Online Unsupervised Domain Adaptation (OUDA) for person Re-Identification (Re-ID) is the task of continuously adapting a model trained on a well-annotated source domain dataset to a target domain observed as a data stream. In OUDA, person Re-ID models face two main challenges: catastrophic forgetting and domain shift. In this work, we propose a new Source-guided Similarity Preservation (S2P) framework to alleviate these two problems. Our framework is based on the extraction of a support set composed of source images that maximizes the similarity with the target data. This support set is used to identify feature similarities that must be preserved during the learning process. S2P can incorporate multiple existing UDA methods to mitigate catastrophic forgetting. Our experiments show that S2P outperforms previous state-of-the-art methods on multiple real-to-real and synthetic-to-real challenging OUDA benchmarks.

翻译：在线无监督域适应（OUDA）行人重识别（Re-ID）任务旨在将基于标注充分的源域数据集训练的模型持续适应到以数据流形式观测的目标域。在OUDA中，行人重识别模型面临两大挑战：灾难性遗忘和域偏移。本文提出了一种新的源引导相似性保持（S2P）框架来缓解这两个问题。该框架通过提取由源图像构成的支撑集，最大化其与目标数据的相似性，并利用该支撑集识别学习过程中需保持的特征相似性。S2P可整合多种现有无监督域适应方法以缓解灾难性遗忘。实验表明，S2P在多个真实到真实及合成到真实的挑战性OUDA基准测试中均优于现有最优方法。