---

参考文献列表

[1] E. Shelhamer, J. Long, and T. Darrell, “Fully convolutional networks for semantic segmentation,” in Proc. Comput. Vis. Pattern Recognit., Jun. 2015, pp. 3431–3440.

中文翻译：[1] 谢尔哈默, E., 龙, J., & 达雷尔, T. (2015). 用于语义分割的全卷积网络. 在计算机视觉与模式识别会议论文集, 3431-3440.

[2] H. L. Yang, J. Yuan, D. Lunga, M. Laverdiere, A. Rose, and B. Bhaduri, “Building extraction at scale using convolutional neural network: Mapping of the United States,” IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 11, no. 8, pp. 2600–2614, Aug. 2018.

中文翻译：[2] 杨, H. L., 元, J., 肺加, D., 拉弗迪埃, M., 罗斯, A., & 巴哈杜里, B. (2018). 使用卷积神经网络进行大规模建筑提取：美国的制图. IEEE 选题应用地球观测与遥感杂志, 11(8), 2600-2614.

[3] K. Bittner, S. Cui, and P. Reinartz, “Building extraction from remote sensing data using fully convolutional networks,” Int. Arch. Photogramm., Remote Sens. Spatial Inf. Sci., vol. XLII-1/W1, pp. 481–486, May 2017.

中文翻译：[3] 比特纳, K., 崔, S., & 雷纳茨, P. (2017). 使用全卷积网络从遥感数据中提取建筑. 国际摄影测量与遥感科学档案, XLII-1/W1, 481-486.

[4] S. Shrestha and L. Vanneschi, “Improved fully convolutional network with conditional random fields for building extraction,” Remote Sens., vol. 10, no. 7, Jul. 2018, Art. no. 1135.

中文翻译：[4] 舍斯特哈, S., & 瓦内斯基, L. (2018). 改进的条件随机场全卷积网络用于建筑提取. 遥感, 10(7), 文章编号 1135.

[5] Y. Wei, Z. Wang, and M. Xu, “Road structure refined CNN for road extraction in aerial image,” IEEE Geosci. Remote Sens. Lett., vol. 14, no. 5, pp. 709–713, May 2017.

中文翻译：[5] 魏, Y., 王, Z., & 徐, M. (2017). 用于航拍图像中道路提取的道路结构细化卷积神经网络. IEEE 地球科学与遥感快报, 14(5), 709-713.

[6] R. Alshehhi, P. R. Marpu, W. L. Woon, and M. D. Mura, “Simultaneous extraction of roads and buildings in remote sensing imagery with convolutional neural networks,” ISPRS J. Photogramm. Remote Sens., vol. 130, pp. 139–149, Aug. 2017.

中文翻译：[6] 阿尔谢赫, R., 马尔普, P. R., 伍恩, W. L., & 穆拉, M. D. (2017). 使用卷积神经网络同时提取遥感图像中的道路和建筑. ISPRS 摄影测量与遥感杂志, 130, 139-149.

[7] Y. Liu, J. Yao, X. Lu, M. Xia, X. Wang, and Y. Liu, “RoadNet: Learning to comprehensively analyze road networks in complex urban scenes from high-resolution remotely sensed images,” IEEE Trans. Geosci. Remote Sens., vol. 57, no. 4, pp. 2043–2056, Apr. 2019.

中文翻译：[7] 刘, Y., 姚, J., 卢, X., 夏, M., 王, X., & 刘, Y. (2019). RoadNet: 从高分辨率遥感图像中学习全面分析复杂城市场景中的道路网络. IEEE 地球科学与遥感杂志, 57(4), 2043-2056.

[8] T. Kattenborn, J. Eichel, and F. E. Fassnacht, “Convolutional neural networks enable efficient, accurate and fine-grained segmentation of plant species and communities from high-resolution UAV imagery,” Sci. Rep., vol. 9, no. 1, Dec. 2019, Art. no. 17656.

中文翻译：[8] 卡滕伯恩, T., 埃希, J., & 法斯纳赫特, F. E. (2019). 卷积神经网络实现从高分辨率无人机图像中高效、准确和细粒度地分割植物种类和群落. 科学报告, 9(1), 文章编号 17656.

[9] A. Safonova, S. Tabik, D. Alcaraz-Segura, A. Rubtsov, Y. Maglinets, and F. Herrera, “Detection of fir trees (Abies sibirica) damaged by the bark beetle in unmanned aerial vehicle images with deep learning,” Remote Sens., vol. 11, no. 6, Mar. 2019, Art. no. 643.

中文翻译：[9] 萨福诺娃, A., 塔比克, S., 阿尔卡拉兹-塞古拉, D., 鲁布佐夫, A., 马格利内茨, Y., & 赫雷拉, F. (2019). 使用深度学习在无人机图像中检测被树皮甲虫损害的冷杉树 (Abies sibirica). 遥感, 11(6), 文章编号 643.

[10] S. Hartling, V. Sagan, P. Sidike, M. Maimaitijiang, and J. Carron, “Urban tree species classification using a worldview-2/3 and LiDAR data fusion approach and deep learning,” Sensors, vol. 19, no. 6, Mar. 2019, Art. no. 1284.

中文翻译：[10] 哈特林, S., 萨根, V., 西迪克, P., 麦麦提江, M., & 卡隆, J. (2019). 使用 WorldView-2/3 和 LiDAR 数据融合方法和深度学习进行城市树种分类. 传感器, 19(6), 文章编号 1284.

[11] X. Feng, J. Han, X. Yao, and G. Cheng, “TCANet: Triple context-aware network for weakly supervised object detection in remote sensing images,” IEEE Trans. Geosci. Remote Sens., vol. 59, no. 8, pp. 6946–6955, Aug. 2021.

中文翻译：[11] 冯, X., 韩, J., 姚, X., & 程, G. (2021). TCANet: 用于遥感图像中弱监督目标检测的三重上下文感知网络. IEEE 地球科学与遥感杂志, 59(8), 6946-6955.

[12] Y. Li, Y. Zhang, X. Huang, and A. L. Yuille, “Deep networks under scene-level supervision for multi-class geospatial object detection from remote sensing images,” ISPRS J. Photogramm. Remote Sens., vol. 146, pp. 182–196, Dec. 2018.

中文翻译：[12] 李, Y., 张, Y., 黄, X., & 尤尔, A. L. (2018).
在场景级监督下的深度网络用于从遥感图像中检测多类地理空间目标. ISPRS 摄影测量与遥感杂志, 146, 182-196.

[13] D. Marcos, M. Volpi, B. Kellenberger, and D. Tuia, “Land cover mapping at very high resolution with rotation equivariant CNNs: Towards small yet accurate models,” ISPRS J. Photogramm. Remote Sens., vol. 145, pp. 96–107, Nov. 2018.

中文翻译：[13] 马科斯, D., 沃尔皮, M., 凯伦伯格, B., & 图亚, D. (2018). 使用旋转等变卷积神经网络进行非常高分辨率的土地覆盖制图：迈向小型但准确的模型. ISPRS 摄影测量与遥感杂志, 145, 96-107.

[14] P. Wei, D. Chai, T. Lin, C. Tang, M. Du, and J. Huang, “Large-Scale rice mapping under different years based on time-series sentinel-1 images using deep semantic segmentation model,” ISPRS J. Photogramm. Remote Sens., vol. 174, pp. 198–214, Apr. 2021.

中文翻译：[14] 魏, P., 蔡, D., 林, T., 唐, C., 杜, M., & 黄, J. (2021). 基于时间序列 Sentinel-1 图像使用深度语义分割模型进行不同年份的大规模水稻制图. ISPRS 摄影测量与遥感杂志, 174, 198-214.

[15] F. Mohammadimanesh, B. Salehi, M. Mahdianpari, E. Gill, and M. Molinier, “A new fully convolutional neural network for semantic segmentation of polarimetric SAR imagery in complex land cover ecosystem,” ISPRS J. Photogramm. Remote Sens., vol. 151, pp. 223–236, May 2019.

中文翻译：[15] 莫哈迪曼什, F., 萨利希, B., 马赫迪安帕里, M., 吉尔, E., & 莫利尼埃, M. (2019). 一种用于复杂土地覆盖生态系统极化 SAR 图像语义分割的新全卷积神经网络. ISPRS 摄影测量与遥感杂志, 151, 223-236.

[16] B. Zhou, A. Khosla, A. Lapedriza, A. Oliva, and A. Torralba, “Learning deep features for discriminative localization,” in Proc. Comput. Vis. Pattern Recognit., 2016, pp. 2921–2929.

中文翻译：[16] 周, B., 轲斯拉, A., 拉佩德里扎, A., 奥利瓦, A., & 托拉尔巴, A. (2016). 学习深度特征用于判别性定位. 在计算机视觉与模式识别会议论文集, 2921-2929.

[17] L. Bazzani, A. Bergamo, D. Anguelov, and L. Torresani, “Self-taught object localization with deep networks,” in Proc. IEEE Winter Conf. Appl. Comput. Vis., 2016, pp. 1–9.

中文翻译：[17] 巴扎尼, L., 伯加莫, A., 安吉洛夫, D., & 托雷萨尼, L. (2016). 使用深度网络进行自教目标定位. 在 IEEE 冬季应用计算机视觉会议论文集, 1-9.

[18] R. G. Cinbis, J. Verbeek, and C. Schmid, “Weakly supervised object localization with multi-fold multiple instance learning,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 39, no. 1, pp. 189–203, Jan. 2017.

中文翻译：[18] 辛比斯, R. G., 维尔贝克, J., & 施密德, C. (2017). 使用多折多实例学习进行弱监督目标定位. IEEE 模式分析与机器智能杂志, 39(1), 189-203.

[19] M. Oquab, L. Bottou, I. Laptev, and J. Sivic, “Learning and transferring mid-level image representations using convolutional neural networks,” in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit., 2014, pp. 1717–1724.

中文翻译：[19] 奥卡布, M., 博图, L., 拉普特夫, I., & 西维奇, J. (2014). 使用卷积神经网络学习和转移中层图像表示. 在 IEEE/CVF 计算机视觉与模式识别会议论文集, 1717-1724.

[20] M. Oquab, L. Bottou, I. Laptev, and J. Sivic, “Is object localization for free? - Weakly-supervised learning with convolutional neural networks,” in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit., 2015, pp. 685–694.

中文翻译：[20] 奥卡布, M., 博图, L., 拉普特夫, I., & 西维奇, J. (2015). 目标定位是免费的吗？- 使用卷积神经网络进行弱监督学习. 在 IEEE/CVF 计算机视觉与模式识别会议论文集, 685-694.

[21] K. Simonyan, A. Vedaldi, and A. Zisserman, “Deep inside convolutional networks: Visualising image classification models and saliency maps,” in Proc. Workshop at Int. Conf. Learn. Representations, 2014.

中文翻译：[21] 西蒙尼扬, K., 维达利, A., & 西斯曼, A. (2014). 卷积网络的深层：可视化图像分类模型和显著性图. 在国际学习表示会议研讨会论文集.

[22] M. Menikdiwela, C. Nguyen, H. Li, and M. Shaw, “CNN-based small object detection and visualization with feature activation mapping,” in Proc. Int. Conf. Image Vis. Comput. New Zealand, 2017, pp. 1–5.

中文翻译：[22] 梅尼克迪韦拉, M., 阮, C., 李, H., & 肖, M. (2017). 基于 CNN 的小目标检测与特征激活映射可视化. 在新西兰图像视觉计算国际会议论文集, 1-5.

[23] A. Kolesnikov and C. H. Lampert, “Seed, expand and constrain: Three principles for weakly-supervised image segmentation,” in Proc. Eur. Conf. Comput. Vis., 2016, pp. 695–711.

中文翻译：[23] 科尔尼科夫, A., & 拉姆珀特, C. H. (2016). 种子、扩展和约束：弱监督图像分割的三个原则. 在欧洲计算机视觉会议论文集, 695-711.

[24] Z. Huang, X. Wang, J. Wang, W. Liu, and J. Wang, “Weakly-supervised semantic segmentation network with deep seeded region growing,” in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit., 2018, pp. 7014–7023.

中文翻译：[24] 黄, Z., 王, X., 王, J., 刘, W., & 王, J. (2018). 带有深度种子区域生长的弱监督语义分割网络. 在 IEEE/CVF 计算机视觉与模式识别会议论文集, 7014-7023.

[25] R. R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, and D. Batra, “Grad-CAM: Visual explanations from deep networks via gradient-based localization,” Int. J. Comput. Vis., vol. 128, no.
2, pp. 336–359, Feb. 2020.

中文翻译：[25] 塞尔瓦拉朱, R. R., 科格斯韦尔, M., 达斯, A., 维丹塔姆, R., 帕里克, D., & 巴特拉, D. (2020). Grad-CAM：通过基于梯度的定位从深度网络中获取视觉解释. 国际计算机视觉杂志, 128(2), 336-359.

[26] A. Chattopadhyay, A. Sarkar, P. Howlader, and V. N. Balasubramanian, “Grad-CAM++: Improved visual explanations for deep convolutional networks,” in Proc. IEEE Winter Conf. Appl. Comput. Vis., 2018, pp. 839–847.

中文翻译：[26] 聊天帕德雅, A., 萨卡尔, A., 哈乌勒德, P., & 巴拉苏布拉马尼安, V. N. (2018). Grad-CAM++：改进的深度卷积网络视觉解释. 在 IEEE 冬季应用计算机视觉会议论文集, 839-847.

[27] D. Omeiza, S. Speakman, C. Cintas, and K. Weldermariam, “Smooth grad cam++: An enhanced inference level visualization technique for deep convolutional neural network models,” CoRR, abs/1908.01224, 2019.

中文翻译：[27] 奥梅扎, D., 斯皮克曼, S., 辛塔斯, C., & 韦尔德马里安, K. (2019). Smooth Grad CAM++：一种增强的深度卷积神经网络模型推理级可视化技术. 计算机研究评论, abs/1908.01224.

[28] H. Wang et al., “Score-CAM: Score-weighted visual explanations for convolutional neural networks,” in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit. Workshops, 2020, pp. 24–25.

中文翻译：[28] 王, H. 等. (2020). Score-CAM：用于卷积神经网络的分数加权视觉解释. 在 IEEE/CVF 计算机视觉与模式识别会议研讨会论文集, 24-25.

[29] B. Vasu, F. U. Rahman, and A. Savakis, “Aerial-CAM: Salient structures and textures in network class activation maps of aerial imagery,” in Proc. IEEE 13th Image, Video, Multidimensional Signal Process. Workshop, 2018, pp. 1–5.

中文翻译：[29] 瓦苏, B., 拉赫曼, F. U., & 萨瓦基斯, A. (2018). Aerial-CAM：航拍图像网络类激活映射中的显著结构和纹理. 在 IEEE 第 13 届图像、视频和多维信号处理研讨会论文集, 1-5.

[30] R. Yang, X. Xu, Z. Xu, C. Ding, and F. Pu, “A class activation mapping guided adversarial training method for land-use classification and object detection,” in Proc. IEEE Int. Geosci. Remote Sens. Symp., 2019. pp. 9474–9477.

中文翻译：[30] 杨, R., 徐, X., 徐, Z., 丁, C., & 蒲, F. (2019). 一种用于土地利用分类和目标检测的类激活映射引导的对抗性训练方法. 在 IEEE 国际地球科学与遥感研讨会论文集, 9474-9477.

[31] J. Li, D. Lin, Y. Wang, G. Xu, and C. Ding, “Deep discriminative representation learning with attention map for scene classification,” Remote Sens., vol. 12, no. 9, 2020, Art. no. 1366.

中文翻译：[31] 李, J., 林, D., 王, Y., 徐, G., & 丁, C. (2020). 带有注意力图的深度判别表示学习用于场景分类. 遥感, 12(9), 文章编号 1366.

[32] W. Castro et al., “Deep learning applied to phenotyping of biomass in forages with UAV-based RGB imagery,” Sensors, vol. 20, no. 17, Aug. 2020, Art. no. 4802.

中文翻译：[32] 卡斯特罗, W. 等. (2020). 深度学习应用于基于无人机 RGB 图像的饲料生物质表型分析. 传感器, 20(17), 文章编号 4802.

[33] Y. Li, W. Chen, Y. Zhang, C. Tao, R. Xiao, and Y. Tan, “Accurate cloud detection in high-resolution remote sensing imagery by weakly supervised deep learning,” Remote Sens. Environ., vol. 250, pp. 112045, Dec. 2020.

中文翻译：[33] 李, Y., 陈, W., 张, Y., 陶, C., 肖, R., & 谭, Y. (2020). 通过弱监督深度学习在高分辨率遥感图像中进行准确的云检测. 遥感环境, 250, 112045.

[34] J. L. Abitbol and M. Karsai, “Interpretable socioeconomic status inference from aerial imagery through urban patterns,” Nature Mach. Intell., vol. 2, no. 11, pp. 684–692, Nov. 2020.

中文翻译：[34] 阿比特博尔, J. L., & 卡萨伊, M. (2020). 通过城市模式从航拍图像中推断可解释的社会经济地位. 自然机器智能, 2(11), 684-692.

[35] K. Fu, W. Dai, Y. Zhang, Z. Wang, M. Yan, and X. Sun, “MultiCAM: Multiple class activation mapping for aircraft recognition in remote sensing images,” Remote Sens., vol. 11, no. 5, Mar. 2019, Art. no. 544.

中文翻译：[35] 傅, K., 戴, W., 张, Y., 王, Z., 严, M., & 孙, X. (2019). MultiCAM：用于遥感图像中飞机识别的多类激活映射. 遥感, 11(5), 文章编号 544.

[36] J. Chen, F. He, Y. Zhang, G. Sun, and M. Deng, “SPMF-Net: Weakly supervised building segmentation by combining superpixel pooling and multi-scale feature fusion,” Remote Sens., vol. 12, no. 6, Mar. 2020, Art. no. 1049.

中文翻译：[36] 陈, J., 何, F., 张, Y., 孙, G., & 邓, M. (2020). SPMF-Net：通过结合超像素池化和多尺度特征融合进行弱监督建筑分割. 遥感, 12(6), 文章编号 1049.

[37] Z. Li, X. Zhang, P. Xiao, and Z. Zheng, “On the effectiveness of weakly supervised semantic segmentation for building extraction from high-resolution remote sensing imagery,” IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 14, pp. 3266–3281, Mar. 2021, doi:10.1109/JSTARS.2021.3063788.

中文翻译：[37] 李, Z., 张, X., 肖, P., & 郑, Z. (2021). 弱监督语义分割在高分辨率遥感图像中提取建筑的有效性. IEEE 应用地球观测与遥感选题杂志, 14, 3266-3281.

[38] E. Kilic and S. Ozturk, “An accurate car counting in aerial images based on convolutional neural networks,”
in Proc. J. Ambient Intell. Humanized Comput., 2021, Art. no. 237746064.

中文翻译：[38] 基利克, E., & 奥兹图尔克, S. (2021). 基于卷积神经网络的航拍图像中准确的汽车计数. 在环境智能与人性化计算杂志论文集, 文章编号 237746064.

[39] S. Wang, W. Chen, S. M. Xie, G. Azzari, and D. B. Lobell, “Weakly supervised deep learning for segmentation of remote sensing imagery,” Remote Sens., vol. 12, no. 2, Jan. 2020, Art. no. 207.

中文翻译：[39] 王, S., 陈, W., 谢, S. M., 阿扎里, G., & 洛贝尔, D. B. (2020). 弱监督深度学习用于遥感图像分割. 遥感, 12(2), 文章编号 207.

[40] D. Smilkov, N. Thorat, B. Kim, F. Viégas, and M. Wattenberg, “Smooth Grad: Removing noise by adding noise,” 2017, arXiv:1706.03825.

中文翻译：[40] 斯米尔科夫, D., 托拉特, N., 金, B., 维埃加斯, F., & 瓦滕伯格, M. (2017). Smooth Grad：通过添加噪声去除噪声. arXiv:1706.03825.

[41] J. Lafferty, A. McCallum, and F. Pereira, “Conditional random fields: Probabilistic models for segmenting and labeling sequence data,” in Proc. 18th Int. Conf. Mach. Learn., 2001, pp. 282–289.

中文翻译：[41] 拉弗蒂, J., 麦考勒姆, A., & 佩雷拉, F. (2001). 条件随机场：用于序列数据分割和标注的概率模型. 在第 18 届国际机器学习会议论文集, 282-289.

[42] G. Cheng and J. Han, “A survey on object detection in optical remote sensing images,” ISPRS J. Photogramm. Remote Sens., vol. 117, pp. 11–28, Jul. 2016.

中文翻译：[42] 程, G., & 韩, J. (2016). 光学遥感图像中目标检测的综述. ISPRS 摄影测量与遥感杂志, 117, 11-28.

[43] G. Cheng, P. Zhou, and J. Han, “Learning rotation-invariant convolutional neural networks for object detection in VHR optical remote sensing images,” IEEE Trans. Geosci. Remote Sens., vol. 54, no. 12, pp. 7405–7415, Dec. 2016.

中文翻译：[43] 程, G., 周, P., & 韩, J. (2016). 学习旋转不变卷积神经网络用于 VHR 光学遥感图像中的目标检测. IEEE 地球科学与遥感杂志, 54(12), 7405-7415.

[44] G. Cheng, J. Han, P. Zhou, and L. Guo, “Multi-Class geospatial object detection and geographic image classification based on collection of part detectors,” ISPRS J. Photogramm. Remote Sens., vol. 98, pp. 119–132, Dec. 2014.

中文翻译：[44] 程, G., 韩, J., 周, P., & 郭, L. (2014). 基于部分检测器集合的多类地理空间目标检测和地理图像分类. ISPRS 摄影测量与遥感杂志, 98, 119-132.

[45] K. Li, G. Wan, G. Cheng, L. Meng, and J. Han, “Object detection in optical remote sensing images: A survey and a new benchmark,” ISPRS J. Photogramm. Remote Sens., vol. 159, pp. 296–307, Jan. 2020.

中文翻译：[45] 李, K., 万, G., 程, G., 孟, L., & 韩, J. (2020). 光学遥感图像中的目标检测：综述和新基准. ISPRS 摄影测量与遥感杂志, 159, 296-307.

[46] E. Maggiori, Y. Tarabalka, G. Charpiat, and P. Alliez, “Can semantic labeling methods generalize to any city? The inria aerial image labeling benchmark,” in Proc. IEEE Int. Geosci. Remote Sens. Symp., 2017, pp. 3226–3229.

中文翻译：[46] 马吉奥里, E., 塔拉巴尔卡, Y., 查皮亚特, G., & 阿利埃, P. (2017). 语义标注方法能否泛化到任何城市？INRIA 航拍图像标注基准. 在 IEEE 国际地球科学与遥感研讨会论文集, 3226-3229.

[47] A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” Commun. ACM, vol. 60, no. 6, pp. 84–90, May 2017.

中文翻译：[47] 克里希维斯基, A., 萨特克弗, I., & 辛顿, G. E. (2017). 使用深度卷积神经网络进行 ImageNet 分类. ACM 通讯, 60(6), 84-90.

[48] K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” in Proc. 3rd Int. Conf. Learn. Represent., San Diego, CA, USA, May 2015, pp. 1–14.

中文翻译：[48] 西蒙尼扬, K., & 西斯曼, A. (2015). 用于大规模图像识别的非常深的卷积网络. 在第三届国际学习表示会议论文集, 1-14.

[49] K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit., Jun. 2016, pp. 770–778.

中文翻译：[49] 何, K., 张, X., 任, S., & 孙, J. (2016). 用于图像识别的深度残差学习. 在 IEEE/CVF 计算机视觉与模式识别会议论文集, 770-778.

[50] M. Tan and Q. V. Le, “EfficientNet: Rethinking model scaling for convolutional neural networks,” in Proc. Int. Conf. Mach. Learn., 2019, pp. 6105–6114.

中文翻译：[50] 谭, M., & 莱, Q. V. (2019). EfficientNet：重新思考卷积神经网络的模型缩放. 在国际机器学习会议论文集, 6105-6114.

[51] J. Wang et al., “Deep high-resolution representation learning for visual recognition,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 43, no. 10, pp. 3349–3364, Oct. 2020.

中文翻译：[51] 王, J. 等. (2020). 用于视觉识别的深度高分辨率表示学习. IEEE 模式分析与机器智能杂志, 43(10), 3349-3364.

[52] J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, “Imagenet: A large-scale hierarchical image database,” in Proc. IEEE Int. Conf. Comput. Vis. Pattern Recognit., 2009, pp. 248–255.

中文翻译：[52] 丁, J., 董, W., 索切尔, R., 李, L.-J., 李, K., & 李, L. Fei-Fei (2009). ImageNet：一个大规模的层次化图像数据库. 在 IEEE 国际计算机视觉与模式识别会议论文集, 248-255.