Abstract
The Federated Domain Generalization for Person re-identification (FedDG-ReID) aims to learn a global server model that can be effectively generalized to source and target domains through distributed source domain data. Existing methods mainly improve the diversity of samples through style transformation, which to some extent enhances the generalization performance of the model. However, we discover that not all styles contribute to the generalization performance. Therefore, we define styles that are beneficial or harmful to the model's generalization performance as positive or negative styles. Based on this, new issues arise: How to effectively screen and continuously utilize the positive styles. To solve these problems, we propose a Style Screening and Continuous Utilization (SSCU) framework. Firstly, we design a Generalization Gain-guided Dynamic Style Memory (GGDSM) for each client model to screen and accumulate generated positive styles. Meanwhile, we propose a style memory recognition loss to fully leverage the positive styles memorized by Memory. Furthermore, we propose a Collaborative Style Training (CST) strategy to make full use of positive styles. Unlike traditional learning strategies, our approach leverages both newly generated styles and the accumulated positive styles stored in memory to train client models on two distinct branches. This training strategy is designed to effectively promote the rapid acquisition of new styles by the client models, and guarantees the continuous and thorough utilization of positive styles, which is highly beneficial for the model's generalization performance. Extensive experimental results demonstrate that our method outperforms existing methods in both the source domain and the target domain.
Abstract (translated)
联邦领域泛化人物再识别（FedDG-ReID）旨在通过分布式源域数据学习一个全局服务器模型，使其能够有效地推广到源域和目标域。现有方法主要通过风格转换来提高样本的多样性，在某种程度上增强了模型的泛化性能。然而，我们发现并非所有风格都对模型的泛化性能有贡献。因此，我们将有益于或有害于模型泛化性能的风格定义为正向风格或负向风格。基于此，新的问题出现了：如何有效地筛选和持续利用正向风格。为了应对这些问题，我们提出了一种风格筛选与连续利用（SSCU）框架。 首先，我们设计了针对每个客户端模型的泛化收益引导动态风格记忆（GGDSM），用于筛选并积累生成的正向风格。与此同时，我们提出了一个风格记忆识别损失，以充分利用内存中记住的正向风格。此外，我们还提出了一种协作式风格训练（CST）策略，以便充分使用正向风格。 与传统的学习策略不同，我们的方法利用了新产生的风格以及存储在内存中的积累的正向风格，在两个不同的分支上训练客户端模型。这种训练策略旨在有效促进客户端模型快速获取新的风格，并确保持续和彻底地利用正向风格，这对模型的泛化性能非常有利。广泛的实验结果表明，我们提出的方法在源域和目标域上均优于现有的方法。
URL
https://arxiv.org/abs/2507.16238
