Unsupervised domain adaptation (UDA) tries to overcome the tedious work of
labeling data by leveraging a labeled source dataset and transferring its
knowledge to a similar but different target dataset. On the other hand, current
vision-language models exhibit astonishing zero-shot prediction capabilities.
In this work, we combine knowledge gained through UDA with the inherent
knowledge of vision-language models. In a first step, we generate the zero-shot
predictions of the source and target dataset using the vision-language model.
Since zero-shot predictions usually exhibit a large entropy, meaning that the
class probabilities are rather evenly distributed, we first adjust the
distribution to accentuate the winning probabilities. This is done using both
source and target data to keep the relative confidence between source and
target data. We then employ a conventional DA method, to gain the knowledge
from the source dataset, in combination with self-knowledge distillation, to
maintain the inherent knowledge of the vision-language model. We further
combine our method with a gradual source domain expansion strategy (GSDE) and
show that this strategy can also benefit by including zero-shot predictions. We
conduct experiments and ablation studies on three benchmarks (OfficeHome,
VisDA, and DomainNet) and outperform state-of-the-art methods. We further show
in ablation studies the contributions of different parts of our algorithm.

无监督领域自适应 (UDA) 通过利用标记的源数据集并将其知识转移到相似但不同的目标数据集，克服了标记数据的繁琐工作。本文结合 UDA 获得的知识与视觉 - 语言模型的内在知识。通过视觉 - 语言模型生成源数据集和目标数据集的零样本预测，调整分布以凸显获胜概率，同时使用源数据和目标数据以保持相对置信度。我们通过传统的领域自适应方法和自知识蒸馏法结合实现对源数据集的知识获取，并将该方法与一种渐进源域扩展策略 (GSDE) 相结合，结果表明零样本预测也有益处。我们在三个基准测试集 (OfficeHome、VisDA 和 DomainNet) 上进行实验和消融研究，超过了最先进的方法，并在消融研究中展示了我们算法不同部分的贡献。