[3] [3] TOSHEV A,SZEGEDY C. DeepPose:human pose estimation via deep neural networks[C]// 2014 IEEE Conference on Computer Vision and Pattern Recognition. Columbus,OH,USA:IEEE, 2014:1653-1660. doi:10.1109/CVPR.2014.214.

[4] [4] CARREIRA J,AGRAWAL P,FRAGKIADAKI K,et al. Human pose estimation with iterative error feedback[DB/OL]. (2015-07-23)[2022-08-09]. https://arxiv.org/abs/1507.06550. doi: 10.48550/arXiv.1507.06550.

[5] [5] WEI S E,RAMAKRISHNA V,KANADE T,et al. Convolutional pose machines[C]// 2016 IEEE Conference on Computer Vision and Pattern Recognition(CVPR). Las Vegas,NV,USA:IEEE, 2016:4724-4732. doi:10.1109/CVPR.2016.511.

[6] [6] NEWELL A, YANG Kaiyu, DENG Jia. Stacked hourglass networks for human pose estimation[J/OL]. Springer International Publishing, 2016:483-499. doi:10.1007/978-3-319-46484-8_29.

[7] [7] CHEN Yilun, WANG Zhicheng, PENG Yuxiang, et al. Cascaded pyramid network for multi-person pose estimation[C]// 2018IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City, UT, USA: IEEE, 2018: 7103-7112. doi:10.1109/CVPR.2018.00742.

[8] [8] FANG Haoshu, XIE Shuqin, TAI Y W, et al. RMPE: Regional Multi-person Pose Estimation[C]// 2017 IEEE International Conference on Computer Vision(ICCV). Venice,Italy:IEEE, 2017:2353-2362. doi:10.1109/ICCV.2017.256.

[9] [9] SUN Ke,XIAO Bin,LIU Dong, et al. Deep high-resolution representation learning for human pose estimation[C]// 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition(CVPR). Long Beach, CA, USA: IEEE, 2019: 5686-5696. doi:10.1109/CVPR.2019.00584.

[10] [10] LI W,WANG Z,YIN B,et al. Rethinking on multi-stage networks for human pose estimation[DB/OL]. (2019-01-01)[2022-08-09]. https://arxiv.org/abs/1901.00148. doi: 10.48550/arXiv.1901.00148.

[11] [11] INSAFUTDINOV E, PISHCHULIN L,ANDRES B, et al. DeeperCut: a deeper,stronger, and faster multi-person pose estimation model[C]// The 14th European Conference. Amsterdam, the Netherlands: Springer International Publishing, 2016: 34-50. doi:10.1007/ 978-3-319-46466-4_3.

[12] [12] PISHCHULIN L, INSAFUTDINOV E, TANG Siyu, et al. DeepCut: joint subset partition and labeling for multi person pose estimation[C]// 2016 IEEE Conference on Computer Vision and Pattern Recognition(CVPR). Las Vegas,NV,USA:IEEE, 2016:4929-4937. doi:10.1109/CVPR.2016.533.

[13] [13] NEWELL A, HUANG Zhi'ao, DENG Jia. Associative embedding: end-to-end learning for joint detection and grouping[C]//Proceedings of the 31st International Conference on Neural Information Processing Systems. [S. l.]:Association for Computing Machinery, 2017:2274-2284.

[14] [14] CAO Zhe, SIMON T,WEI S, et al. Realtime multi-person 2D pose estimation using part affinity fields[DB/OL]. (2016-11-24) [2022-08-09]. https://arxiv.org/abs/1611.08050. doi:10.48550/arXiv.1611.08050.

[15] [15] KREISS S, BERTONI L, ALAHI A. PifPaf: composite fields for human pose estimation[C]// 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition(CVPR). Long Beach, CA, USA: IEEE, 2019: 11969-11978. doi: 10.1109/CVPR.2019.01225.

[16] [16] CHENG Bowen,XIAO Bin,WANG Jingdong,et al. HigherHRNet:scale-aware representation learning for bottom-up human pose estimation[C]// 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition(CVPR). Seattle,WA,USA:IEEE, 2020:5385-5394. doi:10.1109/CVPR42600.2020.00543.

[17] [17] MEHTA S, RASTEGARI M. Mobilevit: light-weight, general-purpose, and mobile-friendly vision transformer[C]// IEEE Conference on Computer Vision and Pattern Recognition(CVPR). 2021. doi: 10.48550/arXiv.2110.02178.

[18] [18] SANDLER M,HOWARD A,ZHU Menglong,et al. MobileNetV2:inverted residuals and linear bottlenecks[C]// 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition(CVPR). Salt Lake City,UT,USA:IEEE, 2018:4510-4520. doi:10.1109/CVPR.2018.00474.

[19] [19] LIN T Y, DOLLáR P, GIRSHICK R, et al. Feature pyramid networks for object detection[C]// 2017 IEEE Conference on Computer Vision and Pattern Recognition(CVPR). Honolulu,HI,USA:IEEE, 2017:936-944. doi:10.1109/CVPR.2017.106.

[20] [20] BISWAS K,KUMAR S,BANERJEE S,et al. Smooth maximum unit:smooth activation function for deep networks using smoothing maximum technique[C]// IEEE Conference on Computer Vision and Pattern Recognition(CVPR). New Orleans,LA,USA:IEEE,2021:784-793. doi:10.1109/CVPR52688.2022.00087.

[21] [21] FU Jun,LIU Jing,TIAN Haijie,et al. Dual attention network for scene segmentation[C]// IEEE Conference on Computer Vision and Pattern Recognition(CVPR). Long Beach,CA,USA:IEEE, 2019:3146-3154.

[22] [22] HE Kaiming, ZHAGN Xiangyu, REN Shaoqing, et al. Deep residual learning for image recognition[C]// IEEE Conference on Computer Vision and Pattern Recognition(CVPR). Las Vegas,NV,USA:IEEE, 2016:770-778.

[23] [23] SIMONYAN K, ZISSERMAN A. Very deep convolutional networks for Large-Scale image recognition[DB/OL]. (2014-09-04) [2022-08-09]. https://arxiv.org/abs/1409.1556. doi: 10.48550/arXiv.1409.1556.

[24] [24] YU Changqian, XIAO Bin, GAO Changxin, et al. Lite-HRNet: a lightweight high-resolution network[C]// 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition(CVPR). Nashville,TN,USA:IEEE, 2021: 10435-10445. doi: 10.1109/CVPR46437.2021.01030.

[25] [25] HU Jie, SHEN Li, SUN Gang. Squeeze-and-Excitation networks[C]// 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City,UT,USA:IEEE, 2018:7132-7141. doi:10.1109/CVPR.2018.00745.

[26] [26] ZHANG Qinglong, YANG Yubin. SA-Net:shuffle attention for deep convolutional neural networks[C]// ICASSP 2021—2021IEEE International Conference on Acoustics, Speech and Signal Processing(ICASSP). Toronto,ON,Canada:IEEE, 2021: 2235-2239. doi:10.1109/ICASSP39728.2021.9414568.