  Human is one of the most essential classes in visual recognition tasks such
as detection, segmentation, and pose estimation. Although much effort has been
put into individual tasks, multi-task learning for these three tasks has been
rarely studied. In this paper, we explore a compact multi-task network
architecture that maximally shares the parameters of the multiple tasks via
object-centric learning. To this end, we propose a novel query design to encode
the human instance information effectively, called human-centric query (HCQ).
HCQ enables for the query to learn explicit and structural information of human
as well such as keypoints. Besides, we utilize HCQ in prediction heads of the
target tasks directly and also interweave HCQ with the deformable attention in
Transformer decoders to exploit a well-learned object-centric representation.
Experimental results show that the proposed multi-task network achieves
comparable accuracy to state-of-the-art task-specific models in human
detection, segmentation, and pose estimation task, while it consumes less
computational costs.
