  Foundation models (FMs) have revolutionized computer vision, enabling
effective learning across different domains. However, their performance under
domain shift is yet underexplored. This paper investigates the zero-shot domain
adaptation potential of FMs by comparing different backbone architectures and
introducing novel domain-aware components that leverage domain related textual
embeddings. We propose domain adaptive normalization, termed as Domino, which
explicitly leverages domain embeddings during fine-tuning, thus making the
model domain aware. Ultimately, Domino enables more robust computer vision
models that can adapt effectively to various unseen domains.
