[1] Chen Y, Tian Y, He M. Monocular human pose estimation: A survey of deep learning-based methods［J］. Computer Vision and Image Understanding, 2020, 192: 102897.

[2] Newell A, Yang K, Deng J. Stacked hourglass networks for human pose estimation［C］// Proc. of European Conf. on Computer Vision (ECCV), 2016: 483-499.

[3] Xiao B, Wu H, Wei Y. Simple baselines for human pose estimation and tracking［C］// Proc. of European Conf. on Computer Vision (ECCV), 2018: 466-481.

[4] Sun K, Xiao B, Liu D, et al. Deep high-resolution representation learning for human pose estimation［C］// Proc. of the IEEE/CVF Conf. on Computer Vision and Pattern Recognition, 2019: 5693-5703.

[5] Yu C, Xiao B, Gao C, et al. Lite-HRNet: A lightweight high-resolution network［C］// Proc. of the IEEE/CVF Conf. on Computer Vision and Pattern Recognition, 2021: 10440-10450.

[6] Sandler M, Howard A, Zhu M, et al. Mobilenetv2: Inverted residuals and linear bottlenecks［C］// Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition, 2018: 4510-4520.

[7] Ma N, Zhang X, Zheng H T, et al. ShuffleNet V2: Practical guidelines for efficient CNN architecture design［C］// Proc. of the European Conf. on Computer Vision (ECCV), 2018: 116-131.

[8] Li J, Wang Z, Qi B, et al. Meme: A mutually enhanced modeling method for efficient and effective human pose estimation［J］. Sensors, 2022, 22(2): 632.

[9] He K, Zhang X, Ren S, et al. Deep residual learning for image recognition［C］// Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition, 2016: 770-778.

[10] Shorten C, Khoshgoftaar T M. A survey on image data augmentation for deep learning［J］. J. of Big Data, 2019, 6(1): 1-48.

[11] Cubuk E D, Zoph B, Mane D, et al. AutoAugment: Learning augmentation strategies from data［C］// Proc. of the IEEE/CVF Conf. on Computer Vision and Pattern Recognition, 2019: 113-123.

[12] Krizhevsky A, Sutskever I, Hinton G E. Imagenet classification with deep convolutional neural networks［J］. Adv. in Neural Information Processing Systems, 2012, 25(2): 84-90.

[13] HüLlermeier E, Waegeman W. Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods［J］. Machine Learning, 2021, 110(3): 457-506.

[14] Wang G, Li W, Aertsen M, et al. Aleatoric uncertainty estimation with test-time augmentation for medical image segmentation with convolutional neural networks［J］. Neurocomputing, 2019, 338: 34-45.