OSSA: Unsupervised One-Shot Style Adaptation
<|reference_start|>OSSA: Unsupervised One-Shot Style Adaptation: Despite their success in various vision tasks, deep neural network architectures often underperform in out-of-distribution scenarios due to the difference between training and target domain style. To address this limitation, we introduce One-Shot Style Adaptation (OSSA), a novel unsupervised domain adaptation method for object detection that utilizes a single, unlabeled target image to approximate the target domain style. Specifically, OSSA generates diverse target styles by perturbing the style statistics derived from a single target image and then applies these styles to a labeled source dataset at the feature level using Adaptive Instance Normalization (AdaIN). Extensive experiments show that OSSA establishes a new state-of-the-art among one-shot domain adaptation methods by a significant margin, and in some cases, even outperforms strong baselines that use thousands of unlabeled target images. By applying OSSA in various scenarios, including weather, simulated-to-real (sim2real), and visual-to-thermal adaptations, our study explores the overarching significance of the style gap in these contexts. OSSA's simplicity and efficiency allow easy integration into existing frameworks, providing a potentially viable solution for practical applications with limited data availability. Code is available at https://github.com/RobinGerster7/OSSA<|reference_end|>
@article{gerster2024ossa:,
    title={OSSA: Unsupervised One-Shot Style Adaptation},
    author={Robin Gerster, Holger Caesar, Matthias Rapp, Alexander Wolpert, and
  Michael Teutsch},
    journal={arXiv preprint arXiv:2410.00900},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.00900},
    primaryClass={cs.CV}
}