Semantic segmentation networks require large amounts of pixel-level annotated data, which are costly to obtain for real-world images. Computer graphics engines can generate synthetic images alongside their ground-truth annotations. However, models trained on such images can perform poorly on real images due to the domain gap between real and synthetic images. Style transfer methods can reduce this difference by applying a realistic style to synthetic images. Choosing effective data transformations and their sequence is difficult due to the large combinatorial search space of style transfer operators. Using multi-objective genetic algorithms, we optimize pipelines to balance structural coherence and style similarity to target domains. We study the use of paired-image metrics on individual image samples during evolution to enable rapid pipeline evaluation, as opposed to standard distributional metrics that require the generation of many images. After optimization, we evaluate the resulting Pareto front using distributional metrics and segmentation performance. We apply this approach to standard datasets in synthetic-to-real domain adaptation: from the video game GTA5 to real image datasets Cityscapes and ACDC, focusing on adverse conditions. Results demonstrate that evolutionary algorithms can propose diverse augmentation pipelines adapted to different objectives. The contribution of this work is the formulation of style transfer as a sequencing problem suitable for evolutionary optimization and the study of efficient metrics that enable feasible search in this space. The source code is available at: https://github.com/echigot/MOOSS.

翻译：语义分割网络需要大量像素级标注数据，而真实世界图像的标注获取成本高昂。计算机图形引擎可生成合成图像及其对应的真实标注。然而，由于真实图像与合成图像之间存在领域差异，在此类图像上训练的模型在真实图像上可能表现不佳。风格迁移方法可通过为合成图像施加真实风格来减小这种差异。由于风格迁移算子的组合搜索空间庞大，选择有效的数据变换及其执行顺序十分困难。我们采用多目标遗传算法优化处理流程，以平衡目标域的结构连贯性与风格相似性。我们研究了在进化过程中对单张图像样本使用配对图像度量方法，以实现快速流程评估，这与需要生成大量图像的标准分布度量方法形成对比。优化完成后，我们使用分布度量与分割性能评估所得的帕累托前沿。我们将此方法应用于合成到真实领域适应的标准数据集：从电子游戏GTA5到真实图像数据集Cityscapes和ACDC，重点关注恶劣环境条件。结果表明进化算法能够针对不同目标提出多样化的数据增强流程。本工作的贡献在于将风格迁移构建为适合进化优化的序列化问题，并研究了能够在该空间实现可行搜索的高效度量方法。源代码发布于：https://github.com/echigot/MOOSS。