Grounding Visual Representations with Texts for Domain Generalization

Understand

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.