Abstract
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.
Abstract (translated)
现代人员重识别（Re-ID）方法具有较弱的泛化能力，当捕捉环境发生变化时会遇到显著的准确率下降。这是因为现有的多摄像头Re-ID数据集在规模和多样性上有限，因为这类数据难以获取。同时，有大量的未标注单摄像头记录可供使用。这种数据易于收集，因此更加多样化。目前，单摄像头数据仅用于Re-ID方法的自监督预训练。然而，在预训练后用有限的多摄像头数据进行微调会抑制单摄像头数据的多样性。在本文中，我们提出了ReMix，这是一种在有限标记的多摄像头和大量的未标注单摄像头数据混合上共同训练的泛化Re-ID方法。通过一种新颖的数据采样策略和适应于两种类型数据联合使用的新的损失函数，实现了对我们的方法的有效训练。实验表明，ReMix具有较高的泛化能力，在可泛化的人员重识别方面优于最先进的方法。据我们所知，这是首次探索在人员重识别中使用多摄像头与单摄像头数据混合进行共同训练的工作。
URL
https://arxiv.org/abs/2410.21938
