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Multitask learning is a common approach in machine learning, which allows totrain multiple objectives with a shared architecture. It has been shown that bytraining multiple tasks together inference time and compute resources can besaved, while the objectives performance remains on a similar or even higherlevel. However, in perception related multitask networks only closely relatedtasks can be found, such as object detection, instance and semanticsegmentation or depth estimation. Multitask networks with diverse tasks andtheir effects with respect to efficiency on one another are not well studied.In this paper we augment the CenterNet anchor-free approach for trainingmultiple diverse perception related tasks together, including the task ofobject detection and semantic segmentation as well as human pose estimation. Werefer to this DNN as Multitask-CenterNet (MCN). Additionally, we studydifferent MCN settings for efficiency. The MCN can perform several tasks atonce while maintaining, and in some cases even exceeding, the performancevalues of its corresponding single task networks. More importantly, the MCNarchitecture decreases inference time and reduces network size when compared toa composition of single task networks.

MultiTask-CenterNet (MCN): Efficient and Diverse Multitask Learning using an Anchor Free Approach