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                     on Neural Information Processing Systems. Vancouver: Curran Associates Inc., 2020. 20601–20611.
                 [10]   He KM, Zhang XY, Ren SQ, Sun J. Deep residual learning for image recognition. In: Proc. of the 2016 IEEE Conf. on Computer Vision
                     and Pattern Recognition (CVPR). Las Vegas: IEEE, 2016. 770–778. [doi: 10.1109/CVPR.2016.90]
                 [11]   Sandler M, Howard A, Zhu ML, Zhmoginov A, Chen LC. MobileNetV2: Inverted residuals and linear bottlenecks. In: Proc. of the 2018
                     IEEE/CVF  Conf.  on  Computer  Vision  and  Pattern  Recognition.  Salt  Lake  City:  IEEE,  2018.  4510–4520.  [doi:  10.1109/CVPR.2018.
                     00474]
                 [12]   Huang G, Liu Z, van der Maaten L, Weinberger KQ. Densely connected convolutional networks. In: Proc. of the 2017 IEEE Conf. on
                     Computer Vision and Pattern Recognition (CVPR). Honolulu: IEEE, 2017. 2261–2269. [doi: 10.1109/CVPR.2017.243]
                 [13]   Dosovitskiy  A,  Beyer  L,  Kolesnikov  A,  Weissenborn  D,  Zhai  XH,  Unterthiner  T,  Dehghani  M,  Minderer  M,  Heigold  G,  Gelly  S,
                     Uszkoreit J, Houlsby N. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv:2010.11929, 2021.
                 [14]   Ma NN, Zhang XY, Zheng HT, Sun J. ShuffleNet V2: Practical guidelines for efficient CNN architecture design. In: Proc. of the 15th
                     European Conf. on Computer Vision. Munich: Springer-Verlag, 2018. 122–138. [doi: 10.1007/978-3-030-01264-9_8]
                 [15]   Chao P, Kao CY, Ruan YS, Huang CH, Lin YL. HarDNet: A low memory traffic network. In: Proc. of the 2019 IEEE/CVF Int’l Conf. on
                     Computer Vision (ICCV). Seoul: IEEE, 2019. 3551–3560. [doi: 10.1109/ICCV.2019.00365]
                 [16]   Tan MX, Le Q. EfficientNet: Rethinking model scaling for convolutional neural networks. In: Proc. of the 36th Int’l Conf. on Machine
                     Learning. Long Beach: ICML, 2019. 6105–6114.
                 [17]   Hu J, Shen L, Albanie S, Sun G, Vedaldi A. Gather-excite: Exploiting feature context in convolutional neural networks. In: Proc. of the
                     32nd Int’l Conf. on Neural Information Processing Systems. Montréal: Curran Associates Inc., 2018. 9423–9433.
                 [18]   Ronneberger O, Fischer P, Brox T. U-Net: Convolutional networks for biomedical image segmentation. In: Proc. of the 18th Int’l Conf.
                     on Medical Image Computing and Computer-assisted Intervention. Munich: Springer, 2015. 234–241. [doi: 10.1007/978-3-319-24574-
                     4_28]
                 [19]   Zhao HS, Shi JP, Qi XJ, Wang XG, Jia JY. Pyramid scene parsing network. In: Proc. of the 2017 IEEE Conf. on Computer Vision and
                     Pattern Recognition (CVPR). Honolulu: IEEE, 2017. 6230–6239. [doi: 10.1109/CVPR.2017.660]

                 附中文参考文献
                 [2]   马艳军, 于佃海, 吴甜, 王海峰. 飞桨: 源于产业实践的开源深度学习平台. 数据与计算发展前沿, 2019, 1(1): 105–115. [doi: 10.11871/
                    jfdc.issn.2096.742X.2019.01.011]

                 作者简介
                 丁嵘, 副教授, 博士生导师, 主要研究领域为智能软件工程, 计算机视觉, 无人系统智能算法.
                 刘屹洲, 硕士, 主要研究领域为代码迁移, 知识图谱.
                 王雨倩, 硕士, 主要研究领域为预训练语言模型, 知识图谱嵌入.
                 李一錡, 博士生, 主要研究领域为智能软件.
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