A Neural Autoregressive Approach to Attention-based Recognition

International Journal of Computer Vision

Volumn 113, Issue 1, 2015, Pages 67-79

  Zheng, Yin ^a   Zemel, Richard S ^b   Zhang, Yu Jin ^a   Larochelle, Hugo ^c  

^a TSINGHUA UNIVERSITY   (China)

^b UNIVERSITY OF TORONTO   (Canada)

^c UNIVERSITÉ DE SHERBROOKE   (Canada)

Author keywords

Attention based recognition; Deep learning; Neural autoregressive distribution estimator; Neural networks

Indexed keywords

ARTIFICIAL INTELLIGENCE; COMPUTER VISION; GESTURE RECOGNITION; LEARNING SYSTEMS; NEURAL NETWORKS;

ATTENTION-BASED RECOGNITION; AUTO-REGRESSIVE; DEEP LEARNING; EXACT CALCULATIONS; FACIAL EXPRESSION RECOGNITION; PERCEPTION AND ACTIONS; RESTRICTED BOLTZMANN MACHINE; VISUAL RECOGNITION;

FACE RECOGNITION;

EID: 84939873522     PISSN: 09205691     EISSN: 15731405     Source Type: Journal    
DOI: 10.1007/s11263-014-0765-x     Document Type: Article

References (31)

1. Bazzani, L., Freitas, N., Larochelle, H., Murino, V., & Ting, J.-A. (2011). Learning attentional policies for tracking and recognition in video with deep networks. In Proceedings of the 28th international conference on machine learning (ICML 2011) (pp. 937–944). ACM.
2. Butko, N. J., & Movellan, J. R. (2010). Infomax control of eye movements. IEEE Transactions on Autonomous Mental Development, 2(2), 91–107.
3. Cheng, M.-M., Zhang, G.-X., Mitra, N. J., Huang, X., & Hu, S.-M. (2011). Global contrast based salient region detection. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2011 (pp. 409–416). IEEE.
4. Dalal, N., & Triggs, B. (2005). Histograms of oriented gradients for human detection. In IEEE computer society conference on computer vision and pattern recognition. CVPR 2005 (Vol. 1, pp. 886–893). IEEE.
5. David, G. (2004). Lowe. Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60(2), 91–110.
6. Denil, M., Bazzani, L., Larochelle, H., & de Freitas, N. (2012). Learning where to attend with deep architectures for image tracking. Neural Computation, 24(8), 2151–2184.
7. Erez, T., Tramper, J. J., Smart, W. D., & Stan CAM Gielen. (2011). A pomdp model of eye-hand coordination. In AAAI.
8. Fazl, A., Grossberg, S., & Mingolla, E. (2009). View-invariant object category learning, recognition, and search: How spatial and object attention are coordinated using surface-based attentional shrouds. Cognitive psychology, 58(1), 1–48.
9. Hinton, G. E., Srivastava, N., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. R. (2012). Improving neural networks by preventing co-adaptation of feature detectors. arXiv preprint arXiv:1207.0580.
10. Hinton, G. E. (2002). Training products of experts by minimizing contrastive divergence. Neural Computation, 14(8), 1771–1800.
11. Itti, L., Koch, C., & Niebur, E. (1998). A model of saliency-based visual attention for rapid scene analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(11), 1254–1259.
12. Judd, T., Ehinger, K., Durand, F., & Torralba, A. (2009). Learning to predict where humans look. In IEEE International Conference on Computer Vision (ICCV).
13. Kanan, C., & Cottrell, G. (2010) Robust classification of objects, faces, and flowers using natural image statistics. In CVPR.
14. Krause, A., & Ong, C. S. (2011). Contextual gaussian process bandit optimization. In NIPS (pp. 2447–2455).
15. Krizhevsky, A., Sutskever, I., & Hinton, G. (2012). Imagenet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems, 25, 1106–1114.
16. Larochelle, H., & Bengio, Y. (2008). Classification using discriminative restricted boltzmann machines. In Proceedings of the 25th international conference on machine learning (pp. 536–543). ACM.
17. Larochelle, H., & Hinton, G. E. (2010). Learning to combine foveal glimpses with a third-order Boltzmann machine. In Advances in neural information processing systems (pp. 1243–1251).
18. Larochelle, H., & Murray, I. (2011). The neural autoregressive distribution estimator. Artificial Intelligence and Statistics (AISTATS), 15, 29–37.
19. Larochelle, H., & Lauly, S. (2012). A neural autoregressive topic model. Advances in Neural Information Processing Systems, 25, 2717–2725.
20. Lazebnik, S. (2006). Cordelia, and Jean Ponce. Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. In CVPR.
21. Mathe, S., & Sminchisescu, C. (2013). Action from still image dataset and inverse optimal control to learn task specific visual scanpaths. In Advances in neural information processing systems (pp. 1923–1931, 2013).
22. Nair, V., & Hinton, G. E. (2010) Rectified linear units improve restricted boltzmann machines. In ICML.
23. Najemnik, J., & Geisler, W. S. (2005). Optimal eye movement strategies in visual search. Nature, 434(7031), 387–391.
24. Perazzi, F., Krahenbuhl, P., Pritch, Y., & Hornung, A. (2012). Saliency filters: Contrast based filtering for salient region detection. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2012 (pp. 733–740). IEEE.
25. Rifai, S., Vincent, P., Muller, X., Glorot, X., & Bengio, Y. (2011). Contractive auto-encoders: Explicit invariance during feature extraction. In Proceedings of the 28th international conference on machine learning (ICML 2011).
26. Schmidhuber, J., & Huber, R. (1991). Learning to generate artificial fovea trajectories for target detection. International Journal of Neural Systems, 2(01n02), 125–134.
27. Southall, J. P. C. (1962). Helmholtzs treatise on physiological optics. vol. 2: The sensation of vision, trans. J. P. C. Southall. (translated from the third german edition).
28. Susskind, J. M., Anderson, A. K., & Hinton, G. E. (2010). The toronto face database. Department of Computer Science, University of Toronto, Toronto, ON, Canada, Tech. Rep.
29. Uria, B., Murray, I., & Larochelle, H. (2013). Rnade: The real-valued neural autoregressive density-estimator. Advances in Neural Information Processing Systems, 26, 2175–2183.
30. Vincent, P., Larochelle, H., Bengio, Y., & Manzagol, P.-A. (2008). Extracting and composing robust features with denoising autoencoders. In Proceedings of the 25th international conference on machine learning (ICML 2008) (pp. 1096–1103). ACM.
31. Yang, J., Yu., K., & Gong, Y. (2009). Linear spatial pyramid matching using sparse coding for image classification. In CVPR.