科学网—深度学习在目标视觉检测中的应用进展与展望(10)

78 Redmon J, Divvala S, Girshick R, Farhadi A. You only look once:unified, real-time object detection. In: Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas, NV, USA: IEEE, 2016. 779-788

79 Najibi M, Rastegari M, Davis L S. G-CNN:an iterative grid based object detector. In: Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas, NV, USA: IEEE, 2016. 2369-2377

80 Liu W, Anguelov D, Erhan D, Szegedy C, Reed S E, Fu C Y, Berg A C. SSD: single shot multibox detector. In: Proceedings of the 14th European Conference on Computer Vision (ECCV). Amsterdam, Netherlands:Springer, 2016. 21-37

81 Redmon J, Farhadi A. YOLO9000: better, faster, stronger. arXiv:1612.08242, 2016.

[2] n. dalal, b. triggs,histograms of oriented gradients for human detection, proc. ieee conf. computer vision and pattern recognition, 2005.。[4]doupé a， cova m， vigna g. why johnny can’t pentest： an analysis of black-box web vulnerability scanners[c]// proceedings of the 7th international conference of detection of intrusions and malware， and vulnerability assessment. germany： springer berlin heidelberg， 2010： 111-131.。[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.。

83 Xiang Y, Mottaghi R, Savarese S. Beyond PASCAL:a benchmark for 3d object detection in the wild. In: Proceedings of the 2014 IEEE Winter Conference on Applications of Computer Vision (WACV). Steamboat Springs, Colorado, USA: IEEE, 2014. 75-82

第一，安装amazon app，在美国amazon的下载地址：https://www.amazon.com/gp/feature.html。xamarin test cloud (https://xamarin.com/test-cloud/)testdroid ()sauce labs (https://saucelabs.com/mobile/)google cloud test cloud (https://developers.google.com/cloud-test-lab/)aws device farm (https://aws.amazon.com/device-farm/)。network forum february 12, 2017-hui bai house pastoral sofa cushion fashion slip https://detail.tmall.com/item.htm for only 1.9 yuan。

oracle parallel query (opq) - parallel query is adivide-and conquer approach whereby symmetricmultiprocessing (smp) and massively parallel processors(mpp) can get super-fast response time for large-tablefull-table scans。3．shi wf，zhang z，peng l，zhang yz，liu b，zhu cd*．2007．proteotyping：a new approach studying influenza virus evolution at the protein level．virologica sinica．22（5）：405-411．。parallel ldpc decoding on gpus using a stream-based computing approach。

( 王坤峰, 苟超, 王飞跃. 平行视觉:基于ACP的智能视觉计算方法. 自动化学报, 2016, 42(10): 1490-1500.)

86 Wang K F, Gou C, Zheng N N, Rehg J M, Wang F Y. Parallel vision for perception and understanding of complex scenes: methods, framework, and perspectives. Artificial Intelligence Review[Online], available:https://link.springer.com/article/10.1007/s10462-017-9569-z, July 18, 2017

87 Wang Fei-Yue. Parallel system methods for management and control of complex systems. Control and Decision, 2004, 19(5): 485-489, 514.

( 王飞跃. 平行系统方法与复杂系统的管理和控制. 控制与决策, 2004, 19(5): 485-489, 514.)

2. requirements and concepts for future automotive electronic architectures from the view of integrated safety。14.wang hanning*, xu weixiang, jia chaolong，research on intelligent transportation cloud metadata replica technology based on paxos ，journal of beijing institute of technology(english edition), 21(suppl.2), pp:61-68. 2012 (ei)。6. the main contents of the course include theories and concepts of supply chain management, strategic alignment in supply chain operations, forecasting of demand in a supply chain, aggregate planning, demand and supply planning, inventory and transportation management strategy, information technology in supply chain management and supply chain coordination.。

89 Wang Fei-Yue. Parallel control:a method for data-driven and computational control. Acta Automatica Sinica, 2013, 39(4): 293-302.

( 王飞跃. 平行控制:数据驱动的计算控制方法. 自动化学报, 2013, 39(4): 293-302.)

90 Peng X C, Sun B C, Ali K, Saenko K. Learning deep object detectors from 3D models. In: Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV). Santiago, Chile: IEEE, 2015. 1278-1286

91 Johnson-Roberson M, Barto C, Mehta R, Sridhar S N, Rosaen K, Vasudevan R. Driving in the matrix: can virtual worlds replace human-generated annotations for real world tasks? arXiv: 1610.01983, 2016.

92 Pan S J, Yang Q. A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering, 2010, 22(10): 1345-1359.

93 Taylor M E, Stone P. Transfer learning for reinforcement learning domains: a survey. The Journal of Machine Learning Research, 2009, 10: 1633-1685.

后记：本文于2017年8月发表于《自动化学报》第43卷第8期

深度学习在目标视觉检测中的应用进展与展望.pdf