  Domain generalization (DG) attempts to generalize a model trained on single
or multiple source domains to the unseen target domain. Benefiting from the
success of Visual-and-Language Pre-trained models in recent years, we argue
that it is crucial for domain generalization by introducing extra text
information. In this paper, we develop a novel Text-guided Domain
Generalization (TDG) paradigm for domain generalization, which includes three
following aspects. Specifically, we first devise an automatic words generation
method to extend the description of current domains with novel domain-relevant
words. Then, we embed the generated domain information into the text feature
space, by the proposed prompt learning-based text feature generation method,
which shares a common representation space with the image feature. Finally, we
utilize both input image features and generated text features to train a
specially designed classifier that generalizes well on unseen target domains,
while the image encoder is also updated under the supervision of gradients back
propagated from the classifier. Our experimental results show that the
techniques incorporated by TDG contribute to the performance in an easy
implementation manner. Experimental results on several domain generalization
benchmarks show that our proposed framework achieves superior performance by
effectively leveraging generated text information in domain generalization.
