Learning to Adapt Structured Output Space for Semantic Segmentation
Learning to Adapt Structured Output Space for Semantic Segmentation
Convolutional neural network-based approaches for semantic segmentation rely on supervision with pixel-level ground truth, but may not generalize well to unseen image domains. As the labeling process is tedious and labor intensive, developing algorithms that can adapt source ground truth labels to the target domain is of great interest. In …