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.

翻译：行人重识别（ReID）因数据驱动的深度学习技术取得了显著进展。然而，现有基准数据集缺乏多样性，基于这些数据训练的模型难以泛化至动态野外场景。为提升ReID模型的显式泛化能力，我们构建了一个名为OWD的新型开放世界、多样化、跨时空数据集，具有以下显著特征：1）多样化采集场景：多个独立开放世界且高度动态的采集场景，包括街道、路口、商场等；2）多样化光照变化：从白天到黑夜的长跨度时间范围，伴随丰富的光照变化；3）多样化行人状态：涵盖四季多摄像头网络，包含正常/恶劣天气条件及多样化的行人外观（如衣物、随身物品、姿态等）；4）隐私保护：针对隐私关键应用采用不可见人脸。为提升ReID的隐式泛化能力，我们进一步提出潜在域扩展（LDE）方法，通过解耦判别性身份相关特征与可信域相关特征，隐式驱动身份特征空间的域随机化扩展（融入更丰富的域多样性），从而促进域不变表征学习。我们针对社区中大部分基准数据集进行的全面评估对进展至关重要，尽管本工作距离开放世界与动态野外应用的宏大目标仍存在差距。