Recent years have witnessed significant progress in person re-identification (ReID). However, current ReID approaches suffer from considerable performance degradation when the test target domains exhibit different characteristics from the training ones, known as the domain shift problem. To make ReID more practical and generalizable, we formulate person re-identification as a Domain Generalization (DG) problem and propose a novel training framework, named Multiple Domain Experts Collaborative Learning (MD-ExCo). Specifically, the MD-ExCo consists of a universal expert and several domain experts. Each domain expert focuses on learning from a specific domain, and periodically communicates with other domain experts to regulate its learning strategy in the meta-learning manner to avoid overfitting. Besides, the universal expert gathers knowledge from the domain experts, and also provides supervision to them as feedback. Extensive experiments on DG-ReID benchmarks show that our MD-ExCo outperforms the state-of-the-art methods by a large margin, showing its ability to improve the generalization capability of the ReID models.

翻译：然而,当测试目标领域与培训领域(称为域转移问题)具有不同特点时,目前的REID方法出现显著的性能退化。为了使REID更加实用和普遍化,我们将人重新确定为域通用问题,并提出名为多域专家合作学习(多域专家学习)的新培训框架。具体地说,MD-ExCO由一名通用专家和若干名域专家组成,每个域专家侧重于从特定领域学习,并定期与其他领域专家沟通,以元学习方式规范其学习战略,以避免过度使用。此外,普世专家从域专家那里收集知识,并将监督作为反馈。关于DG-RED基准的广泛实验表明,我们的MD-ExCO大大超越了最新方法,显示了其提高REID模式普及能力的能力。