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科学网—深度学习在目标视觉检测中的应用进展与展望(9)

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[19] y. lecun and y. bengio. convolutional networks for images, speech, and time-series. in m. a. arbib, editor, the handbook of brain theory and neural networks. mit press, 1995。[26] krizhevsky, a. parallelizing convolutional neural networks. in tutorial of ieee conference on computer vision and pattern recognition (cvpr 2014). (columbus, ohio, usa, june 23-28, 2014). 2014.。[1]shi, wenzhe, et al. “real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network.” proceedings of the ieee conference on computer vision and pattern recognition. 2016.。

64 Szegedy C, Liu W, Jia Y Q, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A. Going deeper with convolutions. In: Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Boston, Massachusetts, USA: IEEE, 2015. 1-9

65 Szegedy C, Ioffe S, Vanhoucke V, Alemi A. Inception-v4, Inception-ResNet and the impact of residual connections on learning. arXiv:1602.07261, 2016.

[14] s. ioffe and c. szegedy.batch normalization: accelerating deep network training by reducing internal covariate shift. in icml, 2015.。《batch normalization:accelerating deep network training by reducing internal covariate shift》,serger ioffe、christian szegedy。a dns resolver is a dns server that can perform recursion to resolve names for domains for which that dns server is not authoritative. for example, you might have a dns server on your internal network that''''s authoritative for your internal network domain, internalcorp.com. when a client on your network uses that dns server to resolve the name techrepublic.com, that dns server performs recursion by querying other dns servers to get the answer.。

67 Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z. Rethinking the inception architecture for computer vision. arXiv:1512.00567, 2015.

68 He K, Zhang X, Ren S, Sun J. Spatial pyramid pooling in deep convolutional networks for visual recognition. In: Proceedings of the 2014 European Conference on Computer Vision (ECCV). Zurich, Switzerland: Springer, 2014. 346-361

69 Bell S, Lawrence Zitnick C, Bala K, Girshick R. Inside-outside net: detecting objects in context with skip pooling and recurrent neural networks. In: Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas, NV, USA:IEEE, 2016. 2874-2883

photographs alignment and high dynamic range image composition based on varying exposure levels. conference proceedings of icat 2006, 16th international conference on artificial reality and telexistence, nov.2006, hangzhou, china. lecture notes in computer science。10.cairong zou, chengwei huang, dong han, li zhao. detecting practical speech emotion in a cognitive task, computer communications and networks , 2011 proceedings of 20th international conference on, maui, hi, usa, 2011。 mersch eds conference proceedings of the philosophy of computer games 2008, kirkpatrick, graeme 2004 critical technology: a social theory of personal computing, aldershot: ashgate。

71 Shrivastava A, Gupta A, Girshick R. Training region-based object detectors with online hard example mining. In: Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas, NV, USA: IEEE, 2016. 761-769

72 Sung K K. Learning and Example Selection for Object and Pattern Detection[Ph.D. dissertation], Massachusetts Institute of Technology, USA, 1996.

[2] n. dalal, b. triggs,histograms of oriented gradients for human detection, proc. ieee conf. computer vision and pattern recognition, 2005.。[1] dalal, n. and b. triggs. "histograms oforiented gradients for human detection",ieee computer society conference on computer vision and pattern recognition,vol. 1 (june 2005), pp. 886–893.。[1]shi, wenzhe, et al. “real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network.” proceedings of the ieee conference on computer vision and pattern recognition. 2016.。

74 Dai J F, Li Y, He K M, Sun J. R-FCN:object detection via region-based fully convolutional networks. In: Proceedings of the 2016 Advances in Neural Information Processing Systems (NIPS). Barcelona, Spain: MIT Press, 2016. 379-387

neuralnetwork toolbox tutorial: a tutorial on how to constructcustom neural networks starting from an empty network object.。[19] y. lecun and y. bengio. convolutional networks for images, speech, and time-series. in m. a. arbib, editor, the handbook of brain theory and neural networks. mit press, 1995。mao, junhua et al. “deep captioning with multimodal recurrent neural networks (m-rnn).” arxiv preprint arxiv:1412.6632 (2014).。

76 Shang W L, Sohn K, Almeida D, Lee H. Understanding and improving convolutional neural networks via concatenated rectified linear units. In: Proceedings of the 33rd International Conference on Machine Learning (ICML). New York, USA: IEEE, 2016. 2217-2225

[19] y. lecun and y. bengio. convolutional networks for images, speech, and time-series. in m. a. arbib, editor, the handbook of brain theory and neural networks. mit press, 1995。[26] krizhevsky, a. parallelizing convolutional neural networks. in tutorial of ieee conference on computer vision and pattern recognition (cvpr 2014). (columbus, ohio, usa, june 23-28, 2014). 2014.。title:faster r-cnn: towards real-time object detection with region proposal networksabstract: state-of-the-art object detection networks depend on region proposalalgorithms to hypothesize object locations。