  Person re-identification (ReID) has made great strides thanks to the
data-driven deep learning techniques. However, the existing benchmark datasets
lack diversity, and models trained on these data cannot generalize well to
dynamic wild scenarios. To meet the goal of improving the explicit
generalization of ReID models, we develop a new Open-World, Diverse,
Cross-Spatial-Temporal dataset named OWD with several distinct features. 1)
Diverse collection scenes: multiple independent open-world and highly dynamic
collecting scenes, including streets, intersections, shopping malls, etc. 2)
Diverse lighting variations: long time spans from daytime to nighttime with
abundant illumination changes. 3) Diverse person status: multiple camera
networks in all seasons with normal/adverse weather conditions and diverse
pedestrian appearances (e.g., clothes, personal belongings, poses, etc.). 4)
Protected privacy: invisible faces for privacy critical applications. To
improve the implicit generalization of ReID, we further propose a Latent Domain
Expansion (LDE) method to develop the potential of source data, which decouples
discriminative identity-relevant and trustworthy domain-relevant features and
implicitly enforces domain-randomized identity feature space expansion with
richer domain diversity to facilitate domain invariant representations. Our
comprehensive evaluations with most benchmark datasets in the community are
crucial for progress, although this work is far from the grand goal toward
open-world and dynamic wild applications.
