揭示行人重识别中哪些语义特征真正重要
What really matters for person re-identification? A Mixture-of-Experts Framework for Semantic Attribute Importance
- 用专家混合框架分离不同属性的贡献，每个专家对应一个语义属性
- 发现服装颜色和内在特征影响最大，配饰等稀有线索作用有限
- 适合关注可解释性与语义知识融合的行人重识别研究者
当前最先进的行人重识别方法虽精度优异，但缺乏透明度，难以判断其依赖的高层语义属性。我们提出MoSAIC-ReID，一种基于LoRA的专家混合框架，系统量化行人属性对重识别的重要性。每个专家关联单一属性，通过门控路由器实现可控的归因分析。尽管在Market-1501和DukeMTMC上达到竞争性性能（假设测试时有属性标注），其核心价值在于首次大规模、定量分析内在与外在线索的属性重要性。结合广义线性模型、统计检验与特征重要性分析，发现服装颜色和内在特征贡献最显著，而配饰等低频线索影响较小。该工作为可解释重识别提供原则性框架，并揭示了实践中引入显式语义知识的需求。代码已开源。
原文摘要 · Abstract (English)
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 https://github.com/psaltaath/MoSAIC-ReID
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