  Mixture models are well-established learning approaches that, in computer
vision, have mostly been applied to inverse or ill-defined problems. However,
they are general-purpose divide-and-conquer techniques, splitting the input
space into relatively homogeneous subsets in a data-driven manner. Not only
ill-defined but also well-defined complex problems should benefit from them. To
this end, we devise a framework for spatial regression using mixture density
networks. We realize the framework for object detection and human pose
estimation. For both tasks, a mixture model yields higher accuracy and divides
the input space into interpretable modes. For object detection, mixture
components focus on object scale, with the distribution of components closely
following that of ground truth the object scale. This practically alleviates
the need for multi-scale testing, providing a superior speed-accuracy
trade-off. For human pose estimation, a mixture model divides the data based on
viewpoint and uncertainty -- namely, front and back views, with back view
imposing higher uncertainty. We conduct experiments on the MS COCO dataset and
do not face any mode collapse.
