EfficientPose: Efficient human pose estimation with neural architecture search

Computational Visual Media

Jiemin Fang¹  Wenqiang Zhang²  Xinggang Wang²  Wenyu Liu² 

[1] Institute of Artificial Intelligence, Huazhong University of Science and Technology, 430074, Wuhan, China;School of EIC, Huazhong University of Science and Technology, 430074, Wuhan, China;School of EIC, Huazhong University of Science and Technology, 430074, Wuhan, China;

关键词: pose estimation;    neural architecture search;    efficient deep learning;   

DOI  :  10.1007/s41095-021-0214-z

来源: Springer

【 摘 要 】

Human pose estimation from image and video is a key 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 lightweight devices. Nowadays, real-time multimedia applications call for more efficient models for better interaction. Moreover, most deep neural networks for pose estimation directly reuse networks designed for image classification as the backbone, which are not optimized for the pose estimation task. In this paper, we propose an efficient framework for human pose estimation with two parts, an efficient backbone and an efficient head. By implementing a differentiable neural architecture search method, we customize the backbone network design for pose estimation, and reduce computational 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 requires only 0.65 GFLOPs with 88.1% PCKh@0.5 on MPII and our large model needs only 2 GFLOPs while its accuracy is competitive with the state-of-the-art large model, HRNet, which takes 9.5 GFLOPs.

【 授权许可】

CC BY