OSSA: Unsupervised One-Shot Style Adaptation Publication date: 1 Oct 2024 Topic: Object detection Paper: https://arxiv.org/pdf/2410.00900v1.pdf GitHub: https://github.com/robingerster7/ossa Description: 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.