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Sun Ke \"Deep high-resolution representation learning for human pose estimation.\" Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. 2019.","DOI":"10.1109\/CVPR.2019.00584"},{"key":"e_1_3_2_1_3_1","volume-title":"Efficient convolutional neural networks for mobile vision applications.\" arXiv preprint arXiv:1704.04861","author":"Howard Andrew G","year":"2017","unstructured":"Howard , Andrew G . , \" Mobilenets : Efficient convolutional neural networks for mobile vision applications.\" arXiv preprint arXiv:1704.04861 ( 2017 ). Howard, Andrew G., \"Mobilenets: Efficient convolutional neural networks for mobile vision applications.\" arXiv preprint arXiv:1704.04861 (2017)."},{"key":"e_1_3_2_1_4_1","volume-title":"An extremely efficient convolutional neural network for mobile devices.\" Proceedings of the IEEE conference on computer vision and pattern recognition","author":"Zhang Xiangyu","year":"2018","unstructured":"Zhang , Xiangyu , \" Shufflenet : An extremely efficient convolutional neural network for mobile devices.\" Proceedings of the IEEE conference on computer vision and pattern recognition . 2018 . Zhang, Xiangyu, \"Shufflenet: An extremely efficient convolutional neural network for mobile devices.\" Proceedings of the IEEE conference on computer vision and pattern recognition. 2018."},{"key":"e_1_3_2_1_5_1","unstructured":"Hinton Geoffrey Oriol Vinyals and Jeff Dean. \"Distilling the knowledge in a neural network.\" arXiv preprint arXiv:1503.02531 2.7 (2015).  Hinton Geoffrey Oriol Vinyals and Jeff Dean. \"Distilling the knowledge in a neural network.\" arXiv preprint arXiv:1503.02531 2.7 (2015)."},{"key":"e_1_3_2_1_6_1","volume-title":"Hints for thin deep nets.\" arXiv preprint arXiv:1412.6550","author":"Romero Adriana","year":"2014","unstructured":"Romero , Adriana , \" Fitnets : Hints for thin deep nets.\" arXiv preprint arXiv:1412.6550 ( 2014 ). Romero, Adriana, \"Fitnets: Hints for thin deep nets.\" arXiv preprint arXiv:1412.6550 (2014)."},{"key":"e_1_3_2_1_7_1","volume-title":"Springer","author":"Newell Alejandro","year":"2016","unstructured":"Newell , Alejandro , Kaiyu Yang , and Jia Deng . \" Stacked hourglass networks for human pose estimation.\" European conference on computer vision . Springer , Cham , 2016 . Newell, Alejandro, Kaiyu Yang, and Jia Deng. \"Stacked hourglass networks for human pose estimation.\" European conference on computer vision. Springer, Cham, 2016."},{"key":"e_1_3_2_1_8_1","volume-title":"Practical guidelines for efficient cnn architecture design.\" Proceedings of the European conference on computer vision (ECCV)","author":"Ma Ningning","year":"2018","unstructured":"Ma , Ningning , \"Shufflenet v2 : Practical guidelines for efficient cnn architecture design.\" Proceedings of the European conference on computer vision (ECCV) . 2018 . Ma, Ningning, \"Shufflenet v2: Practical guidelines for efficient cnn architecture design.\" Proceedings of the European conference on computer vision (ECCV). 2018."},{"key":"e_1_3_2_1_9_1","volume-title":"Inverted residuals and linear bottlenecks.\" Proceedings of the IEEE conference on computer vision and pattern recognition","author":"Sandler Mark","year":"2018","unstructured":"Sandler , Mark , \"Mobilenetv2 : Inverted residuals and linear bottlenecks.\" Proceedings of the IEEE conference on computer vision and pattern recognition . 2018 . Sandler, Mark, \"Mobilenetv2: Inverted residuals and linear bottlenecks.\" Proceedings of the IEEE conference on computer vision and pattern recognition. 2018."},{"key":"e_1_3_2_1_10_1","volume-title":"A lightweight high-resolution network.\" Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Yu Changqian","year":"2021","unstructured":"Yu , Changqian , \"Lite-hrnet : A lightweight high-resolution network.\" Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition . 2021 . Yu, Changqian, \"Lite-hrnet: A lightweight high-resolution network.\" Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 2021."},{"key":"e_1_3_2_1_11_1","unstructured":"Simonyan Karen and Andrew Zisserman. \"Very deep convolutional networks for large-scale image recognition.\" arXiv preprint arXiv:1409.1556 (2014).  Simonyan Karen and Andrew Zisserman. \"Very deep convolutional networks for large-scale image recognition.\" arXiv preprint arXiv:1409.1556 (2014)."},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"crossref","unstructured":"Zhang Feng \"Distribution-aware coordinate representation for human pose estimation.\" Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. 2020.  Zhang Feng \"Distribution-aware coordinate representation for human pose estimation.\" Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. 2020.","DOI":"10.1109\/CVPR42600.2020.00712"},{"key":"e_1_3_2_1_13_1","volume-title":"Springer","author":"Cai Yuanhao","year":"2020","unstructured":"Cai , Yuanhao , \" Learning delicate local representations for multi-person pose estimation.\" European Conference on Computer Vision . Springer , Cham , 2020 . Cai, Yuanhao, \"Learning delicate local representations for multi-person pose estimation.\" European Conference on Computer Vision. Springer, Cham, 2020."},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"crossref","unstructured":"Xie Saining \"Aggregated residual transformations for deep neural networks.\" Proceedings of the IEEE conference on computer vision and pattern recognition. 2017.  Xie Saining \"Aggregated residual transformations for deep neural networks.\" Proceedings of the IEEE conference on computer vision and pattern recognition. 2017.","DOI":"10.1109\/CVPR.2017.634"},{"key":"e_1_3_2_1_15_1","first-page":"7281","article-title":"Hrformer: High-resolution vision transformer for dense predict","volume":"34","author":"Yuan Yuhui","year":"2021","unstructured":"Yuan , Yuhui , \" Hrformer: High-resolution vision transformer for dense predict .\" Advances in Neural Information Processing Systems 34 ( 2021 ): 7281 - 7293 . Yuan, Yuhui, \"Hrformer: High-resolution vision transformer for dense predict.\" Advances in Neural Information Processing Systems 34 (2021): 7281-7293.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_16_1","unstructured":"Tang Raphael \"Distilling task-specific knowledge from bert into simple neural networks.\" arXiv preprint arXiv:1903.12136 (2019).  Tang Raphael \"Distilling task-specific knowledge from bert into simple neural networks.\" arXiv preprint arXiv:1903.12136 (2019)."},{"key":"e_1_3_2_1_17_1","volume-title":"Proceedings of the IEEE conference on computer vision and pattern recognition.","author":"Zhang Ying","year":"2018","unstructured":"Zhang , Ying , \"Deep mutual learning.\" Proceedings of the IEEE conference on computer vision and pattern recognition. 2018 . Zhang, Ying, \"Deep mutual learning.\" Proceedings of the IEEE conference on computer vision and pattern recognition. 2018."},{"key":"e_1_3_2_1_18_1","volume-title":"Improving the performance of convolutional neural networks via attention transfer.\" arXiv preprint arXiv:1612.03928","author":"Zagoruyko Sergey","year":"2016","unstructured":"Zagoruyko , Sergey , and Nikos Komodakis . \"Paying more attention to attention : Improving the performance of convolutional neural networks via attention transfer.\" arXiv preprint arXiv:1612.03928 ( 2016 ). Zagoruyko, Sergey, and Nikos Komodakis. \"Paying more attention to attention: Improving the performance of convolutional neural networks via attention transfer.\" arXiv preprint arXiv:1612.03928 (2016)."},{"key":"e_1_3_2_1_19_1","volume-title":"Improving the performance of convolutional neural networks via attention transfer.\" arXiv preprint arXiv:1612.03928","author":"Zagoruyko Sergey","year":"2016","unstructured":"Zagoruyko , Sergey , and Nikos Komodakis . \"Paying more attention to attention : Improving the performance of convolutional neural networks via attention transfer.\" arXiv preprint arXiv:1612.03928 ( 2016 ). 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