.org/abs/2512.08697
