Foundation models (e.g., CLIP or DINOv2) have shown their impressive learning
and transferring capabilities on a wide range of visual tasks, by training on a
large corpus of data and adapting to specific downstream tasks. It is, however,
interesting that foundation models have not been fully explored for universal
domain adaptation (UniDA), which is to learn models using labeled data in a
source domain and unlabeled data in a target one, such that the learned models
can successfully adapt to the target data. In this paper, we make comprehensive
empirical studies of state-of-the-art UniDA methods using foundation models. We
first demonstrate that, while foundation models greatly improve the performance
of the baseline methods that train the models on the source data alone,
existing UniDA methods generally fail to improve over the baseline. This
suggests that new research efforts are very necessary for UniDA using
foundation models. To this end, we propose a very simple method of target data
distillation on the CLIP model, and achieves consistent improvement over the
baseline across all the UniDA benchmarks. Our studies are under a newly
proposed evaluation metric of universal classification rate (UCR), which is
threshold- and ratio-free and addresses the threshold-sensitive issue
encountered when using the existing H-score metric.

基于基础模型（如 CLIP 或 DINOv2），本文通过全面实证研究最新的通用域自适应方法，发现现有方法往往无法超越基准线表现；作者提出了一种简单的基于目标数据提炼的方法，并在所有通用分类率基准测试中实现了一致的改进，提出了新的评价指标 UCR 来解决使用现有的 H-score 度量时遇到的阈值敏感问题。