A. Behl, C. V. Jawahar, and M. P. Kumar, Optimizing average precision using weakly supervised data, CVPR, 2014.
URL : https://hal.archives-ouvertes.fr/hal-00984699

M. Blaschko, A. Mittal, and E. Rahtu, An $$\mathcal {O}(n \log n)$$ Cutting Plane Algorithm for Structured Output Ranking, GCPR, 2014.
DOI : 10.1007/978-3-319-11752-2_11

X. Boix, G. Roig, C. Leistner, and L. Van-gool, Nested Sparse Quantization for Efficient Feature Coding, ECCV. 2012
DOI : 10.1007/978-3-642-33709-3_53

J. Deng, W. Dong, R. Socher, L. Li, K. Li et al., ImageNet: A Large-Scale Hierarchical Image Database, CVPR, 2009.

P. Dokania, A. Behl, C. V. Jawahar, and M. P. Kumar, Learning to Rank Using High-Order Information, ECCV, 2014.
DOI : 10.1007/978-3-319-10593-2_40
URL : https://hal.archives-ouvertes.fr/hal-01076220

M. Everingham, L. Van-gool, C. Williams, J. Winn, and A. Zisserman, The PAS- CAL Visual Object Classes Challenge, 2007.

T. Ge, Q. Ke, and J. Sun, Sparse-Coded Features for Image Retrieval, Procedings of the British Machine Vision Conference 2013, 2013.
DOI : 10.5244/C.27.132

R. Girshick, J. Donahue, T. Darrell, and J. Malik, Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation, 2014 IEEE Conference on Computer Vision and Pattern Recognition, 2014.
DOI : 10.1109/CVPR.2014.81

M. Jaderberg, A. Vedaldi, and A. Zisserman, Speeding up Convolutional Neural Networks with Low Rank Expansions, Proceedings of the British Machine Vision Conference 2014, 2014.
DOI : 10.5244/C.28.88

D. Kim, Minimizing Structural Risk on Decision Tree Classification, Multi-Objective Machine Learning, 2006.
DOI : 10.1007/3-540-33019-4_11

S. Maji, L. Bourdev, and J. Malik, Action recognition from a distributed representation of pose and appearance, CVPR 2011, 2011.
DOI : 10.1109/CVPR.2011.5995631

C. Shen, H. Li, and N. Barnes, Totally corrective boosting for regularized risk minimization. arXiv preprint arXiv:1008, 2010.

C. Szegedy, A. Toshev, and D. Erhan, Deep neural networks for object detection, NIPS, 2013.

B. Taskar, C. Guestrin, and D. Koller, Max-margin Markov networks, NIPS, 2003.

I. Tsochantaridis, T. Hofmann, Y. Altun, and T. Joachims, Support vector machine learning for interdependent and structured output spaces, Twenty-first international conference on Machine learning , ICML '04, 2004.
DOI : 10.1145/1015330.1015341

J. Uijlings, K. Van-de-sande, T. Gevers, and A. Smeulders, Selective Search for Object Recognition, International Journal of Computer Vision, vol.57, issue.1, 2013.
DOI : 10.1007/s11263-013-0620-5

J. Yang, K. Yu, and T. Huang, Efficient Highly Over-Complete Sparse Coding Using a Mixture Model, ECCV, 2010.
DOI : 10.1007/978-3-642-15555-0_9

Y. Yue, T. Finley, F. Radlinski, and T. Joachims, A support vector method for optimizing average precision, Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval, SIGIR '07, 2007.
DOI : 10.1145/1277741.1277790