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EfficientPose: Efficient human pose estimation with neural architecture search

Computational Visual Media volume 7, pages 335–347 (2021)Cite this article

Abstract

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.

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Acknowledgements

This work was in part supported by National Natural Science Foundation of China (NSFC) (Nos. 61733007 and 61876212) and Zhejiang Lab (No. 2019NB0AB02).

Author information

Author notes

  1. Wenqian Zhang and Jiemin Fang contributed equally to this work.

Affiliations

  1. School of EIC, Huazhong University of Science and Technology, Wuhan, 430074, China

    Wenqiang Zhang, Jiemin Fang, Xinggang Wang & Wenyu Liu

  2. Institute of Artificial Intelligence, Huazhong University of Science and Technology, Wuhan, 430074, China

    Jiemin Fang

Authors
  1. Wenqiang Zhang
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  2. Jiemin Fang
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Corresponding author

Correspondence to Xinggang Wang.

Additional information

Wenqiang Zhang is a master student in the School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan. His research interests include pose estimation and neural architecture search.

Jiemin Fang received his B.E. degree from the School of Electronic Information and Communications, Huazhong University of Science and Technology in 2018. He is currently a Ph.D. candidate at the Institute of Artificial Intelligence and School of Electronic Information and Communications, Huazhong University of Science and Technology. His research interests include AutoML and efficient deep learning.

Xinggang Wang received his B.S. and Ph.D. degrees in electronics and information engineering from Huazhong University of Science and Technology, in 2009 and 2014, respectively. He is currently an associate professor with the School of Electronic Information and Communications, HUST. His research interests include computer vision and machine learning.

Wenyu Liu received his B.S. degree in computer science from Tsinghua University, Beijing, China, in 1986, and his M.S. and Ph.D. degrees, both in electronics and information engineering, from Huazhong University of Science and Technology (HUST), in 1991 and 2001, respectively. He is now a professor and associate dean of the School of Electronic Information and Communications, HUST. His current research areas include computer vision, multimedia, and machine learning.

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Zhang, W., Fang, J., Wang, X. et al. EfficientPose: Efficient human pose estimation with neural architecture search. Comp. Visual Media 7, 335–347 (2021). https://doi.org/10.1007/s41095-021-0214-z

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  • DOI: https://doi.org/10.1007/s41095-021-0214-z

Keywords

  • pose estimation
  • neural architecture search
  • efficient deep learning