Laser & Optoelectronics Progress, Volume. 55, Issue 2, 021005(2018)

Deep Convolutional Neural Network Based on Two-Stream Convolutional Unit

Congcong Hou1、*, Yuqing He1, Xiaoheng Jiang1, and Jing Pan1
Author Affiliations
  • 1 School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China
  • 1 School of Electronic Engineering, Tianjin University of Technology and Education, Tianjin 300222, China
  • show less
    References(26)

    [4] Ye G L, Sun S Y, Gao K J et al. Nighttime pedestrian detection based on faster region convolution neural network[J]. Laser & Optoelectronics Progress, 54, 081003(2017).

    [5] Mao N, Yang D D, Yang F C et al. Adaptive object tracking based on hierarchical convolution features[J]. Laser& Optoelectronics Progress, 53, 121502(2016).

    [6] Gao L, Wang J F, Fan Y et al. Robust visual tracking based on convolutional neural networks and conformal predictor[J]. Acta Optica Sinica, 37, 0815003(2017).

    [7] Xu L, Zhao H T, Sun S Y et al. Monocular infrared image depth estimation based on deep convolutional neural networks[J]. Acta Optica Sinica, 36, 0715002(2016).

    [9] Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks. [C]// Proceedings of Advances in Neural Information Processing Systems(NIPS), 1106-1114(2012).

    [10] Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition[C]. ICLR, 1-14(2015).

    [11] Szegedy C, Liu W, Jia Y et al. Going deeper with convolutions. [C]// Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition(CVPR), 1-9(2015).

    [12] He K, Zhang X, Ren S et al. Deep residual learning for image recognition. [C]// Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), 770-778(2016).

    [13] Lin M, Chen Q[J]. Yan S. Network in network. arXiv preprint arXiv, 4400, 2013(1312).

    [15] Krizhevsky A, Hinton G. Learning multiple layers of features from tiny images[D]. Toronto: University of Toronto(2009).

    [16] Ioffe S, Szegedy C. Batch normalization: accelerating deep network training by reducing internal covariate shift[C]. International Conference on Machine Learning, 448-456(2015).

    [18] Goodfellow I J, Warde-Farley D, Mirza M et al. Maxout networks[C]. International Conference on Machine Learning, 13719-1327(2013).

    [21] Romero A, Ballas N, Kahou S E et al. Fitnets: hints for thin deep nets[C]. ICLR, 1-13(2015).

    [22] Vedaldi A, Lenc K. MatConvNet: convolutional neural networks for matlab. [C]// Proceedings of ACM Conference on Multimedia Conference, 689-692(2015).

    [23] Srivastava R K, Greff K, Schmidhuber J. Training very deep networks[C]. Advances in Neural Information Processing Systems(NIPS), 2377-2385(2015).

    [24] Liang M, Hu X. Recurrent convolutional neural network for object recognition[C]. Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition(CVPR), 3367-3375(2015).

    [25] Agostinelli F, Hoffman M, Sadowski P et al. Learning activation functions to improve deep neural networks[C]. ICLR, 1-9(2015).

    [26] Lee C Y, Xie S, Gallagher P et al. Deeply-supervised nets[C]. Artificial Intelligence and Statistics, 562-570(2015).

    CLP Journals

    [1] Hanqing Sun, Yanwei Pang. An Neural Network Framework of Self-Learning Uncertainty[J]. Acta Optica Sinica, 2018, 38(6): 0620002

    Tools

    Get Citation

    Copy Citation Text

    Congcong Hou, Yuqing He, Xiaoheng Jiang, Jing Pan. Deep Convolutional Neural Network Based on Two-Stream Convolutional Unit[J]. Laser & Optoelectronics Progress, 2018, 55(2): 021005

    Download Citation

    EndNote(RIS)BibTexPlain Text
    Save article for my favorites
    Paper Information

    Category: Image processing

    Received: Aug. 2, 2017

    Accepted: --

    Published Online: Sep. 10, 2018

    The Author Email: Congcong Hou (houcc@tju.edu.cn)

    DOI:10.3788/LOP55.021005

    Topics