深度学习发展形势下计算机视觉教学内容革新

摘要/Abstract

摘要： 近年来，深度学习理论与应用技术获得了快速发展，其在计算机视觉中的应用日益广泛和深入，在诸多计算机视觉任务中取得了受人注目的成绩，给现有的计算机视觉教学内容带来了不容忽视的影响。在总结深度学习理论在计算机视觉各方面应用现状的基础上，提出计算机视觉教学内容的适应性革新，将深度学习理论融入计算机视觉教学中，更好地体现相关学科理论发展对计算机视觉教学内容变革的促进作用。

关键词: 深度学习, 计算机视觉, 教学内容

Abstract: In recent years, deep learning theory and application technology have achieved rapid development, and its application in computer vision has been increasingly extensive and in-depth. It has made remarkable achievements in many computer vision tasks, and brought significant impacts on the existing computer vision teaching content. On the basis of summarizing the application status of deep learning theory in various aspects of computer vision, this paper proposes adaptive innovation of computer vision teaching content, and integrates deep learning theory into computer vision teaching, so as to better reflect the promotion effects of theoretical development of relevant disciplines on the reform of computer vision teaching content.

Key words: deep learning, computer vision, teaching content

中图分类号:

TP391

陈川, 陈柘, 丁双惠. 深度学习发展形势下计算机视觉教学内容革新[J]. 计算机与现代化, 2020, 0(06): 107-.

CHEN Chuan, CHEN Zhe, DING Shuang-hui. Innovation of Computer Vision Teaching Contents Under Development of Deep Learning[J]. Computer and Modernization, 2020, 0(06): 107-.

参考文献

［1］ DENG J, DONG W, SOCHER R, et al. ImageNet: A large-scale hierarchical image database［C］// Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. 2009:248-255.
［2］ KRIZHEVSKY A, SUTSKEVER I, HINTON G E. ImageNet classification with deep convolutional neural networks［C］// Proceedings of International Conference on Neural Information Processing Systems. 2012:1097-1105.
［3］ SIMONYAN K, ZISSERMAN A. Very deep convolutional networks for large-scale image recognition［C］// IEEE International Conference on Learning Representations. 2015:1264-1278.
［4］ SZEGEDY C, LIU W, JIA Y, et al. Going deeper with convolutions［C］// Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. 2015:1-9.
［5］ HE K, ZHANG X, REN S, et al. Deep residual learning for image recognition［C］// Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. 2016:770-778.
［6］ GIRSHICK R, DONAHUE J, DARRELL T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation［C］// Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. 2014:580-587.
［7］ HE K, ZHANG X, REN S, et al. Spatial pyramid pooling in deep convolutional networks for visual recognition［C］// Proceedings of European Conference on Computer Vision. 2014:346-361.
［8］ GIRSHICK R. Fast R-CNN［C］// Proceedings of IEEE International Conference on Computer Vision. 2015:1440-1448.
［9］ REN S, HE K, GIRSHICK R, et al. Faster R-CNN: Towards real-time object detection with region proposal networks［C］// Proceedings of the Advances in Neural Information Processing Systems. 2015:91-99.
［10］LIU W, ANGUELOV D, ERHAN D, et al. SSD: Single shot multibox detector［C］// Proceedings of European Conference on Computer Vision. 2016:21-37.
［11］REDMON J, DIVVALA S, GIRSHICK R, et al. You only look once: Unified, real-time object detection［C］// Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. 2016:779-788.
［12］LIN T Y, DOLLR P, GIRSHICK R B, et al. Feature pyramid networks for object detection［C］// Proceedings of IEEE Conference on Computer Vision and Image Processing. 2017:936-944.
［13］ZHANG S, WEN L, BIAN X, et al. Singleshot refinement neural network for object detection［C］// Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. 2018:4203-4212.
［14］EVERINGHAM M, VAN GOOL L, WILLIAMS C K I, et al. The Pascal Visual Object Classes(VOC) challenge［J］. International Journal of Computer Vision, 2010,88(2):303-338.
［15］EVERINGHAM M, ESLAMI S A, VAN GOOL L, et al. The Pascal visual object classes challenge: A retrospective［J］. International Journal of Computer Vision, 2015,111(1):98-136.
［16］LIN T Y, MAIRE M, BELONGIE S, et al. Microsoft COCO: Common objects in context［C］// Proceedings of European Conference on Computer Vision. 2014:740-755.
［17］LIN T Y, GOYAL P, GIRSHICK R, et al. Focal loss for dense object detection［C］// Proceedings of IEEE Conference on Computer Vision. 2017:2999-3007.
［18］LI Y, CHEN Y, WANG N, et al. Scale-aware trident networks for object detection［J］. Computer Vision and Pattern Recognition, arXiv:1901.01892, 2019.
［19］田萱,王亮,丁琪. 基于深度学习的图像语义分割方法综述［J］. 软件学报, 2019,30(2):440-468.
［20］HARIHARAN B, ARBELEZ P, GIRSHICK R, et al. Simultaneous detection and segmentation［C］// Proceedings of the European Conference on Computer Vision. 2014:297-312.
［21］PINHEIRO P O, COLLOBERT R, DOLLAR P. Learning to segment object candidates［C］// Proceedings of the Advances in Neural Information Processing Systems. 2015:1990-1998.
［22］LIU S, QI X, SHI J, et al. Multi-scale patch aggregation(MPA) for simultaneous detection and segmentation［C］// Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. 2016:3141-3149.
［23］PINHEIRO P O, LIN T Y, COLLOBERT R, et al. Learning to refine object segments［C］// Proceedings of the European Conference on Computer Vision. 2016:75-91.
［24］ZAGORUYKO S, LERER A, LIN T Y, et al. A multipath network for object detection［J］. Computer Vision and Pattern Recognition, arXiv:1604.02135, 2016.
［25］HE K, GKIOXARI G, DOLLR P, et al. Mask R-CNN［C］// Proceedings of IEEE Conference on Computer Vision. 2017:2980-2988.
［26］SHELHAMER E, LONG J, DARRELL T. Fully convolutional networks for semantic segmentation［J］. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2014,39(4):640-651.
［27］NOH H, HONG S, HAN B. Learning deconvolution network for semantic segmentation［C］// Proceedings of IEEE Conference on Computer Vision. 2015:1520-1528.
［28］LIU Z, LI X, LUO P, et al. Semantic image segmentation via deep parsing network［C］// Proceedings of IEEE Conference on Computer Vision. 2015:1377-1385.
［29］CHEN L C, PAPANDREOU G, KOKKINOS I, et al. Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs［J］. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2018,40(4):834-848.
［30］ZHAO H, SHI J, QI X, et al. Pyramid scene parsing network［C］// Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. 2017:6230-6239.
［31］CHEN L C, PAPANDREOU G, SCHROFF F, et al. Rethinking atrous convolution for semantic image segmentation［J］. Computer Vision and Pattern Recognition, arXiv:1706.05587, 2017.
［32］WANG N, YEUNG D Y. Learning a deep compact image representation for visual tracking［C］// Proceedings of the 26th Annual Conference on Neural Information Processing Systems. 2013,1:809-817.
［33］WANG N, LI S, GUPTA A, et al. Transferring rich feature hierarchies for robust visual tracking［J］. Computer Vision and Pattern Recognition, arXiv:1501.04587, 2015.
［34］WANG L, OUYANG W, WANG X, et al. Visual tracking with fully convolutional networks［C］// Proceedings of IEEE International Conference on Computer Vision. 2015:3119-3127.
［35］NAM H, HAN B. Learning multi-domain convolutional neural networks for visual tracking［C］// Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. 2016:4293-4302.
［36］EIGEN D, PUHRSCH C, FERGUS R. Depth map prediction from a single image using a multi-scale deep network［C］// International Conference on Neural Information Processing Systems. 2014,2:2366-2374.
［37］CHOY C B, XU D, GWAK J, et al. 3DR2N2: A unified approach for single and multi-view 3D object reconstruction［C］// Proceedings of European Conference on Computer Vision. 2016:628-644.
［38］TATARCHENKO M, DOSOVITSKIY A, BROX T. Octree generating networks: Efficient convolutional architectures for high resolution 3D outputs［C］// Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. 2017:2088-2096.
［39］MATURANA D, VOXNE S. VoxNet: A 3D convolutional neural network for real-time object recognition［C］// Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems. 2015:922-928.
［40］WU Z, SONG S, KHOSLA A, et al. 3D shapenets: A deep representation for volumetric shapes［C］// Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. 2015:1912-1920.
［41］RICHTER S R, ROTH S. Matryoshka networks: Predicting 3D geometry via nested shape layers［C］// Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. 2018:1936-1944.
［42］李阳,陈秀万,王媛,等. 基于深度学习的单目图像深度估计的研究进展［J］. 激光与光电子学进展，2019,56(19):1-17.

[1]	赵晨阳, 薛涛, 刘俊华. 基于改进Stable Diffusion的时尚服饰图案生成[J]. 计算机与现代化, 2024, 0(12): 15-23.
[2]	祁贤, 刘大铭, 常佳鑫. 基于改进自注意力机制的多视图三维重建[J]. 计算机与现代化, 2024, 0(11): 106-112.
[3]	陈凯1, 李宜汀1, 2, 全华凤1 . 基于改进YOLOv8的河道废弃瓶检测方法[J]. 计算机与现代化, 2024, 0(11): 113-120.
[4]	杨骏1, 胡为1, 朱文福2. 基于改进MobileNetV3的视觉SLAM回环检测算法[J]. 计算机与现代化, 2024, 0(10): 21-26.
[5]	王莹莹, 郝潇. 基于Res2Net和递归门控卷积的细粒度图像分类[J]. 计算机与现代化, 2024, 0(10): 74-79.
[6]	史星宇1, 李强2, 庄莉3, 梁懿3, 王秋琳3, 陈锴3, 伍臣周3, 常胜1. 一种面向工业部署的目标检测模型蒸馏技术[J]. 计算机与现代化, 2024, 0(10): 93-99.
[7]	张泽1, 张建权2, 3, 周国鹏2, 3. 基于改进YOLOv8s的摄像头模组缺陷检测[J]. 计算机与现代化, 2024, 0(09): 107-113.
[8]	程亚子1, 雷亮1, 2, 陈瀚1, 赵毅然1. 基于转置注意力的多尺度深度融合单目深度估计[J]. 计算机与现代化, 2024, 0(09): 121-126.
[9]	程萌, 李浩. 改进YOLOv5s的落叶树鸟巢检测方法[J]. 计算机与现代化, 2024, 0(08): 24-29.
[10]	王梦溪, 李峻. 老年人跌倒检测技术研究综述[J]. 计算机与现代化, 2024, 0(08): 30-36.
[11]	时现伟1, 范鑫2. 基于轻量化的视频帧场景语义分割方法[J]. 计算机与现代化, 2024, 0(08): 49-53.
[12]	魏嘉焜, 王家润. 手势识别与交互综述[J]. 计算机与现代化, 2024, 0(08): 67-76.
[13]	徐新爱, 李钢. 基于DCGAN的课堂表情图像生成方法[J]. 计算机与现代化, 2024, 0(08): 88-91.
[14]	高帅鹏, 王怡凡. 基于图像的群体情绪识别综述[J]. 计算机与现代化, 2024, 0(08): 98-107.
[15]	黄文栋, 王怡凡. 基于模态类别的多模态信息处理与融合综述[J]. 计算机与现代化, 2024, 0(07): 47-62.