- Deng, J., Dong, W., Sooner, R., Li, L.-J., Li, K. and Li, F.-F. ImageNet: A Large- scale hierarchical image database. In Proceedings of the IEEE Computer Vision and Pattern Recognition, (June 20--25, 2009).Google ScholarCross Ref
- Fukushima, K. Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biol Cybern 34, 4 (1980), 193--202.Google Scholar
- Girshick, R., Donahue, J., Darrell, T. and Malik, J. Rich feature hierarchies for accurate object detection and semantic segmentation. In Proceedings of the IEEE Computer Vision and Pattern Recognition, (2014). Google ScholarDigital Library
- Hubel, D.H. and Wiesel, T.N. Receptive fields, binocular interactions and functional architecture in the cat's visual cortex. J. Physiology 160, 1 (Jan. 1962), 106--154.Google ScholarCross Ref
- Hubel, D.H. and Wiesel, T.N. Receptive fields and functional architecture of monkey striate cortex. J. Physiology 195, 1 (Mar. 1968), 215--243.Google ScholarCross Ref
- LeCun, Y. et al. Backpropagation applied to handwritten zip code recognition. Neural Computation 1 (1989), 541--551. Google ScholarDigital Library
- Rumelhart, D.E., Hinton G.E, and Williams R.J. Learning representations by back-propagating errors. Nature 323 (Oct. 9, 1986), 533--536.Google ScholarCross Ref
- Werbos P. Beyond regression: New tools for prediction and analysis in the behavioral sciences. Ph.D. thesis, Harvard University, 1974.Google Scholar
Index Terms
- Technical Perspective: What led computer vision to deep learning?
Recommendations
Deep Learning for Computer Vision: A Brief Review
Over the last years deep learning methods have been shown to outperform previous state-of-the-art machine learning techniques in several fields, with computer vision being one of the most prominent cases. This review paper provides a brief overview ...
Deep reinforcement learning in computer vision: a comprehensive survey
AbstractDeep reinforcement learning augments the reinforcement learning framework and utilizes the powerful representation of deep neural networks. Recent works have demonstrated the remarkable successes of deep reinforcement learning in various domains ...
Comments