  Single Domain Generalization (SDG) tackles the problem of training a model on
a single source domain so that it generalizes to any unseen target domain.
While this has been well studied for image classification, the literature on
SDG object detection remains almost non-existent. To address the challenges of
simultaneously learning robust object localization and representation, we
propose to leverage a pre-trained vision-language model to introduce semantic
domain concepts via textual prompts. We achieve this via a semantic
augmentation strategy acting on the features extracted by the detector
backbone, as well as a text-based classification loss. Our experiments evidence
the benefits of our approach, outperforming by 10% the only existing SDG object
detection method, Single-DGOD [49], on their own diverse weather-driving
benchmark.
