Although deep learning models have shown impressive performance on supervised
learning tasks, they often struggle to generalize well when the training
(source) and test (target) domains differ. Unsupervised domain adaptation (DA)
has emerged as a popular solution to this problem. However, current DA
techniques rely on visual backbones, which may lack semantic richness. Despite
the potential of large-scale vision-language foundation models like CLIP, their
effectiveness for DA has yet to be fully explored. To address this gap, we
introduce AD-CLIP, a domain-agnostic prompt learning strategy for CLIP that
aims to solve the DA problem in the prompt space. We leverage the frozen vision
backbone of CLIP to extract both image style (domain) and content information,
which we apply to learn prompt tokens. Our prompts are designed to be
domain-invariant and class-generalizable, by conditioning prompt learning on
image style and content features simultaneously. We use standard supervised
contrastive learning in the source domain, while proposing an entropy
minimization strategy to align domains in the embedding space given the target
domain data. We also consider a scenario where only target domain samples are
available during testing, without any source domain data, and propose a
cross-domain style mapping network to hallucinate domain-agnostic tokens. Our
extensive experiments on three benchmark DA datasets demonstrate the
effectiveness of AD-CLIP compared to existing literature.

使用 AD-CLIP 解决图像领域适应问题，通过在 prompt 空间中提取图像风格和内容信息，采用标准的有监督对比学习和最小熵策略来对齐源领域和目标领域，同时提出跨领域风格映射网络来生成领域无关的标记，实验证明了 AD-CLIP 的有效性。