  Multitask learning is a common approach in machine learning, which allows to
train multiple objectives with a shared architecture. It has been shown that by
training multiple tasks together inference time and compute resources can be
saved, while the objectives performance remains on a similar or even higher
level. However, in perception related multitask networks only closely related
tasks can be found, such as object detection, instance and semantic
segmentation or depth estimation. Multitask networks with diverse tasks and
their effects with respect to efficiency on one another are not well studied.
In this paper we augment the CenterNet anchor-free approach for training
multiple diverse perception related tasks together, including the task of
object detection and semantic segmentation as well as human pose estimation. We
refer to this DNN as Multitask-CenterNet (MCN). Additionally, we study
different MCN settings for efficiency. The MCN can perform several tasks at
once while maintaining, and in some cases even exceeding, the performance
values of its corresponding single task networks. More importantly, the MCN
architecture decreases inference time and reduces network size when compared to
a composition of single task networks.
