Grounding Visual Representations with Texts for Domain Generalization. (arXiv:2207.10285v2 [cs.CV] UPDATED)

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 …

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