  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.
