Human Pose Estimation via an Ultra-Lightweight Pose Distillation Network

Most current pose estimation methods have a high resource cost that makes them unusable in some resource-limited devices. To address this problem, we propose an ultra-lightweight end-to-end pose distillation network, which applies some helpful techniques to suitably balance the number of parameters and predictive accuracy. First, we designed a lightweight one-stage pose estimation network, which learns from an increasingly refined sequential expert network in an online knowledge distillation manner. Then, we constructed an ultra-lightweight re-parameterized pose estimation subnetwork that uses a multi-module design with weight sharing to improve the multi-scale image feature acquisition capability of the single-module design. When training was complete, we used the first re-parameterized module as the deployment network to retain the simple architecture. Finally, extensive experimental results demonstrated the detection precision and low parameters of our method.