When models, e.g., for semantic segmentation, are applied to images that are vastly different from training data, the performance will drop significantly. Domain adaptation methods try to overcome this issue, but need samples from the target domain. However, this might not always be feasible for various reasons and therefore domain generalization methods are useful as they do not require any target data. We present a new diffusion-based domain extension (DIDEX) method and employ a diffusion model to generate a pseudo-target domain with diverse text prompts. In contrast to existing methods, this allows to control the style and content of the generated images and to introduce a high diversity. In a second step, we train a generalizing model by adapting towards this pseudo-target domain. We outperform previous approaches by a large margin across various datasets and architectures without using any real data. For the generalization from GTA5, we improve state-of-the-art mIoU performance by 3.8% absolute on average and for SYNTHIA by 11.8% absolute, marking a big step for the generalization performance on these benchmarks. Code is available at https://github.com/JNiemeijer/DIDEX

翻译：当模型（例如用于语义分割的模型）应用于与训练数据差异极大的图像时，其性能会显著下降。域自适应方法试图解决这一问题，但需要目标域的样本。然而，由于各种原因这并非总是可行，因此域泛化方法因无需任何目标数据而颇具价值。我们提出了一种新的基于扩散的域扩展方法（DIDEX），利用扩散模型通过多样化的文本提示生成一个伪目标域。与现有方法相比，这能够控制生成图像的风格和内容，并引入高度多样性。在第二步中，我们通过向此伪目标域进行自适应来训练一个泛化模型。在不使用任何真实数据的情况下，我们在多个数据集和架构上大幅超越了先前方法。针对从GTA5进行泛化，我们将最先进的平均交并比性能绝对提升了3.8%；针对SYNTHIA则绝对提升了11.8%，这标志着在这些基准上的泛化性能取得了重大进展。代码已开源在https://github.com/JNiemeijer/DIDEX。