ECAP: Extensive Cut-and-Paste Augmentation for Unsupervised Domain
  Adaptive Semantic Segmentation
Erik Brorsson Knut Åkesson Lennart Svensson Kristofer Bengtsson
We consider unsupervised domain adaptation (UDA) for semantic segmentation in
which the model is trained on a labeled source dataset and adapted to an
unlabeled target dataset. Unfortunately, current self-training methods are
susceptible to misclassified pseudo-labels resulting from erroneous
predictions. Since certain classes are typically associated with less reliable
predictions in UDA, reducing the impact of such pseudo-labels without skewing
the training towards some classes is notoriously difficult. To this end, we
propose an extensive cut-and-paste strategy (ECAP) to leverage reliable
pseudo-labels through data augmentation. Specifically, ECAP maintains a memory
bank of pseudo-labeled target samples throughout training and cut-and-pastes
the most confident ones onto the current training batch. We implement ECAP on
top of the recent method MIC and boost its performance on two synthetic-to-real
domain adaptation benchmarks. Notably, MIC+ECAP reaches an unprecedented
performance of 69.1 mIoU on the Synthia->Cityscapes benchmark. Our code is
available at https://github.com/ErikBrorsson/ECAP.