Conventional Unsupervised Domain Adaptation (UDA) strives to minimize
distribution discrepancy between domains, which neglects to harness rich
semantics from data and struggles to handle complex domain shifts. A promising
technique is to leverage the knowledge of large-scale pre-trained
vision-language models for more guided adaptation. Despite some endeavors,
current methods often learn textual prompts to embed domain semantics for
source and target domains separately and perform classification within each
domain, limiting cross-domain knowledge transfer. Moreover, prompting only the
language branch lacks flexibility to adapt both modalities dynamically. To
bridge this gap, we propose Domain-Agnostic Mutual Prompting (DAMP) to exploit
domain-invariant semantics by mutually aligning visual and textual embeddings.
Specifically, the image contextual information is utilized to prompt the
language branch in a domain-agnostic and instance-conditioned way. Meanwhile,
visual prompts are imposed based on the domain-agnostic textual prompt to
elicit domain-invariant visual embeddings. These two branches of prompts are
learned mutually with a cross-attention module and regularized with a
semantic-consistency loss and an instance-discrimination contrastive loss.
Experiments on three UDA benchmarks demonstrate the superiority of DAMP over
state-of-the-art approaches.

利用大规模预训练视觉 - 语言模型的知识进行领域不可知的相互提示，通过互相对齐视觉和文本嵌入来利用领域不变的语义，并通过语义一致性损失和实例差异对齐损失进行规则化，实验证明 Domain-Agnostic Mutual Prompting（DAMP）相较于现有方法在三个无监督领域自适应基准上具有优势。

领域无关的互助自适应方法用于无监督领域适应

Domain-Agnostic Mutual Prompting for Unsupervised Domain Adaptation

To leverage machine learning in any decision-making process, one must convert
the given knowledge (for example, natural language, unstructured text) into
representation vectors that can be understood and processed by machine learning
model in their compatible language and data format. The frequently encountered
difficulty is, however, the given knowledge is not rich or reliable enough in
the first place. In such cases, one seeks to fuse side information from a
separate domain to mitigate the gap between good representation learning and
the scarce knowledge in the domain of interest. This approach is named
Cross-Domain Knowledge Transfer. It is crucial to study the problem because of
the commonality of scarce knowledge in many scenarios, from online healthcare
platform analyses to financial market risk quantification, leaving an obstacle
in front of us benefiting from automated decision making. From the machine
learning perspective, the paradigm of semi-supervised learning takes advantage
of large amount of data without ground truth and achieves impressive learning
performance improvement. It is adopted in this dissertation for cross-domain
knowledge transfer. (to be continued)

通过跨领域知识转移来减少领域知识不足的影响，结合半监督学习及大量无监督数据，提高机器学习任务的性能以实现自动决策