Human pose estimation from image and video is a vital task in many multimedia applications. Previous methods achieve great performance but rarely take efficiency into consideration, which makes it difficult to implement the networks on resource-constrained devices. Nowadays real-time multimedia applications call for more efficient models for better interactions. Moreover, most deep neural networks for pose estimation directly reuse the networks designed for image classification as the backbone, which are not yet optimized for the pose estimation task. In this paper, we propose an efficient framework targeted at human pose estimation including two parts, the efficient backbone and the efficient head. By implementing the differentiable neural architecture search method, we customize the backbone network design for pose estimation and reduce the computation cost with negligible accuracy degradation. For the efficient head, we slim the transposed convolutions and propose a spatial information correction module to promote the performance of the final prediction. In experiments, we evaluate our networks on the MPII and COCO datasets. Our smallest model has only 0.65 GFLOPs with 88.1% PCKh@0.5 on MPII and our large model has only 2 GFLOPs while its accuracy is competitive with the state-of-the-art large model, i.e., HRNet with 9.5 GFLOPs.

本文提出了一个高效的框架来解决人体姿势估计的问题，通过可微分神经架构搜索方法来个性化定制网络设计，并介绍了空间信息校正模块来提高最终预测的效果。实验结果表明，我们的最小模型只有0.65 GFLOPs，在 MPII 上具有 88.1% 的 PCKh@0.5，并且我们的大型模型只有2 GFLOPs，而其准确性与最先进的大型模型 HRNet 相当 。

EfficientPose：神经架构搜索优化的高效人体姿态估计