Innovation of Computer Vision Teaching Contents Under Development of Deep Learning

Abstract

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

CLC Number:

TP391

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-.

References

［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.

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