S. Andrews, I. Tsochantaridis, and T. Hofmann, Support Vector Machines for Multiple-Instance Learning, Advances in Neural Information Processing Systems (NIPS) (cit. on pp. 19, pp.33-45, 2003.

R. Arandjelovi´carandjelovi´c, P. Gronat, A. Torii, T. Pajdla, and J. Sivic, NetVLAD: CNN architecture for weakly supervised place recognition, IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (cit, p.16, 2016.

S. Avila, N. Thome, M. Cord, E. Valle, and A. Araujo, Pooling in image representation: The visual codeword point of view, Computer Vision and Image Understanding, vol.117, issue.5, 2012.
DOI : 10.1016/j.cviu.2012.09.007

URL : https://hal.archives-ouvertes.fr/hal-01172709

H. Azizpour, M. Arefiyan, S. Sobhan-naderi-parizi, and . Carlsson, Spotlight the Negatives: A Generalized Discriminative Latent Model, Procedings of the British Machine Vision Conference 2015, p.20, 2015.
DOI : 10.5244/C.29.18

H. Azizpour, A. Sharif-razavian, J. Sullivan, A. Maki, and S. Carlsson, Factors of Transferability for a Generic ConvNet Representation, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.38, issue.9, 2016.
DOI : 10.1109/TPAMI.2015.2500224

R. A. Baeza-yates and B. Ribeiro-neto, Modern Information Retrieval (cit, p.58, 1999.

P. L. Bartlett and S. Mendelson, Rademacher and Gaussian Complexities: Risk Bounds and Structural Results, Journal of Machine Learning Research (JMLR), 2003.
DOI : 10.1007/3-540-44581-1_15

URL : http://wwwmaths.anu.edu.au/~mendelso/papers/dt2.pdf

A. Bearman, O. Russakovsky, V. Ferrari, and L. Fei-fei, What???s the Point: Semantic Segmentation with Point Supervision, European Conference on Computer Vision (ECCV), 2016.
DOI : 10.1145/2647868.2654889

URL : http://arxiv.org/pdf/1506.02106

A. Behl, P. Mohapatra, C. V. Jawahar, and M. P. Kumar, Optimizing Average Precision Using Weakly Supervised Data, IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) (cit. on pp. 57, pp.69-70, 2015.
DOI : 10.1109/cvpr.2014.133

URL : https://hal.archives-ouvertes.fr/hal-00984699

D. Belanger and A. Mccallum, Structured Prediction Energy Networks, International Conference on Machine Learning (ICML) (cit, p.111, 2016.

S. Bell, C. L. Zitnick, K. Bala, and R. Girshick, Inside-Outside Net: Detecting Objects in Context with Skip Pooling and Recurrent Neural Networks, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), p.111, 2016.
DOI : 10.1109/CVPR.2016.314

URL : http://arxiv.org/pdf/1512.04143

A. J. Bency, H. Heesung-kwon, S. Lee, B. S. Karthikeyan, and . Manjunath, Weakly Supervised Localization Using Deep Feature Maps, European Conference on Computer Vision (ECCV) (cit, p.95, 2016.
DOI : 10.1109/CVPR.2015.7298621

URL : http://arxiv.org/pdf/1603.00489

Y. Bengio, P. Simard, and P. Frasconi, Learning long-term dependencies with gradient descent is difficult, IEEE Transactions on Neural Networks (cit, p.14, 1994.
DOI : 10.1109/72.279181

H. Bilen, V. P. Namboodiri, and L. J. Van-gool, Object and Action Classification with Latent Window Parameters, International Journal of Computer Vision, vol.15, issue.4, 2013.
DOI : 10.1109/CVPR.2010.5540096

B. Boser, B. E. Isabelle, M. Guyon, and V. N. Vapnik, A training algorithm for optimal margin classifiers, Proceedings of the fifth annual workshop on Computational learning theory , COLT '92, 1992.
DOI : 10.1145/130385.130401

L. Bossard, M. Guillaumin, and L. J. Van-gool, Food-101 ??? Mining Discriminative Components with Random Forests, European Conference on Computer Vision (ECCV) (cit, p.68, 2014.
DOI : 10.1007/978-3-319-10599-4_29

L. Bottou, Large-Scale Machine Learning with Stochastic Gradient Descent, Proceedings of the 19th International Conference on Computational Statistics (COMPSTAT) (cit, p.38, 2010.
DOI : 10.1201/b11429-4

URL : http://leon.bottou.org/publications/pdf/compstat-2010.pdf

Y. Boureau, J. -lan, Y. Ponce, and . Lecun, A theoretical analysis of feature pooling in vision algorithms, International Conference on Machine Learning (ICML), 2010.

L. Breiman, J. Friedman, J. Charles, . Stone, A. Richard et al., Classification and regression trees, 1984.

R. C. Bunescu and R. J. Mooney, Multiple instance learning for sparse positive bags, Proceedings of the 24th international conference on Machine learning, ICML '07, 2007.
DOI : 10.1145/1273496.1273510

URL : http://www.cs.utexas.edu/~razvan/papers/icml07.pdf

K. Chatfield, K. Simonyan, A. Vedaldi, and A. Zisserman, Return of the Devil in the Details: Delving Deep into Convolutional Nets, Proceedings of the British Machine Vision Conference 2014, pp.16-70, 2014.
DOI : 10.5244/C.28.6

. Chen, G. Liang-chieh, I. Papandreou, K. Kokkinos, A. L. Murphy et al., Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs, International Conference on Learning Representations (ICLR) (cit, pp.16-95, 2015.
DOI : 10.1109/tpami.2017.2699184

G. Chéron, I. Laptev, and C. Schmid, P-CNN: Pose-Based CNN Features for Action Recognition, 2015 IEEE International Conference on Computer Vision (ICCV), p.16, 2015.
DOI : 10.1109/ICCV.2015.368

R. Cinbis, J. Gokberk, C. Verbeek, and . Schmid, Weakly Supervised Object Localization with Multi-Fold Multiple Instance Learning, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.39, issue.1, 2016.
DOI : 10.1109/TPAMI.2016.2535231

URL : https://hal.archives-ouvertes.fr/hal-01123482

M. Cisse, M. Al-shedivat, and S. Bengio, ADIOS: Architectures Deep In Output Space, International Conference on Machine Learning (ICML) (cit, p.111, 2016.

T. Cover and P. Hart, Nearest neighbor pattern classification, IEEE Transactions on Information Theory, vol.13, issue.1, 1967.
DOI : 10.1109/TIT.1967.1053964

URL : http://ssg.mit.edu/cal/abs/2000_spring/np_dens/classification/cover67.pdf

J. Dai, K. He, Y. Li, S. Ren, and J. Sun, Instance-Sensitive Fully Convolutional Networks, European Conference on Computer Vision (ECCV) (cit, pp.16-91, 2016.
DOI : 10.1109/CVPR.2014.50

URL : http://arxiv.org/pdf/1603.08678

J. Dai, Y. Li, K. He, and J. Sun, R-FCN: Object Detection via Regionbased Fully Convolutional Networks, Advances in Neural Information Processing Systems (NIPS) (cit, pp.91-93, 2016.

N. Dalal and B. Triggs, Histograms of Oriented Gradients for Human Detection, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), p.42, 2005.
DOI : 10.1109/CVPR.2005.177

URL : https://hal.archives-ouvertes.fr/inria-00548512

T. Deselaers and V. Ferrari, A Conditional Random Field for Multiple- Instance Learning, International Conference on Machine Learning (ICML) (cit, pp.19-45, 2010.

A. Diba, A. M. Pazandeh, H. Pirsiavash, and L. Van-gool, Deep- CAMP: Deep Convolutional Action & Attribute Mid-Level Patterns, IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (cit, p.16, 2016.
DOI : 10.1109/cvpr.2016.387

URL : http://arxiv.org/pdf/1608.03217

A. Diba, V. Sharma, A. Pazandeh, H. Pirsiavash, and L. Van-gool, Weakly Supervised Cascaded Convolutional Networks, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.
DOI : 10.1109/CVPR.2017.545

T. G. Dietterich, H. Richard, T. Lathrop, and . Lozano-pérez, Solving the multiple instance problem with axis-parallel rectangles, Artificial Intelligence, vol.89, issue.1-2, 1997.
DOI : 10.1016/S0004-3702(96)00034-3

T. Do and T. Eres, Regularized Bundle Methods for Convex and Non-convex Risks, Journal of Machine Learning Research (JMLR), 2012.

C. Doersch, A. Gupta, and A. A. Efros, Mid-Level Visual Element Discovery as Discriminative Mode Seeking, Advances in Neural Information Processing Systems (NIPS) (cit, p.68, 2013.

L. Dong, A comparison of multi-instance learning algorithms, p.18, 2006.

A. Dosovitskiy, P. Fischer, E. Ilg, P. Hausser, C. Hazirbas et al., FlowNet: Learning Optical Flow with Convolutional Networks, 2015 IEEE International Conference on Computer Vision (ICCV), p.16, 2015.
DOI : 10.1109/ICCV.2015.316

J. C. Duchi, E. Hazan, and Y. Singer, Adaptive Subgradient Methods for Online Learning and Stochastic Optimization, Journal of Machine Learning Research (JMLR), 2011.

T. Durand, T. Mordan, N. Thome, and M. Cord, WILD- CAT: Weakly Supervised Learning of Deep ConvNets for Image Classification, Pointwise Localization and Segmentation, IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (cit. on pp. vii, p.90, 2017.

T. Durand, D. Picard, N. Thome, and M. Cord, Semantic pooling for image categorization using multiple kernel learning, 2014 IEEE International Conference on Image Processing (ICIP), 2014.
DOI : 10.1109/ICIP.2014.7025033

URL : https://hal.archives-ouvertes.fr/hal-01077046

T. Durand, N. Thome, and M. Cord, MANTRA: Minimum Maximum Latent Structural SVM for Image Classification and Ranking, 2015 IEEE International Conference on Computer Vision (ICCV), p.51, 2015.
DOI : 10.1109/ICCV.2015.311

URL : https://hal.archives-ouvertes.fr/hal-01343784

T. Durand, N. Thome, and M. Cord, WELDON: Weakly Supervised Learning of Deep Convolutional Neural Networks, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), p.73, 2016.
DOI : 10.1109/CVPR.2016.513

URL : https://hal.archives-ouvertes.fr/hal-01343785

T. Durand, N. Thome, and M. Cord, Negative Evidence for Weakly Supervised Learning of Deep Structured Models, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017.

T. Durand, N. Thome, and M. Cord, SyMIL: MinMax Latent SVM for Weakly Labeled Data, IEEE Transactions on Neural Networks and Learning Systems (TNNLS) [Submission], p.31, 2017.

T. Durand, N. Thome, M. Cord, and S. Avila, Image classification using object detectors, 2013 IEEE International Conference on Image Processing, 2013.
DOI : 10.1109/ICIP.2013.6738894

URL : https://hal.archives-ouvertes.fr/hal-01078079

T. Durand, N. Thome, M. Cord, and D. Picard, Incremental learning of latent structural SVM for weakly supervised image classification, 2014 IEEE International Conference on Image Processing (ICIP), 2014.
DOI : 10.1109/ICIP.2014.7025862

URL : https://hal.archives-ouvertes.fr/hal-01077058

M. Everingham, S. M. Eslami, L. Van-gool, C. K. Williams, J. Winn et al., The Pascal Visual Object Classes Challenge: A Retrospective, International Journal of Computer Vision, vol.34, issue.11, 2015.
DOI : 10.1109/TPAMI.2012.204

M. Everingham, L. Van-gool, C. K. Williams, J. Winn, and A. Zisserman, The Pascal Visual Object Classes (VOC) Challenge, International Journal of Computer Vision, vol.73, issue.2, pp.27-48, 2007.
DOI : 10.1371/journal.pcbi.0040027

URL : http://eprints.pascal-network.org/archive/00006961/01/everingham10.pdf

M. Everingham, L. Van-gool, C. K. Williams, J. Winn, and A. Zisserman, The PASCAL Visual Object Classes Challenge 2012 (VOC2012) Results, p.102, 2012.
DOI : 10.1007/s11263-014-0733-5

P. F. Felzenszwalb, B. Ross, D. Girshick, D. Mcallester, and . Ramanan, Object Detection with Discriminatively Trained Part-Based Models, IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) (cit. on pp. 19, pp.45-47, 2010.
DOI : 10.1109/TPAMI.2009.167

J. Fournier, M. Cord, and S. Philipp-foliguet, RETIN: A Content-Based Image Indexing and Retrieval System, Pattern Analysis & Applications, vol.4, issue.2-3, 2001.
DOI : 10.1007/PL00014576

URL : http://www-etis.ensea.fr/~cord/perso/paa.ps.gz

A. Fukui, D. H. Park, D. Yang, A. Rohrbach, T. Darrell et al., Multimodal Compact Bilinear Pooling for Visual Question Answering and Visual Grounding, Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, 2016.
DOI : 10.18653/v1/D16-1044

K. Fukushima, Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position, Biological Cybernetics, vol.40, issue.4, 1980.
DOI : 10.1007/BF00344251

Y. Gao, O. Beijbom, N. Zhang, and T. Darrell, Compact Bilinear Pooling, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), p.99, 2016.
DOI : 10.1109/CVPR.2016.41

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

P. Gehler and O. Chapelle, Deterministic Annealing for Multiple-Instance Learning, International Conference on Artificial Intelligence and Statistics (AISTAT) (cit, pp.19-45, 2007.

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, pp.16-75, 2014.
DOI : 10.1109/CVPR.2014.81

URL : http://www.cs.berkeley.edu/%7Erbg/papers/r-cnn-cvpr.pdf

R. Girshick, F. Iandola, T. Darrell, and J. Malik, Deformable part models are convolutional neural networks, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), p.92, 2015.
DOI : 10.1109/CVPR.2015.7298641

URL : http://www.cs.berkeley.edu/%7Erbg/papers/cvpr15/dpdpm.pdf

G. Gkioxari, R. Girshick, and J. Malik, Contextual Action Recognition with R*CNN, 2015 IEEE International Conference on Computer Vision (ICCV), p.16, 2015.
DOI : 10.1109/ICCV.2015.129

URL : http://arxiv.org/pdf/1505.01197

X. Glorot and Y. Bengio, Understanding the difficulty of training deep feedforward neural networks, International Conference on Artificial Intelligence and Statistics (AISTAT) (cit, p.14, 2010.

Y. Gong, L. Wang, R. Guo, and S. Lazebnik, Multi-scale Orderless Pooling of Deep Convolutional Activation Features, European Conference on Computer Vision (ECCV) (cit. on pp. 16, pp.69-86, 2014.
DOI : 10.1007/978-3-319-10584-0_26

H. Hajimirsadeghi and G. Mori, Multi-Instance Classification by Max-Margin Training of Cardinality-Based Markov Networks, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.39, issue.9, 2016.
DOI : 10.1109/TPAMI.2016.2613865

. Hariharan, P. Bharath, L. D. Arbelaez, S. Bourdev, J. Maji et al., Semantic contours from inverse detectors, 2011 International Conference on Computer Vision, 2011.
DOI : 10.1109/ICCV.2011.6126343

URL : http://www.eecs.berkeley.edu/Research/Projects/CS/vision/grouping/papers/habmm_iccv2011.pdf

. Hariharan, P. Bharath, R. Arbeláez, J. Girshick, and . Malik, Hypercolumns for object segmentation and fine-grained localization, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), p.111, 2015.
DOI : 10.1109/CVPR.2015.7298642

URL : http://www.cs.berkeley.edu/%7Ebharath2/pubs/pdfs/BharathCVPR2015.pdf

K. He, X. Zhang, S. Ren, and J. Sun, Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition, European Conference on Computer Vision (ECCV) (cit. on pp. 16, pp.71-75, 2014.

K. He, X. Zhang, S. Ren, and J. Sun, Deep Residual Learning for Image Recognition, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.90-91, 2016.
DOI : 10.1109/CVPR.2016.90

URL : http://arxiv.org/pdf/1512.03385

G. Heitz, G. Elidan, B. Packer, and D. Koller, Shape-Based Object Localization for Descriptive Classification, International Journal of Computer Vision, vol.26, issue.5, 2009.
DOI : 10.1017/CBO9780511804441

URL : http://books.nips.cc/papers/files/nips21/NIPS2008_0367.pdf

S. Huang, Z. Xu, D. Tao, and Y. Zhang, Part-Stacked CNN for Fine-Grained Visual Categorization, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), p.16, 2016.
DOI : 10.1109/CVPR.2016.132

URL : http://arxiv.org/pdf/1512.08086

D. H. Hubel, N. Torsten, and . Wiesel, Receptive fields, binocular interaction and functional architecture in the cat's visual cortex, The Journal of Physiology, vol.160, issue.1, 1962.
DOI : 10.1113/jphysiol.1962.sp006837

S. Ioffe and C. Szegedy, Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift, International Conference on Machine Learning (ICML) (cit, p.14, 2015.

H. Jégou, M. Douze, C. Schmid, and P. Pérez, Aggregating local descriptors into a compact image representation, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2010.
DOI : 10.1109/CVPR.2010.5540039

Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long et al., Caffe, Proceedings of the ACM International Conference on Multimedia, MM '14, pp.61-68, 2014.
DOI : 10.1145/2647868.2654889

T. Joachims, T. Finley, and C. Yu, Cutting-plane training of structural SVMs, Machine Learning (cit, pp.24-40, 2009.
DOI : 10.1007/s10994-009-5108-8

J. Johnson, A. Karpathy, and L. Fei-fei, DenseCap: Fully Convolutional Localization Networks for Dense Captioning, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), p.111, 2016.
DOI : 10.1109/CVPR.2016.494

URL : http://arxiv.org/pdf/1511.07571

A. Joulin and F. Bach, A convex relaxation for weakly supervised classifiers, International Conference on Machine Learning (ICML) (cit, pp.19-45, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00717450

M. Juneja, A. Vedaldi, C. V. Jawahar, and A. Zisserman, Blocks That Shout: Distinctive Parts for Scene Classification, 2013 IEEE Conference on Computer Vision and Pattern Recognition, p.68, 2013.
DOI : 10.1109/CVPR.2013.124

URL : http://cvit.iiit.ac.in/papers/Juneja2013Blocks.pdf

M. Kim and F. De-la-torre, Multiple instance learning via Gaussian processes, DMKD) (cit, p.45, 2013.
DOI : 10.1109/TNN.2011.2109011

D. P. Kingma and J. Ba, Adam: A Method for Stochastic Optimization, International Conference on Learning Representations (ICLR), 2014.

A. Kolesnikov and C. H. Lampert, Seed, Expand and Constrain: Three Principles for Weakly-Supervised Image Segmentation, European Conference on Computer Vision (ECCV) (cit, p.27, 2016.
DOI : 10.1109/CVPR.2015.7298888

URL : http://arxiv.org/pdf/1603.06098

P. Krähenbkrähenb¨krähenbühl and V. Koltun, Efficient Inference in Fully Connected CRFs with Gaussian Edge Potentials, Advances in Neural Information Processing Systems (NIPS), 2011.

A. Krizhevsky, I. Sutskever, and G. Hinton, ImageNet classification with deep convolutional neural networks, Advances in Neural Information Processing Systems (NIPS) (cit. on pp. 2, pp.14-16, 2012.
DOI : 10.1162/neco.2009.10-08-881

URL : http://dl.acm.org/ft_gateway.cfm?id=3065386&type=pdf

G. Krummenacher, C. S. Ong, and J. Buhmann, Ellipsoidal Multiple Instance Learning, International Conference on Machine Learning (ICML) (cit, p.45, 2013.

P. Kulkarni, F. Jurie, J. Zepeda, P. Pérez, and L. Chevallier, SPLeaP: Soft Pooling of Learned Parts for Image Classification, European Conference on Computer Vision (ECCV) (cit, p.99, 2016.
DOI : 10.1109/CVPR.2013.90

URL : https://hal.archives-ouvertes.fr/hal-01350562

P. Kulkarni, J. Zepeda, F. Jurie, P. Pérez, and L. Chevallier, Learning the Structure of Deep Architectures Using L1 Regularization, Procedings of the British Machine Vision Conference 2015, p.15, 2015.
DOI : 10.5244/C.29.23

URL : https://hal.archives-ouvertes.fr/hal-01266462

. Lacoste-julien, M. Simon, M. Jaggi, P. Schmidt, and . Pletscher, Block- Coordinate Frank-Wolfe Optimization for Structural SVMs, International Conference on Machine Learning (ICML) (cit, p.24, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00720158

J. Lafferty, A. Mccallum, and F. Pereira, Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data, International Conference on Machine Learning (ICML) (cit, p.25, 2001.

. Lai, F. X. Kuan-ting, M. Yu, S. Chen, and . Chang, Video Event Detection by Inferring Temporal Instance Labels, 2014 IEEE Conference on Computer Vision and Pattern Recognition, p.113, 2014.
DOI : 10.1109/CVPR.2014.288

URL : http://www.ee.columbia.edu/ln/dvmm/publications/14/cvpr14event.pdf

S. Lazebnik, C. Schmid, and J. Ponce, Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Volume 2 (CVPR'06), pp.28-68, 2006.
DOI : 10.1109/CVPR.2006.68

URL : https://hal.archives-ouvertes.fr/inria-00548585

L. Roux, M. Nicolas, F. Schmidt, and . Bach, A Stochastic Gradient Method with an Exponential Convergence Rate for Strongly-Convex Optimization with Finite Training Sets, Advances in Neural Information Processing Systems (NIPS) (cit, p.38, 2012.

Y. Lecun, B. Boser, J. S. Denker, D. Henderson, R. E. Howard et al., Backpropagation Applied to Handwritten Zip Code Recognition, Neural Computation, vol.1, issue.4, 1989.
DOI : 10.1007/BF00133697

Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner, Gradient-based learning applied to document recognition, Proceedings of the IEEE, 1998.
DOI : 10.1109/5.726791

H. Li, Y. Li, and F. Porikli, DeepTrack: Learning Discriminative Feature Representations Online for Robust Visual Tracking, IEEE Transactions on Image Processing, vol.25, issue.4, 2016.
DOI : 10.1109/TIP.2015.2510583

URL : http://arxiv.org/pdf/1503.00072

L. Li and F. Li, What, where and who? Classifying events by scene and object recognition, 2007 IEEE 11th International Conference on Computer Vision, p.28, 2007.
DOI : 10.1109/ICCV.2007.4408872

L. Li, H. Su, Y. Lim, and L. Fei-fei, Object Bank: An Object-Level Image Representation for High-Level Visual Recognition, International Journal of Computer Vision, vol.19, issue.2, pp.70-95, 2014.
DOI : 10.1007/s11263-005-6642-x

W. Li and N. Vasconcelos, Multiple instance learning for soft bags via top instances, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), p.94, 2015.
DOI : 10.1109/CVPR.2015.7299056

T. Lin, P. Dollár, R. Girshick, K. He, B. Hariharan et al., Feature Pyramid Networks for Object Detection, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), p.111, 2017.
DOI : 10.1109/CVPR.2017.106

S. Lloyd, Least squares quantization in PCM, IEEE Transactions on Information Theory, vol.28, issue.2, 1982.
DOI : 10.1109/TIT.1982.1056489

URL : http://www.cs.toronto.edu/~roweis/csc2515-2006/readings/lloyd57.pdf

H. Lobel, R. Vidal, and A. Soto, Hierarchical Joint Max-Margin Learning of Mid and Top Level Representations for Visual Recognition, 2013 IEEE International Conference on Computer Vision, p.68, 2013.
DOI : 10.1109/ICCV.2013.213

J. Long, E. Shelhamer, and T. Darrell, Fully convolutional networks for semantic segmentation, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.75-91, 2015.
DOI : 10.1109/CVPR.2015.7298965

D. G. Lowe, Distinctive Image Features from Scale-Invariant Keypoints, International Journal of Computer Vision, vol.60, issue.2, 2004.
DOI : 10.1023/B:VISI.0000029664.99615.94

URL : http://www.cs.ubc.ca/~lowe/papers/ijcv03.ps

W. Ma and B. S. Manjunath, NeTra: A toolbox for navigating large image databases, Multimedia Systems, 1999.
DOI : 10.1007/s005300050121

URL : http://www-iplab.ece.ucsb.edu/publications/99ACMNeTra.pdf

O. L. Mangasarian and E. W. Wild, Multiple Instance Classification via??Successive??Linear??Programming, Journal of Optimization Theory and Applications, vol.7, issue.1, 2008.
DOI : 10.1007/978-1-4757-3264-1

URL : http://ftp.cs.wisc.edu/pub/dmi/tech-reports/05-02.pdf

K. Miller, M. P. Kumar, B. Packer, D. Goodman, and D. Koller, Max-Margin Min-Entropy Models, International Conference on Artificial Intelligence and Statistics (AISTAT) (cit, pp.42-47, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00773602

P. Mohapatra, C. V. Jawahar, and M. P. Kumar, Efficient Optimization for Average Precision SVM, Advances in Neural Information Processing Systems (NIPS), 2014.
URL : https://hal.archives-ouvertes.fr/hal-01069917

M. Oquab, L. Bottou, I. Laptev, and J. Sivic, Learning and Transferring Mid-level Image Representations Using Convolutional Neural Networks, 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp.17-71, 2014.
DOI : 10.1109/CVPR.2014.222

URL : https://hal.archives-ouvertes.fr/hal-00911179

M. Oquab, L. Bottou, I. Laptev, and J. Sivic, Is object localization for free? - Weakly-supervised learning with convolutional neural networks, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.78-85, 2015.
DOI : 10.1109/CVPR.2015.7298668

URL : https://hal.archives-ouvertes.fr/hal-01015140

M. Palmer, Z. Stone, and . Wu, Verb semantics for English-Chinese translation, Machine Translation, 1995.
DOI : 10.1007/BF00997232

G. Papandreou, L. Chen, K. Murphy, and A. L. Yuille, Weakly-and semi-supervised learning of a DCNN for semantic image segmentation, IEEE International Conference on Computer Vision (ICCV), 2015.

G. Papandreou, I. Kokkinos, and P. Savalle, Modeling local and global deformations in Deep Learning: Epitomic convolution, Multiple Instance Learning, and sliding window detection, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), p.26, 2015.
DOI : 10.1109/CVPR.2015.7298636

URL : https://hal.archives-ouvertes.fr/hal-01263611

S. Parizi, . Naderi, G. John, P. F. Oberlin, and . Felzenszwalb, Reconfigurable models for scene recognition, 2012 IEEE Conference on Computer Vision and Pattern Recognition, p.68, 2012.
DOI : 10.1109/CVPR.2012.6248001

URL : http://www.cs.brown.edu/%7Epff/papers/latent-scene.pdf

S. Parizi, A. Naderi, A. Vedaldi, P. F. Zisserman, and . Felzenszwalb, Automatic discovery and optimization of parts for image classification, International Conference on Learning Representations (ICLR) (cit. on pp. 19, pp.86-94, 2015.

D. Pathak, P. Krahenbuhl, and T. Darrell, Constrained Convolutional Neural Networks for Weakly Supervised Segmentation, 2015 IEEE International Conference on Computer Vision (ICCV), p.95, 2015.
DOI : 10.1109/ICCV.2015.209

URL : http://arxiv.org/pdf/1506.03648

D. Pathak, E. Shelhamer, J. Long, and T. Darrell, Fully Convolutional Multi-Class Multiple Instance Learning, International Conference on Learning Representations Workshop (ICLR-W) (cit, p.26, 2015.

M. Paulin, M. Douze, Z. Harchaoui, J. Mairal, F. Perronin et al., Local Convolutional Features with Unsupervised Training Bibliography for Image Retrieval, IEEE International Conference on Computer Vision (ICCV) (cit, p.16, 2015.
DOI : 10.1109/iccv.2015.19

URL : https://hal.inria.fr/hal-01207966/file/deep_patches.pdf

F. Perronnin and C. Dance, Fisher Kernels on Visual Vocabularies for Image Categorization, 2007 IEEE Conference on Computer Vision and Pattern Recognition, 2007.
DOI : 10.1109/CVPR.2007.383266

URL : http://www.xrce.xerox.com/Publications/Attachments/2006-034/2006-034.pdf

D. Picard and P. Gosselin, Improving image similarity with vectors of locally aggregated tensors, 2011 18th IEEE International Conference on Image Processing, 2011.
DOI : 10.1109/ICIP.2011.6116641

URL : https://hal.archives-ouvertes.fr/hal-00591993

W. Ping, Q. Liu, and A. Ihler, Marginal Structured SVM with Hidden Variables, International Conference on Machine Learning (ICML) (cit, p.24, 2014.

P. O. Pinheiro and R. Collobert, From image-level to pixel-level labeling with Convolutional Networks, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), p.26, 2015.
DOI : 10.1109/CVPR.2015.7298780

URL : http://ronan.collobert.com/pub/matos/2015_semisupsemseg_cvpr.pdf

P. O. Pinheiro, R. Collobert, and P. Dollar, Learning to Segment Object Candidates, Advances in Neural Information Processing Systems (NIPS) (cit, p.16, 2015.

P. O. Pinheiro, R. Tsung-yi-lin, and . Collobert, Learning to Refine Object Segments, European Conference on Computer Vision (ECCV) (cit, p.16, 2016.
DOI : 10.5244/C.30.15

URL : https://infoscience.epfl.ch/record/224543/files/Pinheiro_ECCV_2016.pdf

P. Pletscher, C. S. Ong, and J. M. Buhmann, Entropy and Margin Maximization for Structured Output Learning, European Conference on Machine Learning (ECML) (cit, p.25, 2010.
DOI : 10.1007/978-3-642-15939-8_6

URL : http://www.pletscher.org/papers/pletscher2010maxentmarg.pdf

B. Polyak and . Teodorovich, Some methods of speeding up the convergence of iteration methods, USSR Computational Mathematics and Mathematical Physics, vol.4, issue.5, 1964.
DOI : 10.1016/0041-5553(64)90137-5

A. Quattoni and A. Torralba, Recognizing indoor scenes, 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp.28-104, 2009.
DOI : 10.1109/CVPR.2009.5206537

A. Quattoni, L. Sy-bor-wang, M. Morency, T. Collins, and . Darrell, Hidden Conditional Random Fields, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.29, issue.10, 2007.
DOI : 10.1109/TPAMI.2007.1124

A. Razavian, H. Azizpour, J. Sullivan, and S. Carlsson, CNN Features Off-the-Shelf: An Astounding Baseline for Recognition, 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops, p.15, 2014.
DOI : 10.1109/CVPRW.2014.131

J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, You Only Look Once: Unified, Real-Time Object Detection, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), p.16, 2016.
DOI : 10.1109/CVPR.2016.91

S. Reed, Z. Akata, B. Schiele, and H. Lee, Learning Deep Representations of Fine-Grained Visual Descriptions, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.
DOI : 10.1109/CVPR.2016.13

S. Ren, K. He, R. Girshick, and J. Sun, Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks, Advances in Neural Information Processing Systems (NIPS) (cit, p.16, 2015.
DOI : 10.1109/TPAMI.2016.2577031

S. Rueping, SVM Classifier Estimation from Group Probabilities, International Conference on Machine Learning (ICML), 2010.

D. E. Bibliography-rumelhart, G. E. Hinton, and R. J. Williams, Learning representations by back-propagating errors, Nature, vol.11, issue.13, 1986.

L. Berg and . Fei-fei, ImageNet Large Scale Visual Recognition Challenge, International Journal of Computer Vision (IJCV), vol.2, issue.100, pp.61-81, 2015.

O. Russakovsky, Y. Lin, K. Yu, and L. Fei-fei, Object-Centric Spatial Pooling for Image Classification, European Conference on Computer Vision (ECCV), 2012.
DOI : 10.1007/978-3-642-33709-3_1

S. Sabato and N. Tishby, Multi-Instance Learning with Any Hypothesis Class, Journal of Machine Learning Research (JMLR), 2012.

F. Sadeghi, F. Marshall, and . Tappen, Latent Pyramidal Regions for Recognizing Scenes, European Conference on Computer Vision (ECCV) (cit, p.68, 2012.
DOI : 10.1007/978-3-642-33715-4_17

URL : http://www.eecs.ucf.edu/~mtappen/pubs/2012/sadeghi-tappen-eccv-2012.pdf

G. Salton and M. J. Mcgill, Introduction to Modern Information Retrieval, 1986.

S. Saxena and J. Verbeek, Convolutional Neural Fabrics, Advances in Neural Information Processing Systems (NIPS) (cit, p.15, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01359150

A. G. Schwing, T. Hazan, M. Pollefeys, and R. Urtasun, Distributed message passing for large scale graphical models, CVPR 2011, 2011.
DOI : 10.1109/CVPR.2011.5995642

A. G. Schwing, T. Hazan, M. Pollefeys, and R. Urtasun, Efficient Structured Prediction with Latent Variables for General Graphical Models, International Conference on Machine Learning (ICML) (cit, p.22, 2012.

P. Sermanet, D. Eigen, X. Zhang, M. Mathieu, R. Fergus et al., Overfeat: Integrated recognition, localization and detection using convolutional networks, International Conference on Learning Representations (ICLR) (cit, p.13, 2014.

N. Shah, V. Kolmogorov, and C. H. Lampert, A multi-plane block-coordinate frank-wolfe algorithm for training structural SVMs with a costly max-oracle, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), p.24, 2015.
DOI : 10.1109/CVPR.2015.7298890

. Shalev-shwartz, Y. Shai, N. Singer, A. Srebro, and . Cotter, Pegasos, Proceedings of the 24th international conference on Machine learning, ICML '07, 2011.
DOI : 10.1145/1273496.1273598

G. Sharma, F. Jurie, and C. Schmid, Discriminative spatial saliency for image classification, 2012 IEEE Conference on Computer Vision and Pattern Recognition, p.68, 2012.
DOI : 10.1109/CVPR.2012.6248093

URL : https://hal.archives-ouvertes.fr/hal-00714311

M. Simon and E. Rodner, Neural Activation Constellations: Unsupervised Part Model Discovery with Convolutional Networks, 2015 IEEE International Conference on Computer Vision (ICCV), 2015.
DOI : 10.1109/ICCV.2015.136

K. Simonyan and A. Zisserman, Very Deep Convolutional Networks for Large-Scale Image Recognition, International Conference on Learning Representations (ICLR) (cit, pp.75-85, 2015.

J. Sivic and A. Zisserman, Video Google: a text retrieval approach to object matching in videos, Proceedings Ninth IEEE International Conference on Computer Vision, 2003.
DOI : 10.1109/ICCV.2003.1238663

B. Smeulders, A. W. , M. Worring, S. Santini, A. Gupta et al., Content-based image retrieval at the end of the early years, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.22, issue.12, 2000.
DOI : 10.1109/34.895972

Y. Song, A. G. Schwing, R. S. Zemel, and R. Urtasun, Training Deep Neural Networks via Direct Loss Minimization, International Conference on Machine Learning (ICML), 2016.

B. K. Sriperumbudur, R. G. Gert, and . Lanckriet, On the Convergence of the Concave-Convex Procedure, Advances in Neural Information Processing Systems (NIPS) (cit, p.37, 2009.

R. K. Srivastava, K. Greff, and J. Schmidhuber, Training Very Deep Networks, Advances in Neural Information Processing Systems (NIPS) (cit, p.15, 2015.

C. Sun, M. Paluri, R. Collobert, R. Nevatia, and L. Bourdev, ProNet: Learning to Propose Object-Specific Boxes for Cascaded Neural Networks, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.95-98, 2016.
DOI : 10.1109/CVPR.2016.379

J. Sun and J. Ponce, Learning Discriminative Part Detectors for Image Classification and Cosegmentation, 2013 IEEE International Conference on Computer Vision, p.68, 2013.
DOI : 10.1109/ICCV.2013.422

URL : https://hal.archives-ouvertes.fr/hal-00932380

I. Sutskever, J. Martens, G. Dahl, and G. Hinton, On the importance of initialization and momentum in deep learning, International Conference on Machine Learning (ICML), 2013.

C. Szegedy, S. Ioffe, V. Vanhoucke, and A. Alemi, Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning, 2016.

C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed et al., Going deeper with convolutions, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.13-16, 2015.
DOI : 10.1109/CVPR.2015.7298594

M. Szummer, P. Kohli, and D. Hoiem, Learning CRFs Using Graph Cuts, European Conference on Computer Vision (ECCV), 2008.
DOI : 10.1007/11744047_3

URL : http://www.cs.cmu.edu/~dhoiem/publications/eccv08GraphcutCRFs.pdf

B. Taskar, C. Guestrin, and D. Koller, Max-Margin Markov Networks, Advances in Neural Information Processing Systems (NIPS) (cit, p.22, 2003.

E. Teh, M. Wern, Y. Rochan, and . Wang, Attention Networks for Weakly Supervised Object Localization, Procedings of the British Machine Vision Conference 2016, p.27, 2016.
DOI : 10.5244/C.30.52

T. Tieleman and G. Hinton, RMSprop Gradient Optimization, 2012.

A. Toshev and C. Szegedy, DeepPose: Human Pose Estimation via Deep Neural Networks, 2014 IEEE Conference on Computer Vision and Pattern Recognition, p.16, 2014.
DOI : 10.1109/CVPR.2014.214

URL : http://arxiv.org/pdf/1312.4659

I. Tsochantaridis, T. Joachims, T. Hofmann, and Y. Altun, Large Margin Methods for Structured and Interdependent Output Variables, Journal of Machine Learning Research (JMLR), vol.24, p.55, 2005.

V. Vapnik, Principles of risk minimization for learning theory, Advances in Neural Information Processing Systems (NIPS) (cit, p.12, 1991.

B. Vapnik and V. , The Nature of Statistical Learning Theory, 1995.

C. Wah, S. Branson, P. Welinder, P. Perona, and S. Belongie, The Caltech-UCSD Birds-200-2011 Dataset, p.28, 2011.

H. Wang, F. Nie, and H. Huang, Robust and discriminative distance for multi-instance learning, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2012.

J. Wang and J. Zucker, Solving the Multiple-Instance Problem: A Lazy Learning Approach, International Conference on Machine Learning (ICML), 2000.

L. Wang, W. Ouyang, X. Wang, and H. Lu, Visual Tracking with Fully Convolutional Networks, 2015 IEEE International Conference on Computer Vision (ICCV), p.16, 2015.
DOI : 10.1109/ICCV.2015.357

X. Wang, D. Kumar, N. Thome, M. Cord, and F. Precioso, Recipe recognition with large multimodal food dataset, IEEE International Conference on Multimedia Expo Workshops (ICMEW) (cit, pp.2015-2031, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01196959

X. Wang, B. Wang, X. Bai, W. Liu, and Z. Tu, Max- Margin Multiple-Instance Dictionary Learning, International Conference on Machine Learning (ICML) (cit, p.68, 2013.

Y. Wang, J. Choi, V. Morariu, and L. S. Davis, Mining Discriminative Triplets of Patches for Fine-Grained Classification, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), p.16, 2016.
DOI : 10.1109/CVPR.2016.131

Y. Wei, W. Xia, J. Huang, B. Ni, J. Dong et al., CNN: Single-label to Multi-label, 2014.

Z. Wei and M. Hoai, Region Ranking SVM for Image Classification, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.98-101, 2016.
DOI : 10.1109/CVPR.2016.326

R. Wu, B. Wang, W. Wang, and Y. Yu, Harvesting Discriminative Meta Objects with Deep CNN Features for Scene Classification, 2015 IEEE International Conference on Computer Vision (ICCV), p.99, 2015.
DOI : 10.1109/ICCV.2015.152

URL : http://arxiv.org/pdf/1510.01440

T. Xiao, Y. Xu, K. Yang, and J. Zhang, The Application of Two-Level Attention Models in Deep Convolutional Neural Network for Fine-Grained Image Classification, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015.

S. Xie, R. Girshick, P. Dollár, Z. Tu, and K. He, Aggregated Residual Transformations for Deep Neural Networks, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.
DOI : 10.1109/CVPR.2017.634

H. Xu and K. Saenko, Ask, Attend and Answer: Exploring Question-Guided Spatial Attention for Visual Question Answering, European Conference on Computer Vision (ECCV) (cit, p.16, 2016.
DOI : 10.1007/978-3-642-33715-4_54

URL : http://arxiv.org/pdf/1511.05234

J. Xu, A. G. Schwing, and R. Urtasun, Tell Me What You See and I Will Show You Where It Is, 2014 IEEE Conference on Computer Vision and Pattern Recognition, p.22, 2014.
DOI : 10.1109/CVPR.2014.408

URL : http://pages.cs.wisc.edu/%7Ejiaxu/projects/weak-label-seg/weak-label-seg-cvpr2014.pdf

Z. Xu, D. Tao, Y. Zhang, J. Wu, and A. C. Tsoi, Architectural Style Classification Using Multinomial Latent Logistic Regression, European Conference on Computer Vision (ECCV) (cit, p.25, 2014.
DOI : 10.1007/978-3-319-10590-1_39

J. Yang, B. L. Price, S. Cohen, H. Lee, and M. Yang, Object Contour Detection with a Fully Convolutional Encoder-Decoder Network, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), p.16, 2016.
DOI : 10.1109/CVPR.2016.28

W. Yang, Y. Wang, A. Vahdat, and G. Mori, Kernel Latent SVM for Visual Recognition, Advances in Neural Information Processing Systems (NIPS) (cit, pp.47-48, 2012.

B. Yao and L. Fei-fei, Grouplet: A structured image representation for recognizing human and object interactions, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, p.28, 2010.
DOI : 10.1109/CVPR.2010.5540234

C. Yu and T. Joachims, Learning structural SVMs with latent variables, Proceedings of the 26th Annual International Conference on Machine Learning, ICML '09, pp.61-62, 2009.
DOI : 10.1145/1553374.1553523

F. X. Yu, D. Liu, S. Kumar, T. Jebara, and S. Chang, ?SVM for Learning with Label Proportions, International Conference on Machine Learning (ICML) (cit, pp.94-113, 2013.

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, pp.60-80, 2007.
DOI : 10.1145/1277741.1277790

URL : http://radlinski.org/papers/YueEtAl_SIGIR2007.pdf

A. L. Yuille and A. Rangarajan, The Concave-Convex Procedure, Neural Computation (cit, pp.37-55, 2003.
DOI : 10.1162/08997660260028674

S. Zagoruyko, A. Lerer, T. Lin, P. O. Pinheiro, S. Gross et al., A MultiPath Network for Object Detection, Procedings of the British Machine Vision Conference 2016, p.16, 2016.
DOI : 10.5244/C.30.15

M. D. Zeiler, ADADELTA: An Adaptive Learning Rate Method, 2012.

M. D. Zeiler and R. Fergus, Visualizing and Understanding Convolutional Networks, European Conference on Computer Vision (ECCV), 2014.
DOI : 10.1007/978-3-319-10590-1_53

URL : http://cs.nyu.edu/%7Efergus/papers/zeilerECCV2014.pdf

M. Zeiler and R. Fergus, Stochastic pooling for regularization of deep convolutional neural networks, International Conference on Learning Representations (ICLR) (cit, p.16, 2013.

D. Zhang, J. He, L. Si, and R. D. Lawrence, MILEAGE: Multiple Instance LEArning with Global Embedding, International Conference on Machine Learning (ICML) (cit, p.45, 2013.

N. Zhang, M. Paluri, T. Marc-'aurelio-ranzato, L. Darrell, and . Bourdev, PANDA: Pose Aligned Networks for Deep Attribute Modeling, 2014 IEEE Conference on Computer Vision and Pattern Recognition, p.69, 2014.
DOI : 10.1109/CVPR.2014.212

URL : http://arxiv.org/pdf/1311.5591

Z. Zhang, S. Fidler, and R. Urtasun, Instance-Level Segmentation with Deep Densely Connected MRFs, 2016.
DOI : 10.1109/cvpr.2016.79

URL : http://arxiv.org/pdf/1512.06735

B. Zhou, A. Lapedriza, J. Xiao, A. Torralba, and A. Oliva, Learning Deep Features for Scene Recognition using Places Database, Advances in Neural Information Processing Systems (NIPS) (cit. on pp. 16, pp.61-68, 2014.
DOI : 10.1109/tpami.2017.2723009

B. Zhou, A. Khosla, A. Lapedriza, A. Oliva, and A. Torralba, Learning Deep Features for Discriminative Localization, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.93-96, 2016.
DOI : 10.1109/CVPR.2016.319

URL : http://arxiv.org/pdf/1512.04150

. Zhou, H. Bolei, X. Zhao, S. Puig, A. Fidler et al., Semantic understanding of scenes through the ade20k dataset, 2016.

F. Zhou and Y. Lin, Fine-Grained Image Classification by Exploring Bipartite-Graph Labels, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.
DOI : 10.1109/CVPR.2016.127

X. Zhou, K. Yu, T. Zhang, and T. Huang, Image Classification Using Super-Vector Coding of Local Image Descriptors, European Conference on Computer Vision (ECCV), 2010.
DOI : 10.1007/978-3-642-15555-0_11

. Zhou, Y. Zhi-hua, Y. Sun, and . Li, Multi-instance learning by treating instances as non-I.I.D. samples, Proceedings of the 26th Annual International Conference on Machine Learning, ICML '09, p.46, 2009.
DOI : 10.1145/1553374.1553534

Z. Zuo, G. Wang, B. Shuai, L. Zhao, Q. Yang et al., Learning Discriminative and Shareable Features for Scene Classification, European Conference on Computer Vision (ECCV) (cit, p.68, 2014.
DOI : 10.1007/978-3-319-10590-1_36

S. Bibliography and . Scale, Invariant Feature Transform SGD Stochastic Gradient Descent SPEN Structured Prediction Energy Network SPM Spatial Pyramid Matching SSVM Structured SVM STN Spatial Transformer Network SVC Super-Vector Coding SVM Support Vector Machine VLAD Vector of Locally Aggregated Descriptors VLAT Vector of Locally Aggregated Tensors VQA Visual Question Answering