Source-Free Domain Adaptation refers to a type of machine learning and domain adaptation where the model is trained on a source domain but is expected to perform well on a target domain without access to labeled target domain data during training. In traditional domain adaptation, there are usually labeled samples available from both the source and target domains to facilitate learning domain-invariant features. However, in source-free domain adaptation, the model needs to adapt to the target domain without any labeled samples from the target domain during the training phase.

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Source-Free Video Domain Adaptation with Spatial-Temporal-Historical Consistency Learning

Source-free domain adaptation (SFDA) is an emerging research topic that studies how to adapt a pretrained source model using unlabeled target data. It is derived from unsupervised domain adaptation but has the advantage of not requiring labeled source data to learn adaptive models. This makes it particularly useful in real-world applications where access to source data is restricted. While there has been some SFDA work for images, little attention has been paid to videos. Naively extending image-based methods to videos without considering the unique properties of videos often leads to unsatisfactory results. In this paper, we propose a simple and highly flexible method for Source-Free Video Domain Adaptation (SFVDA), which extensively exploits consistency learning for videos from spatial, temporal, and historical perspectives. Our method is based on the assumption that videos of the same action category are drawn from the same low-dimensional space, regardless of the spatio-temporal variations in the high-dimensional space that cause domain shifts. To overcome domain shifts, we simulate spatio-temporal variations by applying spatial and temporal augmentations on target videos, and encourage the model to make consistent predictions from a video and its augmented versions. Due to the simple design, our method can be applied to various SFVDA settings, and experiments show that our method achieves state-of-the-art performance for all the settings.