A Re-Parameterized Vision Transformer (ReVT) for Domain-Generalized
  Semantic Segmentation
Jan-Aike Termöhlen Timo Bartels Tim Fingscheidt
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.