Group norm for learning structured SVMs with unstructured latent variables

Proceedings of the IEEE International Conference on Computer Vision

Volumn , Issue , 2013, Pages 409-416

  Chen, Daozheng ^a,d   Batra, Dhruv ^b   Freeman, William T ^c  

^a UNIVERSITY OF MARYLAND   (United States)

^b VIRGINIA POLYTECHNIC INSTITUTE AND STATE UNIVERSITY   (United States)

^c MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

^d YAHOO INC   (United States)

Author keywords

Concave Convex Procedure; Coordinate Descent; Deformable Part Models; Group Norm; Latent Structured SVMs; Latent SVMs; Latent variable models; Object Detection; State Learning

Indexed keywords

EQUIVALENCE CLASSES; SUPPORT VECTOR MACHINES;

CONCAVE-CONVEX PROCEDURE; COORDINATE DESCENT; DEFORMABLE PART MODELS; GROUP NORM; LATENT STRUCTURED SVMS; LATENT SVMS; LATENT VARIABLE MODELS; OBJECT DETECTION; STATE LEARNING;

OBJECT RECOGNITION;

EID: 84898822924     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICCV.2013.58     Document Type: Conference Paper

References (30)

1. F. Bach, R. Jenatton, J. Mairal, and G. Obozinski. Structured sparsity through convex optimization. Statistical Science, 2012.
2. S. Bakin. Adaptive Regression and Model Selection in Data Mining Problems. Australian National University, 1999.
3. S. Bengio, F. Pereira, Y. Singer, and D. Strelow. Group sparse coding. In NIPS, 2009.
4. Y. Bengio, J. Louradour, R. Collobert, and J. Weston. Curriculum learning. In ICML, 2009.
5. V. Chandrasekaran, P. A. Parrilo, and A. S. Willsky. Latent variable graphical model selection via convex optimization. Annals of Statistics, 2012.
6. S. S. Chen, D. L. Donoho, and M. A. Saunders. Atomic decomposition by basis pursuit. SIAM Review, 2001.
7. N. Dalal and B. Triggs. Histograms of oriented gradients for human detection. In CVPR, 2005.
8. A. P. Dempster, N. M. Laird, and D. B. Rubin. Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society, 1977.
9. S. K. Divvala, A. A. Efros, and M. Hebert. How important are deformable parts in the deformable parts model? CoRR, abs/1206.3714, 2012.
10. D. L. Donoho. Compressed sensing. Info. Theory, 2006.
11. B. Efron, T. Hastie, I. Johnstone, and R. Tibshirani. Least angle regression. The Annals of Statistics, 2004.
12. M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman. The PASCAL Visual Object Classes Challenge 2007 (VOC2007) Results. http://www.pascalnetwork. org/challenges/VOC/voc2007/workshop/index.html.
13. P. F. Felzenszwalb, R. B. Girshick, and D. McAllester. Discriminatively trained deformable part models, release 4. http://www.cs.brown.edu/∼pff/ latent-release4/.
14. P. F. Felzenszwalb, R. B. Girshick, D. McAllester, and D. Ramanan. Object detection with discriminatively trained part-based models. PAMI, 2010.
15. K. Jia, T.-H. Chan, and Y. Ma. Robust and practical face recognition via structured sparsity. In ECCV, 2012.
16. Y. Jia, M. Salzmann, and T. Darrell. Factorized latent spaces with structured sparsity. In NIPS, 2010.
17. T. Koo, A. M. Rush, M. Collins, T. Jaakkola, and D. Sontag. Dual decomposition for parsing with non-projective head automata. In EMNLP, 2010.
18. P. Kumar, B. Packer, and D. Koller. Self-paced learning for latent variable models. In NIPS, 2010.
19. Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 1998.
20. J. Mairal, F. Bach, J. Ponce, and G. Sapiro. Online dictionary learning for sparse coding. In ICML, 2009.
21. P. Ott and M. Everingham. Shared parts for deformable part-based models. In CVPR, 2011.
22. H. Pirsiavash and D. Ramanan. Steerable part models. In CVPR, 2012.
23. A. Quattoni, M. Collins, and T. Darrell. Conditional random fields for object recognition. In NIPS, 2004.
24. L. Rabiner. A tutorial on hidden markov models and selected applications in speech recognition. Proceedings of the IEEE, 1989.
25. H. O. Song, S. Zickler, T. Althoff, R. Girshick, M. Fritz, C. Geyer, P. Felzenszwalb, and T. Darrell. Sparselet models for efficient multiclass object detection. In ECCV, 2012.
26. R. Tibshirani. Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society, 1996.
27. C. J. Yu and T. Joachims. Learning structural svms with latent variables. In ICML, 2009.
28. M. Yuan, M. Yuan, Y. Lin, and Y. Lin. Model selection and estimation in regression with grouped variables. Journal of the Royal Statistical Society, Series B, 2006.
29. A. L. Yuille and A. Rangarajan. The concave-convex procedure. Neural Computation, 2003.
30. H. Zou and T. Hastie. Regularization and variable selection via the elastic net. Journal Of The Royal Statistical Society Series B, 2005.