One-Shot Domain Adaptive and Generalizable Semantic Segmentation with Class-Aware Cross-Domain Transformers

Understand

Unsupervised sim-to-real domain adaptation (UDA) for semantic segmentation aims to improve the real-world test performance of a model trained on simulated data.

• It can save the cost of manually labeling data in real-world applications such as robot vision and autonomous driving.
• Traditional UDA often assumes that there are abundant unlabeled real-world data samples available during training for the adaptation.
• However, such an assumption does not always hold in practice owing to the collection difficulty and the scarcity of the data.