Traditional cross-domain tasks, including domain adaptation and domain generalization, rely heavily on training model by source domain data. With the recent advance of vision-language models (VLMs), viewed as natural source models, the cross-domain task changes to directly adapt the pre-trained source model to arbitrary target domains equipped with prior domain knowledge, and we name this task Adaptive Domain Generalization (ADG). However, current cross-domain datasets have many limitations, such as unrealistic domains, unclear domain definitions, and the inability to fine-grained domain decomposition, which drives us to establish a novel dataset DomainVerse for ADG. Benefiting from the introduced hierarchical definition of domain shifts, DomainVerse consists of about 0.5 million images from 390 fine-grained realistic domains. With the help of the constructed DomainVerse and VLMs, we propose two methods called Domain CLIP and Domain++ CLIP for tuning-free adaptive domain generalization. Extensive and comprehensive experiments demonstrate the significance of the dataset and the effectiveness of the proposed methods.

传统的跨领域任务依赖于通过源领域数据训练模型；随着视觉语言模型 (VLMs) 最近的进展，跨领域任务转变为直接将预先训练的源模型与具有先验领域知识的任意目标领域进行适应，我们将其命名为自适应领域泛化 (ADG) 任务；然而，当前的跨领域数据集存在许多限制，我们为此建立了一个新的 DomainVerse 数据集，为 ADG 提供了层次化的领域转换定义和约 39 万细粒度真实领域的图像，然后通过构建的 DomainVerse 和 VLMs，我们提出了两种称为 Domain CLIP 和 Domain++ CLIP 的免调优自适应领域泛化方法；广泛而全面的实验证明了数据集的重要性及所提出方法的有效性。

DomainVerse:针对真实世界分布变化的基准测试，用于无需调整的自适应域泛化