T. G. Dietterich, R. H. Lathrop, and T. Lozano-pérez, Solving the multiple instance problem with axis-parallel rectangles, Artif. Intell, vol.89, issue.1-2, pp.31-71, 1997.

S. Andrews, I. Tsochantaridis, and T. Hofmann, Support vector machines for multiple-instance learning, Advances in Neural Information Processing Systems 15, pp.561-568, 2003.

Y. Chen and J. Z. Wang, Image categorization by learning and reasoning with regions, J. Mach. Learn. Res, vol.5, pp.913-939, 2004.

O. Maron and A. L. Ratan, Multiple-instance learning for natural scene classification, International Conference on Machine Learning (ICML), 1998.

T. Gärtner, P. A. Flach, A. Kowalczyk, and A. J. Smola, Multiinstance kernels, International Conf. on Machine Learning, 2002.

G. Krummenacher, C. S. Ong, and J. Buhmann, Ellipsoidal multiple instance learning, International Conference on Machine Learning (ICML), 2013.

R. C. Bunescu and R. J. Mooney, Multiple instance learning for sparse positive bags, International Conference on Machine Learning (ICML), 2007.

P. Gehler and O. Chapelle, Deterministic annealing for multipleinstance learning, Artificial Intelligence and Statistics, 2007.

T. Deselaers and V. Ferrari, A conditional random field for multipleinstance learning, International Conference on Machine Learning (ICML), 2010.

A. Joulin and F. Bach, A convex relaxation for weakly supervised classifiers, International Conference on Machine Learning (ICML), 2012.
URL : https://hal.archives-ouvertes.fr/hal-00717450

N. Quadrianto, A. Smola, T. Caetano, and Q. Le, Estimating labels from label proportions, Journal of Machine Learning Research, vol.10, pp.2349-2374, 2009.

S. Rueping, Svm classifier estimation from group probabilities, International Conference on Machine Learning (ICML), 2010.

F. X. Yu, D. Liu, S. Kumar, T. Jebara, and S. Chang, ?svm for learning with label proportions, International Conference on Machine Learning (ICML), 2013.

K. Lai, F. X. Yu, M. Chen, and S. Chang, Video event detection by inferring temporal instance labels, IEEE Computer Vision and Pattern Recognition (CVPR), 2014.

Z. Zhou, Y. Sun, and Y. Li, Multi-instance learning by treating instances as non-i.i.d. samples, International Conference on Machine Learning (ICML), 2009.

D. Zhang, J. He, L. Si, and R. D. Lawrence, Mileage: Multiple instance learning with global embedding, International Conference on Machine Learning (ICML), 2013.

P. F. Felzenszwalb, R. B. Girshick, D. A. Mcallester, and D. Ramanan, Object detection with discriminatively trained part-based models, IEEE Trans. Pattern Anal. Mach. Intell, 2010.

K. Miller, M. P. Kumar, B. Packer, D. Goodman, and D. Koller, Maxmargin min-entropy models, Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00773602

C. J. Yu and T. Joachims, Learning structural svms with latent variables, International Conference on Machine Learning (ICML), 2009.

S. Sabato and N. Tishby, Multi-instance learning with any hypothesis class, Journal of Machine Learning Research, 2012.

P. L. Bartlett and S. Mendelson, Rademacher and gaussian complexities: Risk bounds and structural results, J. Mach. Learn. Res, vol.3, 2003.

A. L. Yuille and A. Rangarajan, The concave-convex procedure, Neural Computation, vol.15, issue.4, pp.915-936, 2003.

B. K. Sriperumbudur and G. R. Lanckriet, On the convergence of the concave-convex procedure, NIPS, 2009.

L. Bottou, Large-scale machine learning with stochastic gradient descent, International Conference on Computational Statistics (COMPSTAT), 2010.

N. Le-roux, M. Schmidt, and F. Bach, A Stochastic Gradient Method with an Exponential Convergence Rate for Strongly-Convex Optimization with Finite Training Sets, NIPS, 2012.

T. Joachims, T. Finley, and C. Yu, Cutting-plane training of structural svms, Machine Learning, vol.77, pp.27-59, 2009.

G. Heitz, G. Elidan, B. Packer, and D. Koller, Shape-based object localization for descriptive classification, Int. J. Comput. Vision, vol.84, issue.1, 2009.

N. Dalal and B. Triggs, Histograms of oriented gradients for human detection, CVPR, 2005.
URL : https://hal.archives-ouvertes.fr/inria-00548512

C. Carson, S. Belongie, H. Greenspan, and J. Malik, Blobworld: Image segmentation using expectation-maximization and its application to image querying, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.24, pp.1026-1038, 1999.

Q. Zhang and S. A. Goldman, Em-dd: An improved multiple-instance learning technique, Advances in Neural Information Processing Systems, pp.1073-1080, 2001.

T. Gärtner, P. A. Flach, A. Kowalczyk, and A. J. Smola, Multi-instance kernels, International Conference on Machine Learning (ICML), 2002.

O. Mangasarian and E. Wild, Multiple instance classification via successive linear programming, Journal of Optimization Theory and Applications, vol.137, issue.3, 2008.

M. Kim, F. De-la, and T. , Multiple instance learning via gaussian processes, Data Mining and Knowledge Discovery, 2013.

W. Yang, Y. Wang, A. Vahdat, and G. Mori, Kernel latent svm for visual recognition, Advances in Neural Information Processing Systems (NIPS), 2012.

M. Everingham, L. Van-gool, C. K. Williams, J. Winn, and A. Zisserman, The PASCAL Visual Object Classes Challenge 2007 (VOC2007) Results, 2007.

A. Vedaldi and K. Lenc, Matconvnet-convolutional neural networks for matlab, CoRR, 2014.

M. S. Palmer and Z. Wu, Verb semantics for english-chinese translation, Machine Translation, vol.10, issue.1-2, pp.59-92, 1995.

, He received the Ph.D. in Computer Vision and Machine Learning from the University of Pierre et Marie Curie, France, 2017. He received an M.Sc. in Electrical Engineering by ENSEA, France, and an M.Sc. degrees in computer science from the, 2013.

, His research interests include machine learning for computer vision, including applications for semantic understanding of multimedia data. He is involved in several French (ANR), Conservatoire Nationnal des Arts et Métiers (Cnam Paris), 2008.