  We present AutoPose, a novel neural architecture search(NAS) framework that
is capable of automatically discovering multiple parallel branches of
cross-scale connections towards accurate and high-resolution 2D human pose
estimation. Recently, high-performance hand-crafted convolutional networks for
pose estimation show growing demands on multi-scale fusion and high-resolution
representations. However, current NAS works exhibit limited flexibility on
scale searching, they dominantly adopt simplified search spaces of
single-branch architectures. Such simplification limits the fusion of
information at different scales and fails to maintain high-resolution
representations. The presentedAutoPose framework is able to search for
multi-branch scales and network depth, in addition to the cell-level
microstructure. Motivated by the search space, a novel bi-level optimization
method is presented, where the network-level architecture is searched via
reinforcement learning, and the cell-level search is conducted by the
gradient-based method. Within 2.5 GPU days, AutoPose is able to find very
competitive architectures on the MS COCO dataset, that are also transferable to
the MPII dataset. Our code is available at
https://github.com/VITA-Group/AutoPose.
