A lightweight pose estimation network with multi-scale receptive field

Abstract

Existing lightweight networks perform inferior to large-scale models in human pose estimation because of shallow model depths and limited receptive fields. Current approaches utilize large convolution kernels or attention mechanisms to encourage long-range receptive field learning at the expense of model redundancy. In this paper, we propose a novel Multi-scale Field Lightweight High-resolution Network (MFite-HRNet) for human pose estimation. Specifically, our model mainly consists of two lightweight blocks, a Multi-scale Receptive Field Block (MRB) and a Large Receptive Field Block (LRB), to learn informative multi-scale and long-range spatial context information. The MRB utilizes group depthwise dilation convolutions with varied dilation rates to extract multi-scale spatial relationships from different feature maps. The LRB leverages large depthwise convolution kernels to model large-range spatial knowledge at the low-level features. We apply MFite-HRNet to single-person and multi-person pose estimation tasks. Experiments on COCO, MPII, and CrowdPose datasets demonstrate that our network outperforms current state-of-the-art lightweight networks in either single-person or multi-person pose estimation tasks. The source code will be publicly available at https://github.com/lskdje/MFite-HRNet.git.