M. Okutomi and T. Kanade, A multiple-baseline stereo, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.15, issue.4, pp.353-363, 1993.
DOI : 10.1109/34.206955

C. Rhemann, A. Hosni, M. Bleyer, C. Rother, and M. Gelautz, Fast cost-volume filtering for visual correspondence and beyond, CVPR 2011, 2011.
DOI : 10.1109/CVPR.2011.5995372

B. K. Horn and B. G. Schunck, ???Determining optical flow???: a retrospective, Artificial Intelligence, vol.59, issue.1-2, pp.81-87, 1993.
DOI : 10.1016/0004-3702(93)90173-9

J. Revaud, P. Weinzaepfel, Z. Harchaoui, and C. Schmid, DeepMatching: Hierarchical Deformable Dense Matching, International Journal of Computer Vision, vol.34, issue.1, pp.1-24, 2015.
DOI : 10.1109/CVPR.2014.435
URL : https://hal.archives-ouvertes.fr/hal-01148432

P. Weinzaepfel, J. Revaud, Z. Harchaoui, and C. Schmid, DeepFlow: Large Displacement Optical Flow with Deep Matching, 2013 IEEE International Conference on Computer Vision, 2013.
DOI : 10.1109/ICCV.2013.175
URL : https://hal.archives-ouvertes.fr/hal-00873592

J. Matas, O. Chum, M. Urban, and T. Pajdla, Robust wide-baseline stereo from maximally stable extremal regions, Image and Vision Computing, vol.22, issue.10, pp.761-767, 2004.
DOI : 10.1016/j.imavis.2004.02.006

H. Yang, W. Lin, and J. Lu, DAISY Filter Flow: A Generalized Discrete Approach to Dense Correspondences, 2014 IEEE Conference on Computer Vision and Pattern Recognition, 2014.
DOI : 10.1109/CVPR.2014.435
URL : http://www.cs.unc.edu/%7Eyhs/Papers/%5B2014_CVPR%5D_DaisyFlow_Final.pdf

C. Liu, J. Yuen, and A. Torralba, SIFT Flow: Dense Correspondence Across Scenes and Its Applications, TPAMI, vol.33, issue.5, pp.978-994, 2011.
DOI : 10.1007/978-3-319-23048-1_2
URL : http://people.csail.mit.edu/celiu/SIFTflow/SIFTflow.pdf

Y. Hacohen, E. Shechtman, D. B. Goldman, and D. Lischinski, Nonrigid dense correspondence with applications for image enhancement, ACM TOG, vol.30, issue.4, p.70, 2011.

J. Kim, C. Liu, F. Sha, and K. Grauman, Deformable Spatial Pyramid Matching for Fast Dense Correspondences, 2013 IEEE Conference on Computer Vision and Pattern Recognition, 2013.
DOI : 10.1109/CVPR.2013.299
URL : http://people.csail.mit.edu/celiu/pdfs/CVPR13-DSPM.pdf

T. Zhou, Y. J. Lee, S. X. Yu, and A. A. Efros, FlowWeb: Joint image set alignment by weaving consistent, pixel-wise correspondences, CVPR, 2015.

O. Duchenne, A. Joulin, and J. Ponce, A graph-matching kernel for object categorization, 2011 International Conference on Computer Vision, 2011.
DOI : 10.1109/ICCV.2011.6126445
URL : https://hal.archives-ouvertes.fr/hal-00650345

T. Taniai, S. N. Sinha, and Y. Sato, Joint Recovery of Dense Correspondence and Cosegmentation in Two Images, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.
DOI : 10.1109/CVPR.2016.460

H. Bristow, J. Valmadre, and S. Lucey, Dense Semantic Correspondence Where Every Pixel is a Classifier, 2015 IEEE International Conference on Computer Vision (ICCV), 2015.
DOI : 10.1109/ICCV.2015.458
URL : http://ci2cv.net/media/papers/2015_ICCV_Hilton.pdf

T. Hassner, V. Mayzels, and L. Zelnik-manor, On SIFTs and their scales, 2012 IEEE Conference on Computer Vision and Pattern Recognition, 2012.
DOI : 10.1109/CVPR.2012.6247842

J. Hur, H. Lim, C. Park, and S. C. Ahn, Generalized Deformable Spatial Pyramid: Geometry-preserving dense correspondence estimation, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015.
DOI : 10.1109/CVPR.2015.7298745

W. Qiu, X. Wang, X. Bai, and Z. Tu, Scale-Space SIFT Flow, WACV, 2014.
DOI : 10.1007/978-3-319-23048-1_4

M. Tau and T. Hassner, Dense Correspondences across Scenes and Scales, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.38, issue.5, pp.875-888, 2016.
DOI : 10.1109/TPAMI.2015.2474356

E. Trulls, I. Kokkinos, A. Sanfeliu, and F. Moreno-noguer, Dense segmentation-aware descriptors, CVPR, 2013.
DOI : 10.1109/cvpr.2013.372
URL : https://hal.archives-ouvertes.fr/hal-00856023

C. B. Choy, M. Chandraker, J. Gwak, and S. Savarese, Universal correspondence network Learning dense correspondence via 3D-guided cycle consistency, NIPS CVPR, 2016.

P. Arbelaez, J. Pont-tuset, J. Barron, F. Marques, and J. Malik, Multiscale Combinatorial Grouping, 2014 IEEE Conference on Computer Vision and Pattern Recognition, 2014.
DOI : 10.1109/CVPR.2014.49
URL : http://www.eecs.berkeley.edu/Research/Projects/CS/vision/grouping/papers/apbmm_cvpr2014.pdf

J. Hosang, R. Benenson, P. Dollár, and B. Schiele, What makes for effective detection proposals? " TPAMI, 2015.
DOI : 10.1109/tpami.2015.2465908
URL : http://arxiv.org/pdf/1502.05082

S. Manen, M. Guillaumin, and L. Van-gool, Prime Object Proposals with Randomized Prim's Algorithm, 2013 IEEE International Conference on Computer Vision, 2013.
DOI : 10.1109/ICCV.2013.315

J. R. Uijlings, K. E. Van-de-sande, T. Gevers, and A. W. Smeulders, Selective Search for Object Recognition, International Journal of Computer Vision, vol.57, issue.1, pp.154-171, 2013.
DOI : 10.1023/B:VISI.0000013087.49260.fb
URL : http://www.science.uva.nl/research/publications/2011/vandeSandeICCV2011/vandesande_iccv2011.pdf

C. L. Zitnick and P. Dollár, Edge Boxes: Locating Object Proposals from Edges, ECCV, 2014.
DOI : 10.1007/978-3-319-10602-1_26
URL : http://research.microsoft.com/en-us/um/people/larryz/ZitnickDollarECCV14edgeBoxes.pdf

H. Kaiming, Z. Xiangyu, R. Shaoqing, and J. Sun, Spatial pyramid pooling in deep convolutional networks for visual recognition, ECCV, 2014.

G. Zhu, F. Porikli, and H. Li, Beyond Local Search: Tracking Objects Everywhere with Instance-Specific Proposals, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.
DOI : 10.1109/CVPR.2016.108

L. Bourdev and J. Malik, Poselets: Body part detectors trained using 3D human pose annotations, 2009 IEEE 12th International Conference on Computer Vision, 2009.
DOI : 10.1109/ICCV.2009.5459303

D. A. Forsyth and J. Ponce, Computer vision: A modern approach Computer Vision: A Modern Approach, 2011.
URL : https://hal.archives-ouvertes.fr/hal-01063327

M. Cho and K. M. Lee, Progressive graph matching: Making a move of graphs via probabilistic voting, CVPR, 2012.

J. L. Long, N. Zhang, and T. Darrell, Do convnets learn correspondence, NIPS, 2014.

I. Kemelmacher-shlizerman and S. M. Seitz, Collection flow, 2012 IEEE Conference on Computer Vision and Pattern Recognition, 2012.
DOI : 10.1109/CVPR.2012.6247876

X. Zhou, M. Zhu, and K. Daniilidis, Multi-image Matching via Fast Alternating Minimization, 2015 IEEE International Conference on Computer Vision (ICCV), 2015.
DOI : 10.1109/ICCV.2015.459

J. Carreira, A. Kar, S. Tulsiani, and J. Malik, Virtual view networks for object reconstruction, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015.
DOI : 10.1109/CVPR.2015.7298912

G. Gkioxari, R. Girshick, and J. Malik, Contextual Action Recognition with R*CNN, 2015 IEEE International Conference on Computer Vision (ICCV), 2015.
DOI : 10.1109/ICCV.2015.129
URL : http://arxiv.org/pdf/1505.01197

R. G. Cinbis, J. Verbeek, and C. Schmid, Multi-fold MIL Training for Weakly Supervised Object Localization, 2014 IEEE Conference on Computer Vision and Pattern Recognition, 2014.
DOI : 10.1109/CVPR.2014.309
URL : https://hal.archives-ouvertes.fr/hal-00975746

J. Dai, K. He, and J. Sun, BoxSup: Exploiting Bounding Boxes to Supervise Convolutional Networks for Semantic Segmentation, 2015 IEEE International Conference on Computer Vision (ICCV), 2015.
DOI : 10.1109/ICCV.2015.191

M. Cho, S. Kwak, C. Schmid, and J. Ponce, Unsupervised object discovery and localization in the wild: Part-based matching with bottom-up region proposals, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015.
DOI : 10.1109/CVPR.2015.7298724
URL : https://hal.archives-ouvertes.fr/hal-01110036

H. Jiang, Matching bags of regions in RGBD images, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015.
DOI : 10.1109/CVPR.2015.7298957

M. Bai, W. Luo, K. Kundu, and R. Urtasun, Exploiting Semantic Information and Deep Matching for Optical Flow, ECCV, 2016.
DOI : 10.1109/ICCV.2015.169

L. Sevilla-lara, D. Sun, V. Jampani, and M. J. Black, Optical Flow with Semantic Segmentation and Localized Layers, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.
DOI : 10.1109/CVPR.2016.422
URL : http://arxiv.org/pdf/1603.03911

S. Ren, K. He, R. Girshick, and J. Sun, Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks, NIPS, 2015.
DOI : 10.1109/TPAMI.2016.2577031

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), 2005.
DOI : 10.1109/CVPR.2005.177
URL : https://hal.archives-ouvertes.fr/inria-00548512

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), 2006.
DOI : 10.1109/CVPR.2006.68
URL : https://hal.archives-ouvertes.fr/inria-00548585

A. Krizhevsky, I. Sutskever, and G. E. Hinton, ImageNet classification with deep convolutional neural networks, NIPS, 2012.
DOI : 10.1162/neco.2009.10-08-881
URL : http://dl.acm.org/ft_gateway.cfm?id=3065386&type=pdf

B. Leibe, A. Leonardis, and B. Schiele, Robust Object Detection with Interleaved Categorization and Segmentation, International Journal of Computer Vision, vol.73, issue.2, pp.259-289, 2008.
DOI : 10.1007/978-3-540-24670-1_6
URL : http://www.vision.ee.ethz.ch/~bleibe/papers/leibe-interleaved-ijcv07final.pdf

S. Maji and J. Malik, Object detection using a max-margin Hough transform, 2009 IEEE Conference on Computer Vision and Pattern Recognition, 2009.
DOI : 10.1109/CVPR.2009.5206693

P. Felzenszwalb, D. Mcallester, and D. Ramanan, A discriminatively trained, multiscale, deformable part model, 2008 IEEE Conference on Computer Vision and Pattern Recognition, 2008.
DOI : 10.1109/CVPR.2008.4587597
URL : http://people.csail.mit.edu/torralba/courses/6.870/papers/FelzenszwalbMcAllesterRamanan.pdf

H. P. Lopuhaa and P. J. Rousseeuw, Breakdown Points of Affine Equivariant Estimators of Multivariate Location and Covariance Matrices, The Annals of Statistics, pp.229-248, 1991.
DOI : 10.1214/aos/1176347978

P. T. Fletcher, S. Venkatasubramanian, and S. Joshi, Robust statistics on Riemannian manifolds via the geometric median, 2008 IEEE Conference on Computer Vision and Pattern Recognition, 2008.
DOI : 10.1109/CVPR.2008.4587747
URL : http://www.cs.utah.edu/%7Esci/publications/fletcher08/Fletcher_CVPR2008.pdf

R. Chandrasekaran and A. Tamir, Open questions concerning Weiszfeld's algorithm for the Fermat-Weber location problem, Mathematical Programming, pp.293-295, 1989.
DOI : 10.1007/BF01587094

F. L. Bookstein, Principal warps: thin-plate splines and the decomposition of deformations, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.11, issue.6, pp.567-585, 1989.
DOI : 10.1109/34.24792

G. Donato and S. Belongie, Approximate Thin Plate Spline Mappings, ECCV, 2002.
DOI : 10.1007/3-540-47977-5_2
URL : http://www.cs.berkeley.edu/~sjb/nips2001.ps.gz

B. Ham, M. Cho, and J. Ponce, Robust image filtering using joint static and dynamic guidance, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015.
DOI : 10.1109/CVPR.2015.7299115
URL : https://hal.archives-ouvertes.fr/hal-01240280

S. Baker, D. Scharstein, J. Lewis, S. Roth, M. J. Black et al., A database and evaluation methodology for optical flow, pp.1-31, 2011.
DOI : 10.1109/iccv.2007.4408903

D. J. Butler, J. Wulff, G. B. Stanley, and M. J. Black, A Naturalistic Open Source Movie for Optical Flow Evaluation, ECCV, 2012.
DOI : 10.1007/978-3-642-33783-3_44
URL : https://stanley.gatech.edu/publications/ButlerECCV2012.pdf

R. Sibson, A brief description of natural neighbour interpolation Interpreting multivariate data, pp.21-36, 1981.

A. Kanazawa, D. W. Jacobs, and M. Chandraker, WarpNet: Weakly Supervised Matching for Single-View Reconstruction, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.
DOI : 10.1109/CVPR.2016.354
URL : http://arxiv.org/pdf/1604.05592

C. Wah, S. Branson, P. Welinder, P. Perona, and S. Belongie, The CALTECH-UCSD birds, 2011.

Y. Yang and D. Ramanan, Articulated Human Detection with Flexible Mixtures of Parts, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.35, issue.12, pp.2878-2890, 2013.
DOI : 10.1109/TPAMI.2012.261

M. Cho, K. Alahari, and J. Ponce, Learning Graphs to Match, 2013 IEEE International Conference on Computer Vision, 2013.
DOI : 10.1109/ICCV.2013.11
URL : https://hal.archives-ouvertes.fr/hal-00875105

E. Simo-serra, E. Trulls, L. Ferraz, I. Kokkinos, P. Fua et al., Discriminative Learning of Deep Convolutional Feature Point Descriptors, 2015 IEEE International Conference on Computer Vision (ICCV), 2015.
DOI : 10.1109/ICCV.2015.22

D. G. Lowe, Distinctive Image Features from Scale-Invariant Keypoints, International Journal of Computer Vision, vol.60, issue.2, pp.91-110, 2004.
DOI : 10.1023/B:VISI.0000029664.99615.94
URL : http://www.cs.ubc.ca/~lowe/papers/ijcv03.ps

K. Tang, A. Joulin, L. Li, and L. Fei-fei, Co-localization in Real-World Images, 2014 IEEE Conference on Computer Vision and Pattern Recognition, 2014.
DOI : 10.1109/CVPR.2014.190
URL : http://vision.stanford.edu/pdf/tang14.pdf

J. Deng, W. Dong, R. Socher, L. Li, K. Li et al., ImageNet: A large-scale hierarchical image database, CVPR, 2009.

M. Brown, G. Hua, and S. Winder, Discriminative Learning of Local Image Descriptors, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.33, issue.1, pp.43-57, 2011.
DOI : 10.1109/TPAMI.2010.54
URL : http://www.cs.bath.ac.uk/brown/papers/pami2010.pdf

M. Paulin, Local Convolutional Features with Unsupervised Training for Image Retrieval, 2015 IEEE International Conference on Computer Vision (ICCV), 2015.
DOI : 10.1109/ICCV.2015.19
URL : https://hal.archives-ouvertes.fr/hal-01207966

S. Zagoruyko and N. Komodakis, Learning to compare image patches via convolutional neural networks, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015.
DOI : 10.1109/CVPR.2015.7299064
URL : https://hal.archives-ouvertes.fr/hal-01246261

X. Han, T. Leung, Y. Jia, R. Sukthankar, and A. C. Berg, MatchNet: Unifying feature and metric learning for patch-based matching, CVPR, 2015.

L. Fei-fei, R. Fergus, and P. Perona, One-shot learning of object categories, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.28, issue.4, pp.594-611, 2006.
DOI : 10.1109/TPAMI.2006.79

C. Liu, J. Yuen, and A. Torralba, Nonparametric Scene Parsing via Label Transfer, TPAMI, vol.33, issue.12, pp.2368-2382, 2011.
DOI : 10.1007/978-3-319-23048-1_10
URL : http://people.csail.mit.edu/celiu/pdfs/LabelTransferTPAMI.pdf

E. G. Learned-miller, Data driven image models through continuous joint alignment, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.28, issue.2, pp.236-250, 2006.
DOI : 10.1109/TPAMI.2006.34

Y. Peng, A. Ganesh, J. Wright, W. Xu, and Y. Ma, RASL: Robust alignment by sparse and low-rank decomposition for linearly correlated images, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp.2233-2246, 2012.
DOI : 10.1109/CVPR.2010.5540138

Y. Lin, V. I. Morariu, W. Hsu, and L. S. Davis, Jointly Optimizing 3D Model Fitting and Fine-Grained Classification, ECCV, 2014.
DOI : 10.1007/978-3-319-10593-2_31
URL : http://www.umiacs.umd.edu/~morariu/publications/LinFineGrained3dECCV14.pdf

M. Rubinstein, A. Joulin, J. Kopf, and C. Liu, Unsupervised Joint Object Discovery and Segmentation in Internet Images, 2013 IEEE Conference on Computer Vision and Pattern Recognition, 2013.
DOI : 10.1109/CVPR.2013.253
URL : https://hal.archives-ouvertes.fr/hal-01064227

B. Hariharan, P. Arbeláez, L. Bourdev, S. Maji, and J. Malik, 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

X. Chen, R. Mottaghi, X. Liu, S. Fidler, and R. Urtasun, Detect What You Can: Detecting and Representing Objects Using Holistic Models and Body Parts, 2014 IEEE Conference on Computer Vision and Pattern Recognition, 2014.
DOI : 10.1109/CVPR.2014.254

Y. Xiang, R. Mottaghi, and S. Savarese, Beyond PASCAL: A benchmark for 3D object detection in the wild, IEEE Winter Conference on Applications of Computer Vision, 2014.
DOI : 10.1109/WACV.2014.6836101

S. Kim, D. Min, B. Ham, S. Jeon, S. Lin et al., FCSS: Fully Convolutional Self-Similarity for Dense Semantic Correspondence, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017.
DOI : 10.1109/CVPR.2017.73

K. Han, R. S. Rezende, B. Ham, K. K. Wong, M. Cho et al., SCNet: Learning semantic correspondence, 1705.
URL : https://hal.archives-ouvertes.fr/hal-01576117