DomainVerse: A Benchmark Towards Real-World Distribution Shifts For
  Tuning-Free Adaptive Domain Generalization
Feng Hou Jin Yuan Ying Yang Yang Liu Yang Zhang Cheng Zhong Zhongchao Shi Jianping Fan Yong Rui Zhiqiang He
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.