Abstract
Deep learning models for computer vision often suffer from poor generalization when deployed in real-world settings, especially when trained on synthetic data due to the well-known Sim2Real gap. Despite the growing popularity of style transfer as a data augmentation strategy for domain generalization, the literature contains unresolved contradictions regarding three key design axes: the diversity of the style pool, the role of texture complexity, and the choice of style source. We present a systematic empirical study that isolates and evaluates each of these factors for driving scene understanding, resolving inconsistencies in prior work. Our findings show that (i) expanding the style pool yields larger gains than repeated augmentation with few styles, (ii) texture complexity has no significant effect when the pool is sufficiently large, and (iii) diverse artistic styles outperform domain-aligned alternatives. Guided by these insights, we derive StyleMixDG (Style-Mixing for Domain Generalization), a lightweight, model-agnostic augmentation recipe that requires no architectural modifications or additional losses. Evaluated on the GTAV $\rightarrow$ {BDD100k, Cityscapes, Mapillary Vistas} benchmark, StyleMixDG demonstrates consistent improvements over strong baselines, confirming that the empirically identified design principles translate into practical gains. The code will be released on GitHub.
Abstract (translated)
计算机视觉深度学习模型在真实世界场景中部署时，常因Sim2Real差距（合成数据与真实数据间的分布差异）而面临泛化能力不足的问题。尽管风格迁移作为领域泛化的数据增强策略日益流行，但关于三个关键设计维度——风格池多样性、纹理复杂度的作用以及风格源的选择——学界仍存在未解决的矛盾。本研究通过系统性实证分析，分离并评估了这些因素对驾驶场景理解任务的影响，解决了先前工作中的矛盾。研究发现：(i) 扩大风格池比重复使用少量风格带来更大提升；(ii) 当风格池足够大时，纹理复杂度的影响不显著；(iii) 多样化艺术风格优于领域对齐的替代方案。基于这些洞见，我们提出了StyleMixDG（面向领域泛化的风格混合），一种轻量级、模型无关的数据增强方案，无需修改网络结构或增加损失函数。在GTAV→{BDD100k, Cityscapes, Mapillary Vistas}基准测试中，StyleMixDG相较于强基线持续取得改进，验证了实证设计原则可转化为实际性能提升。代码将在GitHub发布。
URL
https://arxiv.org/abs/2604.05616
