참고문헌 (65)

1. M. Everingham et al., "The Pascal Visual Object Classes (VOC) Challenge - A Retrospective," IJCV, vol. 111, no. 1, 2014, pp. 98-136. 

2. O. Russakovsky et al., "ImageNet Large Scale Visual Recognition Challenge," IJCV, vol. 115, no. 3, 2015, pp. 211-252. 

   상세보기

3. Standford University and Princeton University, "ImageNet," Accessed 2018. http://image-net.org 

4. Standford University, "ILSVRC" Accessed 2018. http://image-net.org/challenges/LSVRC/ 

5. Princeton University, "WordNet," Accessed 2018. https://wordnet.princeton.edu/ 

6. M. Everingham et al., "PASCAL Visual Object Classes Challenge (VOC) 2005-2012," Accessed 2018. http://host.robots.ox.ac.uk/pascal/VOC/voc2012/index.html 

7. Kaggle, "ImageNet Challenge (Kaggle)," Accessed 2018. https://www.kaggle.com/image-net 

8. K. Yu, T. Zhang, and Y. Gong, "Nonlinear Learning Using Local Coordinate Coding," in NIPS, Vancouver, Canada, Dec. 2009, pp. 2223-2231. 

9. F. Perronnin and C. Dance, "Fisher Kernels on Visual Vocabularies for Image Categorization," CVPR, Minneapolis, MN, USA, June 17-22, 2007, pp. 1-8. 

10. M.A. Hearst et al., "Support Vector Machines," IEEE Intell. Syst. Their Applicat., vol. 13, no. 4, 1998, pp. 18-28. 

11. F. Perronnin et al., "Large-Scale Image Retrieval with Compressed Fisher Vectors," CVPR, Sna Francisco, CA, USA, June 13-18, 2010, pp. 3384-3391. 

12. J. Sanchez and F. Perronnin, "High-Dimensional Signature Compression for large-Scale Image Classification," CVPR, Colorado Springs, CO, USA, June 20-25, 2011, pp. 1665-1672. 

13. K.E. Van de Sande et al., "Segmentation as Selective Search for Object Recognition," ICCV, Barcelona, Spain, Nov. 6-13, 2011, pp. 1879-1886. 

14. J. Sivic and A. Zisserman, "Video Google: A Text Retrieval Approach to Object Matching in Videos," ICCV, Nice, France, Oct. 14-17, 2003, pp. 1470-1478. 

15. D.G. Lowe, "Distinctive Image Features from Scale-Invariant Keypoints," IJCV, vol. 60, no. 2, 2004, pp. 91-110. 

    상세보기

16. A. Krizhevsky, I. Sutskever, and G.E. Hinton, "Imagenet Classification with deep Convolutional Neural Networks," NIPS, Lake Tahoe, CA, USA, Dec. 3-8, 2012, pp. 1097-1105. 

17. Clarifai, "Clarifai Website," Accessed 2018. https://clarifai.com/ 

18. M.D. Zeiler, and R. Fergus, "Visualizing and Understanding Convolutional Networks," ECCV, Zurich, Swiss, Sept. 6-12, 2014, pp. 818-833. 

19. P. Sermanet et al., "Overfeat: Integrated Recognition, Localization and Detection Using Convolutional Networks," arXiv preprint arXiv: 1312.6229, 2013. 

20. M. Lin, Q. Chen, and S. Yan, "Network in Network," arXiv preprint arXiv: 1312.4400, 2013. 

21. K. Simonyan and A. Zisserman, "Very Deep Convolutional Networks for Large-Scale Image Recognition," arXiv preprint arXiv:1409.1556, 2014. 

22. C. Szegedy et al., "Going Deeper with Convolutions", CVPR, Boston, MA, USA, June 8-10, 2015. 

23. C. Szegedy et al., "Rethinking the Inception Architecture for Computer Vision," CVPR, Las Vegas, NV, USA, June 26-July 1, 2016, pp. 2818-2826 

24. C. Szegedy et al., "Inception-v4, Inception-Resnet and the Impact of Residual Connections on Learning," AAAI, San Francisco, CA, USA, Feb. 4-9, 2017, pp. 4278-4284. 

25. K. He et al., "Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition," IEEE Trans. Pattern Anal Mach. Intell., vol. 37, no. 9, 2015, pp. 1904-1916. 

    상세보기

26. R. Girshick et al., "Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation," CVPR, Columbus, OH, USA, June 24-27, 2014, pp. 580-587. 

27. R. Girshick, "Fast R-CNN," CVPR, Boston, MA, USA, June 8-10, 2015. pp. 1440-1448. 

28. S. Ren et al., "Faster R-CNN: Towards real-Time Object Detection with Region Proposal Networks," NIPS, Montreal, Canada, Dec. 7-12, 2015, pp. 91-99. 

29. ICCV 2015, "ImageNet and MS COCO Visual Recognition Challenges Joint Workshop," Accessed 2018. http://imagenet.org/challenges/ilsvrc+mscoco2015 

30. COCO, "COCO (Common Objects in Context," Accessed 2018. http://cocodataset.org/ 

31. K. He et al., "Identity Mappings in Deep Residual Networks," ECCV, Amsterdam, Netherlands, Oct. 8-16, 2016, pp. 630-645. 

32. The Third Research Institute of the Ministry of Public Security, P.R. China, "TRIMPS," Accessed 2018. http://hr.trimps.ac.cn/ 

33. SenseTime, "SenseTime," Accessed 2018. https://www.sensetime.com/ 

34. Hikvision, "Hikvision," Accessed 2018. http://en.hikrobotics.com/welcome.htm 

35. Y. Li, et al., "Fully Convolutional Instance-Aware Semantic Segmentation," CVPR, Honolulu, HI, USA, July 21-26, 2017, pp. 4438-4446. 

36. S. Xie et al., "Aggregated Residual Transformations for Deep Neural Networks," CVPR, Honolulu, HI, USA, July 21-26, 2017, pp. 5987-5995. 

37. Cityscapes Team, "Cityscapes Dataset," Accessed 2018. https://www.cityscapes-dataset.com/ 

38. Google, "Youtube8M Dataset," Accessed 2018. https://research.google.com/youtube8m/ 

39. Google, "Open Images Dataset v4," Accessed 2018. https://storage.googleapis.com/openimages/web/index.html 

40. Qihoo 360, "About Qihoo 360," Accessed 2018. https://www.360totalsecurity.com/en/about/ 

41. J. Hu, L. Shen, and G. Sun, "Squeeze-and-Excitation Networks," arXiv preprint arXiv:1709.01507, 2017. 

42. Y. Chen et al., "Dual Path Networks," NIPS, Long beach, CA, USA, Dec. 4-9, 2017, pp. 4470-4478. 

43. G. Huang et al., "Densely Connected Convolutional Networks," CVPR, Honolulu, HI, USA, July 21-26, 2017, pp. 4700-4708. 

44. X. Zhang et al., "PolyNet: A Pursuit of Structural Diversity in Very Deep Networks," CVPR, Honolulu, HI, USA, July 21-26, 2017, pp. 718-726. 

45. M. Denil et al., "Predicting Parameters in Deep Learning," NIPS, Lake Tehoe, CA, USA, Dec. 5-10, 2013. 

46. M. Jaderberg, A. Vedaldi, and A. Zis-serman, "Speeding up Convolutional Neural Networks with Low Rank Expansions," BMVC, Nottingham, UK, 2014. 

47. E. Denton et al., "Exploiting Linear Structure within convolutional Networks for Efficient Evaluation," NIPS, Montreal, Canada, Dec. 8-13, 2014. 

48. V. Lebedev et al., "Speeding-up Convolutional Neural Networks Using Fine-Tuned CP-Decomposition," ICLR, San Diego, CA, USA, May 7-9, 2015. 

49. Y. Gong, L. Liu, and L. Bourdev, "Compressing Deep Convolutional Networks Using Vector Quantization," arXiv preprint arXiv:1412.6115v1, Dec. 2014. 

50. S. Hang et al., "Learning Both Weight and CONNEctions for Efficient Neural Networks," NIPS, Montreal, Canada, Dec. 7-12, 2015. 

51. A.G. Howard et al., "Mobilenets: Efficient Convolutional Neural Networks for Mobile Vision Applications," arXiv preprint arXiv:1704.04861, 2017. 

52. M. Sandler et al., "Inverted Residuals and Linear Bottlenecks: Mobile Networks for Classification, Detection and Segmentation," arXiv preprint arXiv:1801.04381, 2018. 

53. X. Zhang et al., "Shufflenet: An Extremely Efficient Convolutional Neural Network for Mobile Devices," arXiv preprint arXiv:1707.01083, 2017. 

54. F.N. Iandola et al., "SqueezeNet: AlexNet-Level Accuracy with 50x Fewer Parameters and < 0.5 MB Model Size," arXiv preprint arXiv:1602.07360, 2016. 

55. IEEE Rebooting Computing, "Low-Power Image Recognition Challenge," Accessed 2018. https://rebootingcomputing.ieee.org/lpirc 

56. Google, "Tensorflow Lite," Accessed 2018. https://www.tensorflow.org/mobile/tflite/ 

57. Facebook, "Caffe2," Accessed 2018. https://caffe2.ai/ 

58. Apple, "CoreML," Accessed 2018. https://developer.apple.com/documentation/coreml 

59. Google, "Google Cloud AI," Accessed 2018. https://cloud.google.com/products/machine-learning/ 

60. Amazon, "AWS 기반 기계 학습," Accessed 2018. https://aws.amazon.com/ko/machine-learning/ 

61. Microsoft, "Microsoft AI," Accessed 2018. https://www.microsoft.com/en-us/ai/ 

62. Nokia, "Nokia Smart City," Accessed 2018. https://networks.nokia.com/smart-city 

63. Amazon, "Amazon Go," Accessed 2018. https://www.amazon.com/b?ieUTF8&node16008589011 

64. C. Lu et al., "Visual Relationship Detection with Language Priors," ECCV, Amsterdam, Netherlands, Oct. 8-16, 2016, pp. 852-869. 

65. A. Santoro et al., "A Simple Neural Network Module for Relational Reasoning," arXiv preprint arXiv:1706.01427, 2017.