The task of semantic segmentation requires a model to assign semantic labels to each pixel of an image. However, the performance of such models degrades when deployed in an unseen domain with different data distributions compared to the training domain. We present a new augmentation-driven approach to domain generalization for semantic segmentation using a re-parameterized vision transformer (ReVT) with weight averaging of multiple models after training. We evaluate our approach on several benchmark datasets and achieve state-of-the-art mIoU performance of 47.3% (prior art: 46.3%) for small models and of 50.1% (prior art: 47.8%) for midsized models on commonly used benchmark datasets. At the same time, our method requires fewer parameters and reaches a higher frame rate than the best prior art. It is also easy to implement and, unlike network ensembles, does not add any computational complexity during inference.

翻译：语义分割任务要求模型为图像中每个像素分配语义标签。然而，当模型部署在与训练域数据分布不同的未见域时，其性能会下降。我们提出了一种新的基于数据增强的域广义语义分割方法，该方法采用重参数化视觉Transformer（ReVT），并在训练后对多个模型进行权重平均。我们在多个基准数据集上评估了该方法，在常用基准数据集上，小模型实现了47.3%的先进平均交并比（mIoU）（先前最优：46.3%），中等模型实现了50.1%的mIoU（先前最优：47.8%）。同时，我们的方法参数更少，帧率高于先前最优方法。该方法易于实现，且与网络集成不同，在推理过程中不增加任何计算复杂度。