Scalable multitask representation learning for scene classification

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

Volumn , Issue , 2014, Pages 1434-1441

  Lapin, Maksim ^a   Schiele, Bernt ^a   Hein, Matthias ^b  

^a MAX PLANCK INSTITUTE FOR INFORMATICS   (Germany)

^b SAARLAND UNIVERSITY   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION (OF INFORMATION); IMAGE PROCESSING; MULTI-TASK LEARNING; PATTERN RECOGNITION;

CLASSIFICATION PERFORMANCE; FEATURE DIMENSIONS; FISHER VECTORS; HIGH-DIMENSIONAL IMAGES; LOW-DIMENSIONAL REPRESENTATION; SCENE CLASSIFICATION; STATE OF THE ART; TRAINING EXAMPLE;

LEARNING SYSTEMS;

EID: 84911375886     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2014.186     Document Type: Conference Paper

References (22)

1. A. Argyriou, T. Evgeniou, and M. Pontil. Convex multi-task feature learning. Machine Learning, 73(3):243-272, 2008.
2. R. Caruana. Multitask learning. Machine learning, 28(1):41-75, 1997.
3. K. Chatfield, V. Lempitsky, A. Vedaldi, and A. Zisserman. The devil is in the details: an evaluation of recent feature encoding methods. In BMVC, 2011.
4. S. Clinchant, G. Csurka, F. Perronnin, and J.-M. Renders. XRCE's participation to ImagEval. In ImageEval workshop at CVIR, 2007.
5. J. Donahue, Y. Jia, O. Vinyals, J. Hoffman, N. Zhang, E. Tzeng, and T. Darrell. DeCAF: A Deep Convolu-tional Activation Feature for Generic Visual Recognition. arXiv:1310.1531, 2013.
6. P. Felzenszwalb, D. McAllester, and D. Ramanan. A dis-criminatively trained, multiscale, deformable part model. In CVPR, 2008.
7. J. Gorski, F. Pfeuffer, and K. Klamroth. Biconvex sets and optimization with biconvex functions: a survey and extensions. Math. Meth. Oper. Res., 66(3):373-407, 2007.
8. T. Jaakkola, D. Haussler, et al. Exploiting generative models in discriminative classifiers. In NIPS, 1999.
9. M. Juneja, A. Vedaldi, C. Jawahar, and A. Zisserman. Blocks that shout: distinctive parts for scene classification. In CVPR, 2013.
10. Z. Kang, K. Grauman, and F. Sha. Learning with whom to share in multi-task feature learning. In ICML, 2011.
11. D. G. Lowe. Distinctive image features from scale-invariant keypoints. IJCV, 60(2):91-110, 2004.
12. J. Mairal, F. Bach, and J. Ponce. Task-driven dictionary learning. TPAMI, 34(4):791-804, 2012.
13. A. Maurer, M. Pontil, and B. Romera-Paredes. Sparse coding for multitask and transfer learning. In ICML, 2013.
14. B. Romera-Paredes, A. Argyriou, N. Berthouze, and M. Pon-til. Exploiting unrelated tasks in multi-task learning. In AIS-TATS, 2012.
15. J. Sánchez, F. Perronnin, T. Mensink, and J. Verbeek. Image classification with the Fisher vector: theory and practice. IJCV, pages 1-24, 2013.
16. S. Shalev-Shwartz and T. Zhang. Proximal Stochastic Dual Coordinate Ascent. arXiv:1211.2717, 2012.
17. S. Shalev-Shwartz and T. Zhang. Accelerated Proximal Stochastic Dual Coordinate Ascent for Regularized Loss Minimization. arXiv:1309.2375, 2013.
18. S. Shalev-Shwartz and T. Zhang. Stochastic dual coordinate ascent methods for regularized loss minimization. JMLR, 14:567-599, 2013.
19. Y. Su and F. Jurie. Improving image classification using semantic attributes. IJCV, 100(1):59-77, 2012.
20. A. Vedaldi and B. Fulkerson. VLFeat: An open and portable library of computer vision algorithms. http://www.vlfeat.org/, 2008.
21. J. Xiao, J. Hays, K. A. Ehinger, A. Oliva, and A. Torralba. SUN database: Large-scale scene recognition from abbey to zoo. In CVPR, 2010.
22. H. Zou and T. Hastie. Regularization and variable selection via the elastic net. J. R. Stat. Soc., 67(2):301-320, 2005.