Computer Vision 연구자가 Deep Learning의 시대를 사는 법

References

1. G. Hinton, S. Osindero, and Y.-W. Teh, "A fast learning algorithm for deep belief nets", Neural Computation, 2006. https://doi.org/10.1162/neco.2006.18.7.1527
2. Y. Bengio, "Learning deep architectures for AI", Foundations and Trends${(R)}$ in Machine Learning, 2009.
3. A. Krizhevsky, I. Sutskever, and G. Hinton, "Imagenet classification with deep convolutional neural networks", NIPS, 2012.
4. P. Sermanet, D. Eigen, X. Zhang, M. Mathieu, R. Fergus, and Y. LeCun: "OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks", International Conference on Learning Representations (ICLR 2014), April 2014.
5. Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, and T. Darrell, "Caffe: Convolutional Architecture for Fast Feature Embedding", arXiv preprint arXiv:1408.5093, 2014.
6. R. Girshick, J. Donahue, T. Darrell, and J. Malik, "Rich feature hierarchies for accurate object detection and semantic segmentation", CVPR, 2014.
7. K. Simonyan, A. Zisserman, "Two-Stream Convolutional Networks for Action Recognition in Videos", NIPS, 2014.
8. D. Tran, L. Bourdev, R. Fergus, L. Torresani, and M. Paluri, "C3D: Generic Features for Video Analysis", arXiv:1412.0767, 2014.
9. J. Y.-H. Ng, M. Hausknecht, S. Vijayanarasimhan, O. Vinyals, R. Monga, and G. Toderici, "Beyond Short Snippets: Deep Networks for Video Classification", CVPR, 2015.
10. M. S. Ryoo, B. Rothrock, and L. Matthies, "Pooled Motion Features for First-Person Videos", CVPR, 2015.