Domain-Aware Fine-Tuning of Foundation Models
<|reference_start|>Domain-Aware Fine-Tuning of Foundation Models: 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.<|reference_end|>
@article{kaplan2024domain-aware,
    title={Domain-Aware Fine-Tuning of Foundation Models},
    author={Ugur Ali Kaplan, Margret Keuper, Anna Khoreva, Dan Zhang, Yumeng Li},
    journal={arXiv preprint arXiv:2407.03482},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.03482},
    primaryClass={cs.CV cs.AI cs.LG}
}