Modern person re-identification (Re-ID) methods have a weak generalization ability and experience a major accuracy drop when capturing environments change. This is because existing multi-camera Re-ID datasets are limited in size and diversity, since such data is difficult to obtain. At the same time, enormous volumes of unlabeled single-camera records are available. Such data can be easily collected, and therefore, it is more diverse. Currently, single-camera data is used only for self-supervised pre-training of Re-ID methods. However, the diversity of single-camera data is suppressed by fine-tuning on limited multi-camera data after pre-training. In this paper, we propose ReMix, a generalized Re-ID method jointly trained on a mixture of limited labeled multi-camera and large unlabeled single-camera data. Effective training of our method is achieved through a novel data sampling strategy and new loss functions that are adapted for joint use with both types of data. Experiments show that ReMix has a high generalization ability and outperforms state-of-the-art methods in generalizable person Re-ID. To the best of our knowledge, this is the first work that explores joint training on a mixture of multi-camera and single-camera data in person Re-ID.

翻译：现代行人重识别方法泛化能力较弱，当捕获环境发生变化时会出现显著的精度下降。这是因为现有的多摄像头行人重识别数据集在规模和多样性上存在局限，此类数据难以获取。与此同时，海量的未标注单摄像头记录是可用的。这类数据易于采集，因此具有更高的多样性。目前，单摄像头数据仅用于行人重识别方法的自监督预训练。然而，在预训练后使用有限的多摄像头数据进行微调时，单摄像头数据的多样性优势会受到抑制。本文提出ReMix——一种在有限标注的多摄像头数据与大规模未标注单摄像头数据混合集上联合训练的广义行人重识别方法。通过适用于两类数据联合使用的新型数据采样策略和损失函数，实现了本方法的有效训练。实验表明，ReMix具有优异的泛化能力，在广义行人重识别任务上超越了现有最优方法。据我们所知，这是首个探索在行人重识别中混合多摄像头与单摄像头数据进行联合训练的研究工作。