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.

在线无监督领域适应（OUDA）用于人员重新识别（Re-ID）的任务是将在经过良好注释的源域数据集上训练的模型持续适应为作为数据流观察到的目标域。本文提出了一种新的基于源引导的相似性保持（S2P）框架来缓解这两个问题，并通过提取与目标数据最相似的源图像组成的支持集来标识在学习过程中必须保持的特征相似性。S2P 可以融合多个现有的 UDA 方法来减轻灾难性遗忘，并且我们的实验证明 S2P 在多个现实到现实和合成到现实的具有挑战性的 OUDA 基准测试中优于先前的最先进方法。

在线人物再识别的源导向相似性保持

Source-Guided Similarity Preservation for Online Person  Re-Identification

We propose online unsupervised domain adaptation (DA), which is performed
incrementally as data comes in and is applicable when batch DA is not possible.
In a part-of-speech (POS) tagging evaluation, we find that online unsupervised
DA performs as well as batch DA.

本文提出了实时的无监督领域自适应方法，可用于数据流的增量处理，其在词性标注任务中表现优异，甚至比起批量领域自适应表现还好。