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Learning to Adapt Structured Output Space for Semantic Segmentation
CVPR 2018 | We develop a semantic segmentation method for adapting source ground truth labels to the unseen target domain. To achieve it, we consider semantic segmentation as structured prediction with spatial similarities between the source and target domains and then adopt multi-level adversarial learning in the output space. We show that our method can perform adaptation under various settings, including synthetic-to-real and cross-city scenarios.
Collaborators: Yi-Hsuan Tsai, Wei-Chih Hung, Samuel Schulter, Kihyuk Sohn, Ming-Hsuan Yang, Manmohan Chandraker
