DADA: Depth-aware Domain Adaptation in Semantic Segmentation

Understand

Unsupervised domain adaptation (UDA) is important for applications where large scale annotation of representative data is challenging.

• For semantic segmentation in particular, it helps deploy on real "target domain" data models that are trained on annotated images from a different "source domain", notably a virtual environment.
• To this end, most previous works consider semantic segmentation as the only mode of supervision for source domain data, while ignoring other, possibly available, information like depth.
• In this work, we aim at exploiting at best such a privileged information while training the UDA model.