  Human Pose Estimation (HPE) plays a crucial role in computer vision
applications. However, it is difficult to deploy state-of-the-art models on
resouce-limited devices due to the high computational costs of the networks. In
this work, a binary human pose estimator named BiHRNet(Binary HRNet) is
proposed, whose weights and activations are expressed as $\pm$1. BiHRNet
retains the keypoint extraction ability of HRNet, while using fewer computing
resources by adapting binary neural network (BNN). In order to reduce the
accuracy drop caused by network binarization, two categories of techniques are
proposed in this work. For optimizing the training process for binary pose
estimator, we propose a new loss function combining KL divergence loss with
AWing loss, which makes the binary network obtain more comprehensive output
distribution from its real-valued counterpart to reduce information loss caused
by binarization. For designing more binarization-friendly structures, we
propose a new information reconstruction bottleneck called IR Bottleneck to
retain more information in the initial stage of the network. In addition, we
also propose a multi-scale basic block called MS-Block for information
retention. Our work has less computation cost with few precision drop.
Experimental results demonstrate that BiHRNet achieves a PCKh of 87.9 on the
MPII dataset, which outperforms all binary pose estimation networks. On the
challenging of COCO dataset, the proposed method enables the binary neural
network to achieve 70.8 mAP, which is better than most tested lightweight
full-precision networks.
