Learning to select pre-trained deep representations with Bayesian evidence framework

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

Volumn 2016-December, Issue , 2016, Pages 5318-5326

  Kim, Yong Deok ^a   Jang, Taewoong ^b   Han, Bohyung ^c   Choi, Seungjin ^c  

^a SAMSUNG Electronics   (South Korea)

^b StradVision Incorporation   (South Korea)

^c Pohang University of Science and Technology (POSTECH)   (South Korea)

Author keywords

[No Author keywords available]

Indexed keywords

BAYESIAN NETWORKS; COMPUTER VISION; NEURAL NETWORKS;

BAYESIAN EVIDENCE FRAMEWORK; COMPETITIVE PERFORMANCE; CONVOLUTIONAL NEURAL NETWORK; PREDICTION ACCURACY; REGULARIZATION PARAMETERS; STATE-OF-THE-ART PERFORMANCE; TRAINING AND TESTING; VISUAL RECOGNITION;

PATTERN RECOGNITION;

EID: 84986313422     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2016.574     Document Type: Conference Paper

References (37)

1. A. C. Aitken. On Bernoulli's numerical solution of algebraic equations. Proceedings of the Royal Society of Edinburgh, 46: 289-305, 1927.
2. H. Azizpour, A. S. Razavian, J. Sulivan, A. Maki, and S. Carlsson. From generic to specific deep representations for visual recognition. In CVPR Workshops, 2015.
3. A. Binder, W. Samek, M. Kloft, C. Müller, K.-R. Müller, and M. Kawanabe. The joint submission of the TU Berlin and Fraunhofer FIRST (TUBFI) to the ImageCLEF2011 photo annotation task, 2011.
4. C. M. Bishop. Neural Networks for Pattern Recognition. Clarendon press Oxford, 1995.
5. L. D. Bourdev, S. Maji, and J. Malik. Describing people: A poselet-based approach to attribute classification. In ICCV, 2011.
6. K. Chatfield, K. Simonyan, A. Vedaldi, and a. Zisserman. Return of the devil in the details: Delving deep into convolutional nets. In BMVC, 2014.
7. J. Donahue, Y. Jia, O. Vinyals, J. Hoffman, N. Zhang, E. Tzeng, and T. Darrell. DeCAF: A deep convolutional activation feature for generic visual recognition. In ICML, 2014.
8. M. Everingham, L. V. Gool, C. K. I. Williams, J. Winn, and A. Zisserman. The PASCAL Visual Object Classes Challenge 2007 (VOC 2007) Results, 2007.
9. M. Everingham, L. V. Gool, C. K. I. Williams, J. Winn, and A. Zisserman. The PASCAL Visual Object Classes Challenge 2012 (VOC 2012) Results, 2012.
10. R. E. Fan, K. W. Chang, C. J. Hsieh, X. R. Wang, and C. J. Lin. LIBLINEAR: A library for large linear classification. JMLR, 9: 1871-1874, 2008.
11. A. Farhadi, I. Endres, D. Hoiem, and D. A. Forsyth. Describing objects by their attributes. In CVPR, 2009.
12. L. Fei-Fei, R. Fergus, and P. Perona. Learning generative visual models from few training examples: An incremental Bayesian approach tested on 101 object categories. CVIU, 106 (1): 59-70, 2007.
13. R. Girshick, J. Donahue, T. Darrell, and J. Malik. Rich feature hierarchies for accurate object detection and semantic segmentation. In CVPR, 2014.
14. G. Griffin, A. Holub, and P. Perona. Caltech-256 object category dataset. Technical report, California Institute of Technology, 2007.
15. A. Krizhevsky, I. Sutskever, and G. E. Hinton. ImageNet classification wit deep convolutional neural networks. In NIPS, volume 25, 2012.
16. D. J. C. MacKay. Bayesian interpolation. Neural Computation, 4 (3): 415-447, 1992.
17. H. Nam and B. Han. Learning multi-domain convolutional neural networks for visual tracking. In CVPR, 2016.
18. M.-E. Nilsback and A. Zisserman. Automated flower classification over a large number of classes. In Proceedings of the Indian Conference on Computer Vision, Graphics and Image Processing, 2008.
19. H. Noh, S. Hong, and B. Han. Learning deconvolution network for semantic segmentation. In ICCV, 2015.
20. S. Nowak, K. Nagel, and J. Liebetrau. The CLEF 2011 photo annotation and concept-based retrieval tasks. In CLEF Workshop Notebook Paper, 2011.
21. M. Oquab, L. Bottou, I. Laptev, and J. Sivic. Learning and transferring mid-level image representations using convolutional neural networks. In CVPR, 2014.
22. A. Quattoni and A. Torrabla. Recognizing indoor scenes. In CVPR, 2009.
23. A. S. Razavian, H. Azizpour, J. Sullivan, and S. Carlsson. CNN features off-the-shelf: An astounding baseline for recognition. In CVPR Workshops, 2014.
24. P. Sermanet, D. Eigen, X. Zhang, M. Mathieu, R. Fergus, and Y. LeCun. OverFeat: Integrated recognition, localization and detection using convolutional networks. In ICLR, 2014.
25. K. Simonyan and A. Zisserman. Very deep convolutional networks for large-scale image recognition. In ICLR, 2015.
26. J. A. K. Suykens and J. Vandewalle. Least squares support vector machine classifiers. Neural Processing Letters, 9 (3): 293-300, 1999.
27. C. Szegedy, W. Liu, Y. Jia, P. Sermanet, D. A. S. Reed, D. Erhan, V. Vanhoucke, and A. Rabinovich. Going deeper with convolutions. In CVPR, 2015.
28. T. Van Gestel, J. A. K. S. B. Baesems, S. Viaene, J. Vanthienen, G. Dedene, B. De Moor, and J. Vandewalle. Benchmarking least squares support vector machines classifiers. Machine Learning, 54 (1): 5-32, 2004.
29. T. Van Gestel, J. A. K. Suykens, G. Lanckrie, A. Lambrechts, B. D. Moor, and J. Vandewalle. Bayesian framework for least-squares support vector machine classifiers, Gaussian processes, and kernel fisher discriminant analysis. Neural Computation, 14 (5): 1115-1147, 2002.
30. C. Wah, S. Branson, P. Welinder, P. Perona, and S. Belongie. The Caltech-UCSD Birds-200-2011 dataset. Technical report, California Institute of Technology, 2011.
31. Z. Wu, Y. Zhang, F. Yu, and J. Xiao. A GPU implementation of GoogLeNet. Technical report, Princeton University, 2014.
32. J. Xiao, J. Hays, K. A. Ehinger, A. Oliva, and A. Torrabla. SUN database: Large-scale scene recognition from abbey to zoo. In CVPR, 2010.
33. B. Yao, X. Jiang, A. Khosla, A. L. Lin, L. J. Guibas, and L. Fei-Fei. Action recognition by learning bases of action attributes and parts. In ICCV, 2011.
34. M. D. Zeiler and R. Fergus. Visualizing and understanding convolutional networks. In ECCV, 2014.
35. N. Zhang, M. Paluri, M. Ranzato, T. Darrell, and L. Bourdev. PANDA: Pose aligned networks for deep attribute modeling. In CVPR, 2014.
36. N. Zhang, J. Donahue, R. Girshick, and T. Darrell. Partbased R-CNNs for fine-grained category detection. In ECCV, 2014.
37. P. Zhang and J. Peng. SVM vs regularized least squares classification. In ICPR, 2004.