  Unsupervised domain adaptation (UDA) for semantic segmentation addresses the
cross-domain problem with fine source domain labels. However, the acquisition
of semantic labels has always been a difficult step, many scenarios only have
weak labels (e.g. bounding boxes). For scenarios where weak supervision and
cross-domain problems coexist, this paper defines a new task: unsupervised
domain adaptation based on weak source domain labels (WUDA). To explore
solutions for this task, this paper proposes two intuitive frameworks: 1)
Perform weakly supervised semantic segmentation in the source domain, and then
implement unsupervised domain adaptation; 2) Train an object detection model
using source domain data, then detect objects in the target domain and
implement weakly supervised semantic segmentation. We observe that the two
frameworks behave differently when the datasets change. Therefore, we construct
dataset pairs with a wide range of domain shifts and conduct extended
experiments to analyze the impact of different domain shifts on the two
frameworks. In addition, to measure domain shift, we apply the metric
representation shift to urban landscape image segmentation for the first time.
The source code and constructed datasets are available at
\url{https://github.com/bupt-ai-cz/WUDA}.
