An efficient sparse pruning method for human pose estimation-Reference-Cited by-同舟云学术

An efficient sparse pruning method for human pose estimation

Published:2021-12-13 Issue: Volume: Page:1-15
ISSN:0954-0091
Container-title:Connection Science
language:en
Short-container-title:Connection Science

Author:

Wang Mingyang¹²,Sun Tianyi³,Song Kang¹⁴,Li Shuang²,Jiang Jing⁴,Sun Linjun²

Affiliation:

1. College of Electronic and Information Engineering, Qingdao University, Qingdao, People’s Republic of China

2. Institute of Semi-Conductors, Chinese Academy of Sciences, Beijing, People’s Republic of China

3. Zhejiang Wanxin Digital Technology Co., Ltd, Hangzhou, People’s Republic of China

4. Shaanxi Key Laboratory of Information Communication Network and Security, Xi’an University of Posts and Telecommunications, Xi’an, People’s Republic of China

Funder

National Natural Science Foundation of China

Open Research Funds of Information Communication Networks and Security Key Laboratory of Shaanxi Province

Chinese Academy of Sciences

Publisher

Informa UK Limited

Subject

Artificial Intelligence,Human-Computer Interaction,Software

Link

https://www.tandfonline.com/doi/pdf/10.1080/09540091.2021.2012423

Reference47 articles.

1. Andriluka, M., Pishchulin, L., Gehler, P. V. & Schiele, B. (2014). 2D human pose estimation: New benchmark and state of the art analysis. 2014 IEEE Conference on Computer Vision and Pattern Recognition (pp. 3686–3693).

2. A categorical approach to affective gesture recognition

3. Bulat, A. & Tzimiropoulos, G. (2017). Binarized convolutional landmark localizers for human pose estimation and face alignment with limited resources. 2017 IEEE International Conference on Computer Vision (ICCV) (pp. 3726–3734).

4. OpenPose: Realtime Multi-Person 2D Pose Estimation Using Part Affinity Fields

5. Changpinyo, S., Sandler, M. & Zhmoginov, A. (2017). The power of sparsity in convolutional neural networks. arXiv preprint arXiv:1702.06257.

Cited by 14 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A lightweight attention-driven distillation model for human pose estimation;Pattern Recognition Letters;2024-09

2. Pruning‐guided feature distillation for an efficient transformer‐based pose estimation model;IET Computer Vision;2024-03-31

3. Empowering edge devices: FPGA‐based 16‐bit fixed‐point accelerator with SVD for CNN on 32‐bit memory‐limited systems;International Journal of Circuit Theory and Applications;2024-02-13

4. DSC-HRNet: a lightweight teaching pose estimation model with depthwise separable convolution and deep high-resolution representation learning in computer-aided education;International Journal of Information Technology;2023-05-31

5. A high-speed reusable quantized hardware accelerator design for CNN on constrained edge device;Design Automation for Embedded Systems;2023-04-26