  Despite the fact that object detection, 3D pose estimation, and sub-category
recognition are highly correlated tasks, they are usually addressed
independently from each other because of the huge space of parameters. To
jointly model all of these tasks, we propose a coarse-to-fine hierarchical
representation, where each level of the hierarchy represents objects at a
different level of granularity. The hierarchical representation prevents
performance loss, which is often caused by the increase in the number of
parameters (as we consider more tasks to model), and the joint modelling
enables resolving ambiguities that exist in independent modelling of these
tasks. We augment PASCAL3D+ dataset with annotations for these tasks and show
that our hierarchical model is effective in joint modelling of object
detection, 3D pose estimation, and sub-category recognition.
