Recurrent convolutional neural network for object recognition

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

Volumn 07-12-June-2015, Issue , 2015, Pages 3367-3375

  Liang, Ming ^a   Hu, Xiaolin ^a  

^a TSINGHUA UNIVERSITY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER VISION; CONVOLUTION; NEURAL NETWORKS; OBJECT RECOGNITION; PATTERN RECOGNITION;

CONTEXT INFORMATION; CONVOLUTIONAL NEURAL NETWORK; FEED-FORWARD ARCHITECTURES; FIXED NUMBERS; LEARNING PROCESS; MULTIPLE-PATH; STATE OF THE ART; VISUAL SYSTEMS;

RECURRENT NEURAL NETWORKS;

EID: 84959193001     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2015.7298958     Document Type: Conference Paper

References (54)

1. T. D. Albright and G. R. Stoner. Contextual influences on visual processing. Annual review of neuroscience, 25(1):339-379, 2002
2. S. Behnke. Hierarchical Neural Networks for Image Interpretation, volume 2766 of Lecture Notes in Computer Science. Springer-Verlag, 2003
3. Y-L. Boureau, F. Bach, Y LeCun, and J. Ponce. Learning mid-level features for recognition. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 2559-2566, 2010
4. G. A. Carpenter and S. Grossberg. A massively parallel architecture for a self-organizing neural pattern recognition machine. Computer vision, graphics, and image processing, 37(1):54-115, 1987
5. K. Chatfield, K. Simonyan, A. Vedaldi, and A. Zisserman. Return of the devil in the details: Delving deep into convolutional nets. In British Machine Vision Conference, 2014
6. P. Dayan and L. F. Abbott. Theoretical neuroscience. Cambridge, MA: MIT Press, 2001
7. G. Deco and T. S. Lee. The role of early visual cortex in visual integration: a neural model of recurrent interaction. European Journal of Neuroscience, 20(4): 1089-1100, 2004
8. J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. FeiFei. Imagenet: A large-scale hierarchical image database. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 248-255, 2009
9. D. Eigen, J. Rolfe, R. Fergus, and Y LeCun. Understanding deep architectures using a recursive convolutional network. In International Conference on Learning Representations (ICLR), 2014
10. J. L. Elman. Finding structure in time. Cognitive Science, 14(2):179-211, 1990
11. B. Fernandez, A. G. Pari os, and W. Tsai. Nonlinear dynamic system identification using artificial neural networks (anns). In International Joint Conference on Neural Networks (IJCNN), pages 133-141, 1990
12. J. Fraser. A new visual illusion of direction. British Journal of Psychiatry, 2: 307-320, 1908
13. K. Fukushima. Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biological cybernetics, 36(4):193-202, 1980
14. X. Glorot, A. Bordes, and Y Bengio. Deep sparse rectifier networks. In Proceedings of the 14th International Conference on Artificial Intelligence and Statistics, volume 15, pages 315-323, 2011
15. I. Goodfellow, A. Couville, and Y Bengio. Large-scale feature learning with spike-and-slab sparse coding. In International Conference on Machine Learning (ICML). 2012
16. I. J. Goodfellow, Y Bulatov, J. Ibarz, S. Arnoud, and V. Shet. Multi-digit number recognition from street view imagery using deep convolutional neural networks. In International Conference on Learning Representations (ICLR), 2014
17. I. J. Goodfellow, D. Warde-Farley, M. Mirza, A. C. Courville, and Y Bengio. Maxout networks. In Proceedings of the 30th International Conference on Machine Learning (ICML), pages 1319-1327, 2013
18. A. Graves, M. Liwicki, S. Fernandez, R. Bertolami, H. Bunke, and J. Schmidhuber. A novel connectionist system for unconstrained handwriting recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAM!), 31(5):855-868, 2009
19. A. Graves, A.-r. Mohamed, and G. Hinton. Speech recognition with deep recurrent neural networks. In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 6645-6649, 2013
20. A. Graves and J. Schmidhuber. Offline handwriting recognition with multidimensional recurrent neural networks. In Advances in Neural Information Processing Systems (NIPS), pages 545-552, 2009
21. J. J. Hopfield. Neural networks and physical systems with emergent collective computational abilities. Proceedings of the National Academy of Sciences, 79(8): 2554-2558, 1982
22. D. H. Hubel and T. N. Wiesel. Receptive fields of single neurones in the cat's striate cortex. The Journal of physiology, 148(3):574, 1959
23. D. H. Hubel and T. N. Wiesel. Receptive fields, binocular interaction and functional architecture in the cat's visual cortex. The Journal of Physiology, 160(1): 106, 1962
24. M. I. Jordan. attractor dynamics and parallelism in a connectionist sequential machine. pages 112-127. IEEE Press, 1990
25. A. Krizhevsky and G. Hinton. Learning multiple layers of features from tiny images. Computer Science Department, University of Toronto, Tech. Rep, 2009
26. A. Krizhevsky, I. Sutskever, and G. E. Hinton. Imagenet classification with deep convolutional neural networks. In Advances in Neural Information Processing Systems (NIPS), pages 1097-1105, 2012
27. Y. LeCun, B. Boser, J. Denker, D. Henderson, R. Howard, W. Hubbard, and L. Jackel. Handwritten digit recognition with a back-propagation network. In Advances in Neural Information Processing Systems (NIPS), pages 396-404, 1990
28. Y. LeCun, B. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard, and L. D. Jackel. Backpropagation applied to handwritten zip code recognition. Neural Computation, 1(4):541-551, 1989
29. Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner. Gradientbased learning applied to document recognition. Proceedings of the IEEE, 86(11):2278-2324, 1998
30. c.-Y. Lee, S. Xie, P. Gallagher, Z. Zhang, and Z. Tu. Deeplysupervised nets. In Advances in neural information processing systems (NIPS), Deep Learning and Representation Learning Workshop, 2014
31. H. Lee, R. Grosse, R. Ranganath, and A. Y. Ng. Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations. In Proceedings of the 26th Annual International Conference on Machine Learning (ICML), pages 609-616, 2009
32. T. S. Lee and D. Mumford. Hierarchical Bayesian inference in the visual cortex. Journal of the Optical Society of America A, 20(7):1434-1448, 2003
33. M. Lin, Q. Chen, and S. Yan. Network in network. In International Conference on Learning Representations (ICLR), 2014
34. w. McCulloch and W. Pitts. A logical calculus of the ideas immanent in nervous activity. Bulletin of Mathematical Biophysics, 5:115-133, 1943
35. Y. Netzer, T. Wang, A. Coates, A. Bissacco, B. Wu, and A. Y. Ng. Reading digits in natural images with unsupervised feature learning. In NIPS workshop on Deep Learning and Unsupervised Feature Learning, volume 2011, page 4, 2011
36. P. Pinheiro and R. Collobert. Recurrent convolutional neural networks for scene labeling. In Proceedings of the 31st International Conference on Machine Learning (ICML), pages 82-90, 2014
37. G. Y. Puskorius and L. A. Feldkamp. Neurocontrol of nonlinear dynamical systems with kalman filter trained recurrent networks. IEEE Transactions on Neural Networks, 5(2):279-297, 1994
38. M. Riesenhuber and T. Poggio. Hierarchical models of object recognition in cortex. Nature Neuroscience, 2: 1019-1025, 1999
39. F. Rosenblatt. Principles of neurodynamics; perceptrons and the theory of brain mechanisms. Spartan Books Washington, 1962
40. D. E. Rumelhart, G. E. Hinton, and R. J. Williams. Parallel distributed processing: Explorations in the microstructure of cognition, vol. 1. chapter Learning Internal Representations by Error Propagation, pages 318-362. MIT Press, 1986
41. A. Saxe, P. W. Koh, Z. Chen, M. Bhand, B. Suresh, and A. Y. Ng. On random weights and unsupervised feature learning. In Proceedings of the 28th International Conference on Machine Learning (ICML), pages 1089-1096, 2011
42. P. Series, J. Lorenceau, and Y. Fregnac. The silent surround of V 1 receptive fields: theory and experiments. Journal of physiology-Paris, 97(4):453-474, 2003
43. A. Sharif Razavian, H. Azizpour, J. Sullivan, and S. Carlsson. Cnn features off-the-shelf: An astounding baseline for recognition. In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2014
44. K. Simonyan and A. Zisserman. Very deep convolutional networks for large-scale image recognition. CoRR, absIl409. 1556, 2014
45. R. Socher, C. C. Lin, C. Manning, and A. Y. Ng. Parsing natural scenes and natural language with recursive neural networks. In Proceedings of the 28th International Conference on Machine Learning (ICML), pages 129-136, 2011
46. R. Socher, C. D. Manning, and A. Y. Ng. Learning continuous phrase representations and syntactic parsing with recursive neural networks. In Advances in neural information processing systems (NIPS), Deep Learning and Representation Learning Workshop, pages 1-9, 2010
47. J. T. Springenberg and M. Riedmiller. Improving deep neural networks with probabilistic maxout units. In International Conference on Learning Representations (ICLR), 2014
48. N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov. Dropout: A simple way to prevent neural networks from overfitting. Journal of Machine Learning Research, 15:1929-1958, 2014
49. N. Srivastava and R. Salakhutdinov. Discriminative transfer learning with tree-based priors. In Advances in Neural Information Processing Systems (NIPS), pages 2094-2102, 2013
50. C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, Y. Vanhoucke, and A. Rabinovich. Going deeper with convolutions. arXiv preprint arXiv: 1409. 4842, 2014
51. L. Wan, M. Zeiler, S. Zhang, Y. L. Cun, and R. Fergus. Regularization of neural networks using dropconnect. In Proceedings of the 30th International Conference on Machine Learning (ICML), pages 1058-1066, 2013
52. P. J. Werbos. Backpropagation through time: what it does and how to do it. Proceedings of the IEEE, 78(10): 1550-1560, 1990
53. M. D. Zeiler and R. Fergus. Stochastic pooling for regularization of deep convolutional neural networks. In International Conference on Learning Representations (ICLR), 2013
54. M. Zhu and C. Rozell. Visual nonclassical receptive field effects emerge from sparse coding in a dynamical system. PLOS Computational Biology, 9(8): e1003191, 2013.