Modeling local and global deformations in Deep Learning: Epitomic convolution, Multiple Instance Learning, and sliding window detection

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

Volumn 07-12-June-2015, Issue , 2015, Pages 390-399

  Papandreou, George ^a   Kokkinos, Iasonas ^b   Savalle, Pierre André ^b  

^a GOOGLE INC   (United States)

^b CENTRALESUPÉLEC   (France)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; ASPECT RATIO; CLASSIFICATION (OF INFORMATION); COMPUTER VISION; CONVOLUTION; DEFORMATION; LEARNING ALGORITHMS; LEARNING SYSTEMS; NEURAL NETWORKS; PATTERN RECOGNITION;

COMPUTATIONAL COSTS; CONVOLUTIONAL NEURAL NETWORK; DOMAIN TRANSFORMATION; GLOBAL DEFORMATIONS; MODEL DEFORMATIONS; MULTIPLE INSTANCE LEARNING; SLIDING WINDOW; SLIDING WINDOW DETECTOR;

IMAGE CLASSIFICATION;

EID: 84959218210     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2015.7298636     Document Type: Conference Paper

References (45)

1. M. Aharon and M. Elad. Sparse and redundant modeling of image content using an image-signaturedictionary. SIAM J. Imaging Sci., 1(3):228-247, 2008
2. S. Andrews, T. Hofmann, and I. Tsochantaridis. Support vector machines for multiple instance learning. In NIPS, 2002
3. L. Benot, J. Mairal, F. Bach, and J. Ponce. Sparse image representation with epitomes. In Proc. CVPR, pages 2913-2920, 2011
4. K. Chatfield, K. Simonyan, A. Vedaldi, and A. Zisserman. Return of the devil in the details: Delving deep into convolutional nets. arXiv, 2014
5. T. Cootes, G. Edwards, and C. Taylor. Active appearance models. IEEE Trans. PAMI, 23(6):681-685, 2001
6. T. Cootes and C. Taylor. Constrained active appearance models. In Proc. ICCV, volume 1, pages 748-754, 2001
7. J. Deng, W. Dong, R. Socher, L. Li-Jia, K. Li, and L. Fei-Fei. Imagenet: A large-scale hierarchical image database. In Proc. CVPR, 2009
8. T. G. Dietterich, R. H. Lathrop, and T. Lozanoperez. Solving the multiple-instance problem with axis-parallel rectangles. Artificial Intelligence, 89:31-71, 1997
9. C. Dubout and F. Fleuret. Exact acceleration of linear object detectors. In Computer Vision-ECCV 2012, pages 301-311. Springer Berlin Heidelberg, 2012
10. P. Felzenszwalb, R. Girshick, D. McAllester, and D. Ramanan. Object detection with discriminatively trained part-based models. IEEE Trans. PAMI, 32(9):1627-1645, 2010
11. R. Girshick, J. Donahue, T. Darrell, and J. Malik. Rich feature hierarchies for accurate object detection and semantic segmentation. In Proc. CVPR, 2014
12. R. Girshick, F. Iandola, T. Darrell, and J. Malik. Deformable part models are convolutional neural networks. arXiv:1409. 5403, 2014
13. R. B. Girshick, F. N. Iandola, T. Darrell, and J. Malik. Deformable part models are convolutional neural networks. CoRR, abs/1409. 5403, 2014
14. I. Goodfellow, D. Warde-Farley, M. Mirza, A. Courville, and Y. Bengio. Maxout networks. In Proc. ICML, 2013
15. K. He, X. Zhang, S. Ren, and J. Sun. Spatial pyramid pooling in deep convolutional networks for visual recognition. In ECCV, 2014
16. K. He, X. Zhang, S. Ren, and J. Sun. Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. CoRR, abs/1502. 01852, 2015
17. M. Hoai and A. Zisserman. Improving human action recognition using score distribution and ranking. In Asian Conference on Computer Vision, 2014
18. A. G. Howard. Some improvements on deep convolutional neural network based image classification, 2013. arXiv:1409. 0575
19. Y. Hu, M. Li, and N. Yu. Multiple-instance ranking: Learning to rank images for image retrieval. In Proc. CVPR, 2008
20. S. Ioffe and C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. CoRR, abs/1502. 03167, 2015
21. K. Jarrett, K. Kavukcuoglu, M. Ranzato, and Y. Le-Cun. What is the best multi-stage architecture for object recognition? In Proc. ICCV, pages 2146-2153, 2009
22. Y. Jia. Caffe: An open source convolutional architecture for fast feature embedding, 2013
23. N. Jojic, B. Frey, and A. Kannan. Epitomic analysis of appearance and shape. In Proc. ICCV, pages 34-41, 2003
24. A. Krizhevsky, I. Sutskever, and G. Hinton. ImageNet classification with deep convolutional neural networks. In Proc. NIPS, 2013
25. Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner. Gradient-based learning applied to document recognition. Proc. IEEE, 86(11):2278-2324, 1998
26. M. Lin, Q. Chen, and S. Yan. Network in network. In ICLR, 2014
27. J. Long, E. Shelhamer, and T. Darrell. Fully convolutional networks for semantic segmentation. CoRR, abs/1411. 4038, 2014
28. S. Mallat. A Wavelet Tour of Signal Processing. Acad. Press, 2 edition, 1999
29. M. Oquab, L. Bottou, I. Laptev, and J. Sivic. Weakly supervised object recognition with convolutional neural networks. Technical Report HAL-01015140, INRIA, 2014
30. W. Ouyang, P. Luo, X. Zeng, S. Qiu, Y. Tian, H. Li, S. Yang, Z. Wang, Y. Xiong, C. Qian, Z. Zhu, R. Wang, C. C. Loy, X. Wang, and X. Tang. Deepid-net: multi-stage and deformable deep convolutional neural networks for object detection. CoRR, abs/1409. 3505, 2014
31. W. Ouyang and X. Wang. Joint deep learning for pedestrian detection. In Proc. ICCV, 2013
32. W. Ouyang and X. Wang. Joint deep learning for pedestrian detection. In ICCV, 2013
33. P.-A. Savalle and S. Tsogkas and G. Papandreou and I. Kokkinos. Deformable part models with cnn features. In 3rd Parts and Attributes Workshop, ECCV., 2014
34. G. Papandreou. Deep epitomic convolutional neural networks. arXiv, 2014
35. G. Papandreou, L.-C. Chen, and A. Yuille. Modeling image patches with a generic dictionary of miniepitomes. In Proc. CVPR, 2014
36. S. Ray and M. Craven. Supervised versus multiple instance learning: an empirical comparison. In Proc. ICML, 2005
37. A. Razavian, H. Azizpour, J. Sullivan, and S. Carlsson. CNN features off-the-shelf: An astounding baseline for recognition. In Proc. CVPR Workshop, 2014
38. M. Riesenhuber and T. Poggio. Hierarchical models of object recognition in cortex. Nature neuroscience, 2(11):1019-1025, 1999
39. P. Sermanet, D. Eigen, X. Zhang, M. Mathieu, R. Fergus, and Y. LeCun. Overfeat: Integrated recognition, localization and detection using convolutional networks. 2014
40. K. Simonyan and A. Zisserman. Very deep convolutional networks for large-scale image recognition, 2014. http://arxiv. org/abs/1409. 1556/
41. C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich. Going deeper with convolutions. CoRR, abs/1409. 4842, 2014
42. J. R. R. Uijlings, K. E. A. van de Sande, T. Gevers, and A. W. M. Smeulders. Selective search for object recognition. 2013
43. M. Varma and D. Ray. Learning the discriminative power-invariance trade-off. In ICCV, 2007
44. L. Wan, D. Eigen, and R. Fergus. End-to-end integration of a convolutional network, deformable parts model and non-maximum suppression. arXiv, 2014
45. M. Zeiler and R. Fergus. Visualizing and understanding convolutional networks. arXiv, 2013.