  Multi-task partially annotated data where each data point is annotated for
only a single task are potentially helpful for data scarcity if a network can
leverage the inter-task relationship. In this paper, we study the joint
learning of object detection and semantic segmentation, the two most popular
vision problems, from multi-task data with partial annotations. Extensive
experiments are performed to evaluate each task performance and explore their
complementarity when a multi-task network cannot optimize both tasks
simultaneously. We propose employing knowledge distillation to leverage
joint-task optimization. The experimental results show favorable results for
multi-task learning and knowledge distillation over single-task learning and
even full supervision scenario. All code and data splits are available at
https://github.com/lhoangan/multas
