  Multi-task learning is central to many real-world applications.
Unfortunately, obtaining labelled data for all tasks is time-consuming,
challenging, and expensive. Active Learning (AL) can be used to reduce this
burden. Existing techniques typically involve picking images to be annotated
and providing annotations for all tasks.
  In this paper, we show that it is more effective to select not only the
images to be annotated but also a subset of tasks for which to provide
annotations at each AL iteration. Furthermore, the annotations that are
provided can be used to guess pseudo-labels for the tasks that remain
unannotated. We demonstrate the effectiveness of our approach on several
popular multi-task datasets.
