  Neural Architecture Search (NAS) technologies have emerged in many domains to
jointly learn the architectures and weights of the neural network. However,
most existing NAS works claim they are task-specific and focus only on
optimizing a single architecture to replace a human-designed neural network, in
fact, their search processes are almost independent of domain knowledge of the
tasks. In this paper, we propose Pose Neural Fabrics Search (PoseNFS). We
explore a new solution for NAS and human pose estimation task: part-specific
neural architecture search, which can be seen as a variant of multi-task
learning. Firstly, we design a new neural architecture search space, Cell-based
Neural Fabric (CNF), to learn micro as well as macro neural architecture using
a differentiable search strategy. Then, we view locating human keypoints as
multiple disentangled prediction sub-tasks, and then use prior knowledge of
body structure as guidance to search for multiple part-specific neural
architectures for different human parts. After search, all these part-specific
CNFs have distinct micro and macro architecture parameters. The results show
that such knowledge-guided NAS-based architectures have obvious performance
improvements to a hand-designed part-based baseline model. The experiments on
MPII and MS-COCO datasets demonstrate that PoseNFS\footnote{Code is available
at \url{https://github.com/yangsenius/PoseNFS}} can achieve comparable
performance to some efficient and state-of-the-art methods.
