  In recent years, supervised Person Re-identification (Person ReID) approaches
have demonstrated excellent performance. However, when these methods are
applied to inputs from a different camera network, they typically suffer from
significant performance degradation. Different from most domain adaptation (DA)
approaches addressing this issue, we focus on developing a domain
generalization (DG) Person ReID model that can be deployed without additional
fine-tuning or adaptation. In this paper, we propose the Domain Embedding
Expansion (DEX) module. DEX dynamically manipulates and augments deep features
based on person and domain labels during training, significantly improving the
generalization capability and robustness of Person ReID models to unseen
domains. We also developed a light version of DEX (DEXLite), applying negative
sampling techniques to scale to larger datasets and reduce memory usage for
multi-branch networks. Our proposed DEX and DEXLite can be combined with many
existing methods, Bag-of-Tricks (BagTricks), the Multi-Granularity Network
(MGN), and Part-Based Convolutional Baseline (PCB), in a plug-and-play manner.
With DEX and DEXLite, existing methods can gain significant improvements when
tested on other unseen datasets, thereby demonstrating the general
applicability of our method. Our solution outperforms the state-of-the-art DG
Person ReID methods in all large-scale benchmarks as well as in most the
small-scale benchmarks.
