仅用一张图就能让模型适应新风格，大幅提升目标检测效果
OSSA: Unsupervised One-Shot Style Adaptation
- 用单张目标图像生成多种风格，通过特征层适配实现无监督迁移
- 在多个场景下超越现有方法，甚至媲美使用数千张图的强基线
- 方法简单高效，适合数据稀缺的实际应用
尽管深度神经网络在各类视觉任务中表现优异，但在分布外场景下仍因训练与目标域风格差异而性能下降。为此，我们提出无监督的一次性风格自适应方法OSSA，仅需一张未标注的目标图像即可近似目标域风格。OSSA通过扰动该图像提取的风格统计量生成多样化的目标风格，并利用自适应实例归一化（AdaIN）在特征层面将这些风格应用于已标注源数据集。大量实验表明，OSSA在一次性域适应方法中达到新的领先水平，部分情况下甚至优于使用数千张未标注目标图像的强基线。在天气变化、模拟到真实（sim2real）及可见光到热成像等场景中的应用验证了风格差距的普遍重要性。OSSA结构简洁、效率高，易于集成至现有框架，为数据有限的实际场景提供可行解决方案。代码已公开于 https://github.com/RobinGerster7/OSSA。
原文摘要 · Abstract (English)
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
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