Bidirectional Learning for Domain Adaptation of Semantic Segmentation

Understand

Domain adaptation for semantic image segmentation is very necessary since manually labeling large datasets with pixel-level labels is expensive and time consuming.

• Existing domain adaptation techniques either work on limited datasets, or yield not so good performance compared with supervised learning.
• In this paper, we propose a novel bidirectional learning framework for domain adaptation of segmentation.
• Using the bidirectional learning, the image translation model and the segmentation adaptation model can be learned alternatively and promote to each other.