. Bibliography and . Aghazadeh, Mixture component identification and learning for visual recognition, European Conference on Computer Vision (ECCV), 2012.

. Amit, A coarse-to-fine strategy for multiclass shape detection, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.26, issue.12, 2004.
DOI : 10.1109/TPAMI.2004.111

Z. Aytar, Y. Aytar, and A. Zisserman, Tabula rasa: Model transfer for object category detection, 2011 International Conference on Computer Vision, 2011.
DOI : 10.1109/ICCV.2011.6126504

. Bar, Part-based feature synthesis for human detection, European Conference on Computer Vision (ECCV), 2010.

. Bart, Unsupervised learning of visual taxonomies, 2008 IEEE Conference on Computer Vision and Pattern Recognition, 2008.
DOI : 10.1109/CVPR.2008.4587620

U. Bart, Crossgeneralization: learning novel classes from a single example by feature replacement, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2005.

. Benenson, Pedestrian detection at 100 frames per second, 2012 IEEE Conference on Computer Vision and Pattern Recognition, 2012.
DOI : 10.1109/CVPR.2012.6248017

. Benenson, Stixels estimation without depth map computation, 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops), 2011.
DOI : 10.1109/ICCVW.2011.6130495

. Bengio, Label embedding trees for large multi-class tasks, Conference on Neural Information Processing Systems (NIPS), 2010.

B. P. Bennett, J. A. Bennett, and . Blue, A support vector machine approach to decision trees, 1998 IEEE International Joint Conference on Neural Networks Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98CH36227), pp.97-100, 1997.
DOI : 10.1109/IJCNN.1998.687237

. Bergboer, Context-based object detection in still images, Image and Vision Computing, vol.24, issue.9, 2006.
DOI : 10.1016/j.imavis.2006.02.024

. Biederman, Recognition-by-components: A theory of human image understanding, Psychological Review, 1987.

. Binder, On Taxonomies for Multi-class Image Categorization, International Journal of Computer Vision, vol.73, issue.2, 2012.
DOI : 10.1007/s11263-010-0417-8

M. Blaschko and A. Gretton, Learning taxonomies by dependence maximization, Advances in neural information processing systems 21, 2009.

. Blaschko, Simultaneous object detection and ranking with weak supervision, Conference on Neural Information Processing Systems (NIPS), 2010.

. Blei, Hierarchical topic models and the nested chinese restaurant process, Conference on Neural Information Processing Systems (NIPS), 2003.

. Bottou and . Bousquet, The tradeoffs of large scale learning, Conference on Neural Information Processing Systems (NIPS), 2007.

V. Boyd, . Boyd, and V. Stephen, Convex Optimization, 2004.

H. Cai, . Cai, . Lijuan, and T. Hofmann, Hierarchical document categorization with support vector machines, Proceedings of the Thirteenth ACM conference on Information and knowledge management , CIKM '04, 2004.
DOI : 10.1145/1031171.1031186

. Canini, . Griffiths, R. Kevin, T. L. Canini, and . Griffiths, Modeling transfer learning and taxonomy induction with the hierarchical Dirichlet process, Transfer Learning via Rich Generative Models -Workshop held at NIPS, 2010.

C. , L. Chang, C. , and L. Chih-jen, LIBSVM: A library for support vector machines, ACM Transactions on Intelligent Systems and Technology, 2011.

. Cho, Real-time pedestrian detection with deformable part models, 2012 IEEE Intelligent Vehicles Symposium, 2012.
DOI : 10.1109/IVS.2012.6232264

. Choi, Exploiting hierarchical context on a large database of object categories, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2010.
DOI : 10.1109/CVPR.2010.5540221

. Choi, A tree-based context model for object recognition, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012.

. Ciresan, A committee of neural networks for traffic sign classification, The 2011 International Joint Conference on Neural Networks, 2011.
DOI : 10.1109/IJCNN.2011.6033458

. Crammer, On the algorithmic implementation of multiclass kernel-based vector machines, Journal of Machine Learning Research, 2001.

T. Dalal, Histograms of Oriented Gradients for Human Detection, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), 2005.
DOI : 10.1109/CVPR.2005.177
URL : https://hal.archives-ouvertes.fr/inria-00548512

. Dean, Fast accurate detection of 100000 object classes on a single machine, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013.

. Deng, Fast and balanced: Efficient label tree learning for large scale object recognition, Conference on Neural Information Processing Systems (NIPS), 2011.

. Desai, Discriminative models for multi-class object layout, International Journal of Computer Vision (IJCV, 2011.

. Deselaers, Localizing Objects While Learning Their Appearance, European Conference on Computer Vision (ECCV), 2010.
DOI : 10.1007/978-3-642-15561-1_33

. Divvala, How Important Are ???Deformable Parts??? in the Deformable Parts Model?, 2012.
DOI : 10.1007/978-3-642-33885-4_4

. Divvala, An empirical study of context in object detection, 2009 IEEE Conference on Computer Vision and Pattern Recognition, 2009.
DOI : 10.1109/CVPR.2009.5206532

. Dollár, Fast Feature Pyramids for Object Detection, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.36, issue.8, 2014.
DOI : 10.1109/TPAMI.2014.2300479

. Dollar, The Fastest Pedestrian Detector in the West, Procedings of the British Machine Vision Conference 2010, 2010.
DOI : 10.5244/C.24.68

. Dollar, Integral Channel Features, Procedings of the British Machine Vision Conference 2009, 2009.
DOI : 10.5244/C.23.91

F. Dubout, C. Dubout, and F. Fleuret, Exact Acceleration of Linear Object Detectors, European Conference on Computer Vision (ECCV), 2012.
DOI : 10.1007/978-3-642-33712-3_22

H. Duda, R. O. Duda, and P. E. Hart, Pattern Classification and Scene Analysis, 1973.

. Espinace, Indoor scene recognition by a mobile robot through adaptive object detection, Robotics and Autonomous Systems, vol.61, issue.9, 2013.
DOI : 10.1016/j.robot.2013.05.002

. Everingham, The Pascal Visual Object Classes (VOC) Challenge, International Journal of Computer Vision, vol.73, issue.2, 2010.
DOI : 10.1007/s11263-009-0275-4

. Everingham, The Pascal Visual Object Classes (VOC) Challenge, International Journal of Computer Vision, vol.73, issue.2, 2012.
DOI : 10.1007/s11263-009-0275-4

. Fei, ]. B. Liu, J. Fei, and . Liu, Binary tree of SVM: a new fast multiclass training and classification algorithm, IEEE Transactions on Neural Net- works, 2006.
DOI : 10.1109/TNN.2006.872343

. Fei-fei, A Bayesian approach to unsupervised one-shot learning of object categories, International Conference on Computer Vision (ICCV), 2003.

. Felzenszwalb, Link to deformable part model, 2008.

. Felzenszwalb, A discriminatively trained, multiscale, deformable part model, 2008 IEEE Conference on Computer Vision and Pattern Recognition, 2008.
DOI : 10.1109/CVPR.2008.4587597

. Felzenszwalb, Object Detection with Discriminatively Trained Part-Based Models, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.32, issue.9, 2010.
DOI : 10.1109/TPAMI.2009.167

. Felzenszwalb, Cascade object detection with deformable part models, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2010.
DOI : 10.1109/CVPR.2010.5539906

H. Felzenszwalb, F. Pedro, D. P. Felzenszwalb, and . Huttenlocher, Distance transforms of sampled functions, 2012.

. Fidler, Sanja Fidler and Marko Boben and Ales Leonardis Evaluating multi-class learning strategies in a generative hierarchical framework for object detection, Conference on Neural Information Processing Systems (NIPS), 2009.

. Fidler, Sanja Fidler and Marko Boben and Ales Leonardis A coarse-to-fine taxonomy of constellations for fast multi-class object detection, European Conference on Computer Vision (ECCV), 2010.

L. Fidler, Sanja Fidler and Ales Leonardis Towards scalable representations of object categories: Learning a hierarchy of parts, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2007.

Y. Freund and R. E. Schapire, A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting, Journal of Computer and System Sciences, vol.55, issue.1, 1997.
DOI : 10.1006/jcss.1997.1504

. Gadeski, GPU deformable part model for object recognition, Journal of Real-Time Image Processing, vol.14, issue.3, 2014.
DOI : 10.1007/s11554-014-0447-5

. Gangaputra, . Geman, . Gangaputra, . Sachin, and D. Geman, A Design Principle for Coarse-to-Fine Classification, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Volume 2 (CVPR'06), 2006.
DOI : 10.1109/CVPR.2006.21

K. Gao, D. Gao, and . Koller, Discriminative learning of relaxed hierarchy for large-scale visual recognition, International Conference on Computer Vision (ICCV), 2011.

. Geiger, Link to kitti dataset, 2012.

. Geiger, Vision meets robotics: The KITTI dataset, The International Journal of Robotics Research, vol.32, issue.11, 2013.
DOI : 10.1177/0278364913491297

. Girshick, 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

. Gönen, . Gönen, and E. Mehmet-and-alpayd?n, Multiple kernel learning algorithms, Journal of Machine Learning Research (JMLR), 2011.

. Göring, Nonparametric part transfer for finegrained recognition, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014.

. Gretton, Measuring Statistical Dependence with Hilbert-Schmidt Norms, International Conference on Algorithmic Learning Theory (ALT), 2005.
DOI : 10.1007/11564089_7

P. Griffin, Learning and using taxonomies for fast visual categorization, 2008 IEEE Conference on Computer Vision and Pattern Recognition, 2008.
DOI : 10.1109/CVPR.2008.4587410

F. Guillaumin, M. Guillaumin, and V. Ferrari, Large-scale knowledge transfer for object localization in ImageNet, 2012 IEEE Conference on Computer Vision and Pattern Recognition, 2012.
DOI : 10.1109/CVPR.2012.6248055

. Guzmán-rivera, Divmcuts: Faster training of structural svms with diverse m-best cutting-planes, International Conference on Artificial Intelligence and Statistics (AISTATS), 2013.

]. Hamel, Knowledge Discovery with: Support Vector Machines, 2009.
DOI : 10.1002/9780470503065

R. Hanson, A. R. Hanson, and E. M. Riseman, VI- SIONS: A computer system for interpreting scenes, Computer Vision Systems, 1978.

. Hao, Hierarchically SVM classification based on support vector clustering method and its application to document categorization, Expert Systems with Applications, 2007.
DOI : 10.1016/j.eswa.2006.06.009

. Hoffman, LSDA: Large scale detection through adaptation, Conference on Neural Information Processing Systems (NIPS), 2014.

L. Hsu, C. Hsu, and L. Chih-jen, A comparison of methods for multiclass support vector machines, IEEE Transactions on Neural Net- works, 2002.

. Izadinia, Incorporating Scene Context and Object Layout into Appearance Modeling, 2014 IEEE Conference on Computer Vision and Pattern Recognition, 2014.
DOI : 10.1109/CVPR.2014.37

D. Jain, . Jain, K. Anil, and R. C. Dubes, Algorithms for Clustering Data, 1988.

]. T. Joachims, Making large-scale SVM learning practical, Advances in Kernel Methods - Support Vector Learning, pp.169-184, 1999.

. Joachims, Cutting-plane training of structural SVMs, Machine Learning, vol.6, issue.2, 2009.
DOI : 10.1007/s10994-009-5108-8

. Krempp, Sequential learning of reusable parts for object detection, 2002.

. Kressel and H. Ulrich, Pairwise classification and support vector machines, Advances in Kernel Methods, pp.255-268, 1999.

. Krizhevsky, Imagenet classification with deep convolutional neural networks, Conference on Neural Information Processing Systems (NIPS), pp.1097-1105, 2012.

. Kuzborskij, From N to N+1: Multiclass Transfer Incremental Learning, 2013 IEEE Conference on Computer Vision and Pattern Recognition, 2013.
DOI : 10.1109/CVPR.2013.431

C. Lampert, An efficient divide-and-conquer cascade for nonlinear object detection, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2010.
DOI : 10.1109/CVPR.2010.5540107

. Lampert, Efficient Subwindow Search: A Branch and Bound Framework for Object Localization, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.31, issue.12, 2009.
DOI : 10.1109/TPAMI.2009.144

B. Lampert, H. Christoph, M. B. Lampert, and . Blaschko, A Multiple Kernel Learning Approach to Joint Multi-class Object Detection, Deutsche Arbeitsgemeinschaft für Mustererkennung, 2008.
DOI : 10.1007/978-3-540-69321-5_4

. Lampert, Learning to detect unseen object classes by between-class attribute transfer, 2009 IEEE Conference on Computer Vision and Pattern Recognition, 2009.
DOI : 10.1109/CVPR.2009.5206594

. Leibe, Robust Object Detection with Interleaved Categorization and Segmentation, International Journal of Computer Vision, vol.73, issue.2, 2008.
DOI : 10.1007/s11263-007-0095-3

B. Leibe and B. Schiele, Interleaved Object Categorization and Segmentation, Procedings of the British Machine Vision Conference 2003, 2003.
DOI : 10.5244/C.17.78

. Levi, Fast Multiple-Part Based Object Detection Using KD-Ferns, 2013 IEEE Conference on Computer Vision and Pattern Recognition, 2013.
DOI : 10.1109/CVPR.2013.127

. Lim, Transfer learning by borrowing examples for multiclass object detection, Conference on Neural Information Processing Systems (NIPS), 2011.

. Lim, Multi-class Object Layout with Unsupervised Image Classification and Object Localization, International Symposium on Visual Computing (ISVC), 2011.
DOI : 10.1016/0012-365X(75)90103-X

. Luo, Multiclass transfer learning from unconstrained priors, International Conference on Computer Vision (ICCV), 2011.

. Madzarov, A multi-class svm classifier utilizing binary decision tree, 2008.

. Malisiewicz, Ensemble of exemplar-SVMs for object detection and beyond, 2011 International Conference on Computer Vision, 2011.
DOI : 10.1109/ICCV.2011.6126229

S. Marszalek, Constructing Category Hierarchies for Visual Recognition, European Conference on Computer Vision (ECCV), 2008.
DOI : 10.1007/978-3-540-88693-8_35
URL : https://hal.archives-ouvertes.fr/inria-00548656

. Matan, Reading handwritten digits: a ZIP code recognition system, Computer, vol.25, issue.7, 1992.
DOI : 10.1109/2.144441

. Mikolajczyk, Multiple Object Class Detection with a Generative Model, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Volume 1 (CVPR'06), 2006.
DOI : 10.1109/CVPR.2006.202

. Mikolajczyk, Shape recognition with edge-based features, Procedings of the British Machine Vision Conference 2003, 2003.
DOI : 10.5244/C.17.79
URL : https://hal.archives-ouvertes.fr/inria-00548226

. Miller, Learning from one example through shared densities on transforms, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662), 2000.
DOI : 10.1109/CVPR.2000.855856

. Mittal, Taxonomic Multi-class Prediction and Person Layout Using Efficient Structured Ranking, European Conference on Computer Vision (ECCV), 2012.
DOI : 10.1007/978-3-642-33709-3_18
URL : https://hal.archives-ouvertes.fr/hal-00775992

. Mottaghi, The Role of Context for Object Detection and Semantic Segmentation in the Wild, 2014 IEEE Conference on Computer Vision and Pattern Recognition, 2014.
DOI : 10.1109/CVPR.2014.119

[. Fard, Détection hiérarchique multi-classes d'objets dans les images, ORASIS, 2013.

[. Fard, Apprentissage hiérarchique simultané pour la détection efficace d'objets, Reconnaissance de Formes et Intelligence Artificielle, 2014.

[. Fard, Joint hierarchical learning for efficient multi-class object detection, IEEE Winter Conference on Applications of Computer Vision, 2014.
DOI : 10.1109/WACV.2014.6836090
URL : https://hal.archives-ouvertes.fr/hal-01296142

[. Fard, Joint Learning for Multi-class Object Detection, International conference on computer vision theory and applications (VISAPP), 2014.
URL : https://hal.archives-ouvertes.fr/hal-01295900

T. Oliva, . Oliva, . Aude, and A. Torralba, The role of context in object recognition, Trends in Cognitive Sciences, vol.11, issue.12, 2007.
DOI : 10.1016/j.tics.2007.09.009

. Opelt, Learning an Alphabet of Shape and Appearance for Multi-Class Object Detection, International Journal of Computer Vision, vol.73, issue.2, 2008.
DOI : 10.1007/s11263-008-0139-3

. Oquab, Learning and Transferring Mid-level Image Representations Using Convolutional Neural Networks, 2014 IEEE Conference on Computer Vision and Pattern Recognition, 2014.
DOI : 10.1109/CVPR.2014.222
URL : https://hal.archives-ouvertes.fr/hal-00911179

E. Ott, Shared parts for deformable part-based models, CVPR 2011, 2011.
DOI : 10.1109/CVPR.2011.5995357

. Pedersoli, Recursive Coarse-to-Fine Localization for Fast Object Detection, European Conference on Computer Vision (ECCV), 2010.
DOI : 10.1007/978-3-642-15567-3_21

. Pedersoli, Towards a real-time pedestrian detection based only on vision, IEEE Transactions on Intelligent Transportation Systems, 2013.

. Pedersoli, A coarse-to-fine approach for fast deformable object detection, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2011.

. Pepik, Occlusion patterns for object class detection, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013.

. Peralta, Adaptive hierarchical contexts for object recognition with conditional mixture of trees, Procedings of the British Machine Vision Conference 2012, 2012.
DOI : 10.5244/C.26.121

L. Perko, Roland Perko and Ales Leonardis Context driven focus of attention for object detection, Attention in Cognitive Systems. Theories and Systems from an Interdisciplinary Viewpoint, 4th International Workshop on Attention in Cognitive Systems, 2007.

C. John and . Platt, Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods, Advances in Large Margin Classifiers, 1999.

. Platt, Large margin dags for multiclass classification, Conference on Neural Information Processing Systems (NIPS), 2000.

S. Polak, The semiexplicit shape model for multi-object detection and classification, European Conference on Computer Vision (ECCV), 2010.

R. Prisacariu, fasthog a real-time gpu implementation of hog, 2009.

. Razavi, Scalable multi-class object detection, CVPR 2011, 2011.
DOI : 10.1109/CVPR.2011.5995441

. Razavi, Latent Hough Transform for Object Detection, European Conference on Computer Vision (ECCV), 2012.
DOI : 10.1007/978-3-642-33712-3_23

. Ristin, Local Context Priors for Object Proposal Generation, Asian Conference on Computer Vision (ACCV), 2013.
DOI : 10.1007/978-3-642-37331-2_5

. Russakovsky, ImageNet Large Scale Visual Recognition Challenge, International Journal of Computer Vision, vol.1010, issue.1, 2015.
DOI : 10.1007/s11263-015-0816-y

S. J. Russell and P. Norvig, Artificial Intelligence: A Modern Approach, 2003.

. Sadeghi, . Forsyth, . Mohammad-amin, D. A. Sadeghi, and . Forsyth, 30Hz Object Detection with DPM V5, European Conference on Computer Vision (ECCV), 2014.
DOI : 10.1007/978-3-319-10590-1_5

. Salakhutdinov, One-shot learning with a hierarchical nonparametric bayesian model, Unsupervised and Transfer Learning -Workshop held at ICML, 2012.

. Salakhutdinov, Learning to share visual appearance for multiclass object detection, CVPR 2011, 2011.
DOI : 10.1109/CVPR.2011.5995720

. Shalev-shwartz, Pegasos, Proceedings of the 24th international conference on Machine learning, ICML '07, 2007.
DOI : 10.1145/1273496.1273598

M. Shi, J. Shi, and . Malik, Normalized cuts and image segmentation, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000.

F. Simoncelli, W. Simoncelli, and . Freeman, The steerable pyramid: a flexible architecture for multi-scale derivative computation, Proceedings., International Conference on Image Processing, 1995.
DOI : 10.1109/ICIP.1995.537667

. Sivic, Unsupervised discovery of visual object class hierarchies, 2008 IEEE Conference on Computer Vision and Pattern Recognition, 2008.
DOI : 10.1109/CVPR.2008.4587622

. Song, Sparselet Models for Efficient Multiclass Object Detection, European Conference on Computer Vision (ECCV), 2012.
DOI : 10.1007/978-3-642-33709-3_57

. Song, Contextualizing object detection and classification, CVPR 2011, 2011.
DOI : 10.1109/CVPR.2011.5995330

L. P. Sudowe, B. Sudowe, and . Leibe, Efficient Use of Geometric Constraints for Sliding-Window Object Detection in Video, International Conference on Computer Vision Systems (ICVS), 2011.
DOI : 10.1007/11736790_8

. Sun, Find the Best Path: An Efficient and Accurate Classifier for Image Hierarchies, 2013 IEEE International Conference on Computer Vision, 2013.
DOI : 10.1109/ICCV.2013.40

A. Takahashi, S. Takahashi, and . Abe, Decision-tree-based multiclass support vector machines, Proceedings of the 9th International Conference on Neural Information Processing, 2002. ICONIP '02., 2003.
DOI : 10.1109/ICONIP.2002.1202854

. Tang, Detection and tracking of occluded people, British Machine Vision Conference (BMVC), 2012.

. Tenenbaum, How to Grow a Mind: Statistics, Structure, and Abstraction, Science, vol.331, issue.6022, 2011.
DOI : 10.1126/science.1192788

. Teo, A scalable modular convex solver for regularized risk minimization, Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining , KDD '07, 2007.
DOI : 10.1145/1281192.1281270

. Torralba, Sharing features: efficient boosting procedures for multiclass object detection, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004., 2004.
DOI : 10.1109/CVPR.2004.1315241

. Torralba, Sharing Visual Features for Multiclass and Multiview Object Detection, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.29, issue.5, 2007.
DOI : 10.1109/TPAMI.2007.1055

S. Torralba, Statistical context priming for object detection, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001, 2001.
DOI : 10.1109/ICCV.2001.937604

. Totoni, Easy, fast, and energy-efficient object detection on heterogeneous on-chip architectures, ACM Transactions on Architecture and Code Optimization, vol.10, issue.4, 2013.
DOI : 10.1145/2541228.2555302

. Tsochantaridis, 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

. Uijlings, Selective Search for Object Recognition, International Journal of Computer Vision, vol.57, issue.1, 2013.
DOI : 10.1007/s11263-013-0620-5

. Uricar, 2013] Michal Uricar and Vojtech Franc and Václav Hlavác (2013) Bundle methods for structured output learning -back to the roots, Scandinavian Conference on Image Analysis (SCIA)

F. Vezhnevets, . Alexander, . Ferrari, and . Vittorio, Associative embeddings for large-scale knowledge transfer with selfassessment, 2013.

A. Paul, M. J. Viola, and . Jones, Rapid object detection using a boosted cascade of simple features, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2001.

. Wang, Comparative object similarity for improved recognition with few or no examples, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2010.
DOI : 10.1109/CVPR.2010.5539955

. Wojek, Sliding-Windows for Rapid Object Class Localization: A Parallel Technique, Pattern Recognition, 2008.
DOI : 10.1007/978-3-540-69321-5_8

T. Xu, J. B. Xu, and . Tenenbaum, Word learning as Bayesian inference., Psychological Review, 2007.
DOI : 10.1037/0033-295X.114.2.245

W. Xu, R. Xu, I. Wunsch, and D. , Survey of Clustering Algorithms, IEEE Transactions on Neural Networks, vol.16, issue.3, 2005.
DOI : 10.1109/TNN.2005.845141

T. Yang, . Jian-bo, I. W. Yang, and . Tsang, Hierarchical maximum margin learning for multi-class classification, ACM Computing Research Repository, 2012.

Y. Yao and G. Doretto, Boosting for transfer learning with multiple sources, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2010.
DOI : 10.1109/CVPR.2010.5539857

Y. , J. Yu, and T. Joachims, Training structural svms with kernels using sampled cuts, ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2008.

. Zehnder, An Efficient Shared Multi-Class Detection Cascade, Procedings of the British Machine Vision Conference 2008, 2008.
DOI : 10.5244/C.22.80

. Zhang, Adaptive object detection by implicit sub-class sharing features, Signal Processing, 2013.
DOI : 10.1016/j.sigpro.2012.11.027

. Zhu, Part and appearance sharing: Recursive compositional models for multi-view, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2010.

. Zhu, Latent hierarchical structural learning for object detection, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2010.

. Zhu, Yukun Zhu and Raquel Urtasun and Ruslan Salakhutdinov and Sanja Fidler (2015). segdeepm: Exploiting segmentation and context in deep neural networks for object detection, 2015.