IDA: Informed Domain Adaptive Semantic Segmentation
Zheng Chen,Zhengming Ding,Jason M. Gregory,Lantao Liu,Zheng Chen,Zhengming Ding,Jason M. Gregory,Lantao Liu
Mixup-based data augmentation has been validated to be a critical stage in the self-training framework for unsupervised domain adaptive semantic segmentation (UDASS), which aims to transfer knowledge from a well-annotated (source) domain to an unlabeled (target) domain. Existing self-training methods usually adopt the popular region-based mixup techniques with a random sampling strategy, which unf...
