  Reducing the representational discrepancy between source and target domains
is a key component to maximize the model generalization. In this work, we
advocate for leveraging natural language supervision for the domain
generalization task. We introduce two modules to ground visual representations
with texts containing typical reasoning of humans: (1) Visual and Textual Joint
Embedder and (2) Textual Explanation Generator. The former learns the
image-text joint embedding space where we can ground high-level
class-discriminative information into the model. The latter leverages an
explainable model and generates explanations justifying the rationale behind
its decision. To the best of our knowledge, this is the first work to leverage
the vision-and-language cross-modality approach for the domain generalization
task. Our experiments with a newly created CUB-DG benchmark dataset demonstrate
that cross-modality supervision can be successfully used to ground
domain-invariant visual representations and improve the model generalization.
Furthermore, in the large-scale DomainBed benchmark, our proposed method
achieves state-of-the-art results and ranks 1st in average performance for five
multi-domain datasets. The dataset and codes are available at
https://github.com/mswzeus/GVRT.
