  Generalization to new domains not seen during training is one of the
long-standing challenges in deploying neural networks in real-world
applications. Existing generalization techniques either necessitate external
images for augmentation, and/or aim at learning invariant representations by
imposing various alignment constraints. Large-scale pretraining has recently
shown promising generalization capabilities, along with the potential of
binding different modalities. For instance, the advent of vision-language
models like CLIP has opened the doorway for vision models to exploit the
textual modality. In this paper, we introduce a simple framework for
generalizing semantic segmentation networks by employing language as the source
of randomization. Our recipe comprises three key ingredients: (i) the
preservation of the intrinsic CLIP robustness through minimal fine-tuning, (ii)
language-driven local style augmentation, and (iii) randomization by locally
mixing the source and augmented styles during training. Extensive experiments
report state-of-the-art results on various generalization benchmarks. Code is
accessible at https://github.com/astra-vision/FAMix .
