Support Vector Machines for Multiple-Instance Learning, Advances in Neural Information Processing Systems (NIPS) (cit. on pp. 19, pp.33-45, 2003. ,
NetVLAD: CNN architecture for weakly supervised place recognition, IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (cit, p.16, 2016. ,
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
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
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
Modern Information Retrieval (cit, p.58, 1999. ,
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
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
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
Structured Prediction Energy Networks, International Conference on Machine Learning (ICML) (cit, p.111, 2016. ,
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
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
Learning long-term dependencies with gradient descent is difficult, IEEE Transactions on Neural Networks (cit, p.14, 1994. ,
DOI : 10.1109/72.279181
Object and Action Classification with Latent Window Parameters, International Journal of Computer Vision, vol.15, issue.4, 2013. ,
DOI : 10.1109/CVPR.2010.5540096
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
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
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
A theoretical analysis of feature pooling in vision algorithms, International Conference on Machine Learning (ICML), 2010. ,
Classification and regression trees, 1984. ,
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
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
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
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
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
ADIOS: Architectures Deep In Output Space, International Conference on Machine Learning (ICML) (cit, p.111, 2016. ,
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
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
R-FCN: Object Detection via Regionbased Fully Convolutional Networks, Advances in Neural Information Processing Systems (NIPS) (cit, pp.91-93, 2016. ,
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
A Conditional Random Field for Multiple- Instance Learning, International Conference on Machine Learning (ICML) (cit, pp.19-45, 2010. ,
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
Weakly Supervised Cascaded Convolutional Networks, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016. ,
DOI : 10.1109/CVPR.2017.545
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
Regularized Bundle Methods for Convex and Non-convex Risks, Journal of Machine Learning Research (JMLR), 2012. ,
Mid-Level Visual Element Discovery as Discriminative Mode Seeking, Advances in Neural Information Processing Systems (NIPS) (cit, p.68, 2013. ,
A comparison of multi-instance learning algorithms, p.18, 2006. ,
FlowNet: Learning Optical Flow with Convolutional Networks, 2015 IEEE International Conference on Computer Vision (ICCV), p.16, 2015. ,
DOI : 10.1109/ICCV.2015.316
Adaptive Subgradient Methods for Online Learning and Stochastic Optimization, Journal of Machine Learning Research (JMLR), 2011. ,
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. ,
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
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
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
Negative Evidence for Weakly Supervised Learning of Deep Structured Models, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017. ,
SyMIL: MinMax Latent SVM for Weakly Labeled Data, IEEE Transactions on Neural Networks and Learning Systems (TNNLS) [Submission], p.31, 2017. ,
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
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
The Pascal Visual Object Classes Challenge: A Retrospective, International Journal of Computer Vision, vol.34, issue.11, 2015. ,
DOI : 10.1109/TPAMI.2012.204
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
The PASCAL Visual Object Classes Challenge 2012 (VOC2012) Results, p.102, 2012. ,
DOI : 10.1007/s11263-014-0733-5
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
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
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
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
Compact Bilinear Pooling, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), p.99, 2016. ,
DOI : 10.1109/CVPR.2016.41
Multi- Instance Kernels, International Conference on Machine Learning (ICML), 2002. ,
Deterministic Annealing for Multiple-Instance Learning, International Conference on Artificial Intelligence and Statistics (AISTAT) (cit, pp.19-45, 2007. ,
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
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
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
Understanding the difficulty of training deep feedforward neural networks, International Conference on Artificial Intelligence and Statistics (AISTAT) (cit, p.14, 2010. ,
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
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
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
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
Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition, European Conference on Computer Vision (ECCV) (cit. on pp. 16, pp.71-75, 2014. ,
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
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
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
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
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift, International Conference on Machine Learning (ICML) (cit, p.14, 2015. ,
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
Caffe, Proceedings of the ACM International Conference on Multimedia, MM '14, pp.61-68, 2014. ,
DOI : 10.1145/2647868.2654889
Cutting-plane training of structural SVMs, Machine Learning (cit, pp.24-40, 2009. ,
DOI : 10.1007/s10994-009-5108-8
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 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
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
Multiple instance learning via Gaussian processes, DMKD) (cit, p.45, 2013. ,
DOI : 10.1109/TNN.2011.2109011
Adam: A Method for Stochastic Optimization, International Conference on Learning Representations (ICLR), 2014. ,
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
Efficient Inference in Fully Connected CRFs with Gaussian Edge Potentials, Advances in Neural Information Processing Systems (NIPS), 2011. ,
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
Ellipsoidal Multiple Instance Learning, International Conference on Machine Learning (ICML) (cit, p.45, 2013. ,
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
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
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
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data, International Conference on Machine Learning (ICML) (cit, p.25, 2001. ,
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
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
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. ,
Backpropagation Applied to Handwritten Zip Code Recognition, Neural Computation, vol.1, issue.4, 1989. ,
DOI : 10.1007/BF00133697
Gradient-based learning applied to document recognition, Proceedings of the IEEE, 1998. ,
DOI : 10.1109/5.726791
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
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
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
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
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
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
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
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
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
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
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
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
Efficient Optimization for Average Precision SVM, Advances in Neural Information Processing Systems (NIPS), 2014. ,
URL : https://hal.archives-ouvertes.fr/hal-01069917
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
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
Verb semantics for English-Chinese translation, Machine Translation, 1995. ,
DOI : 10.1007/BF00997232
Weakly-and semi-supervised learning of a DCNN for semantic image segmentation, IEEE International Conference on Computer Vision (ICCV), 2015. ,
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
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
Automatic discovery and optimization of parts for image classification, International Conference on Learning Representations (ICLR) (cit. on pp. 19, pp.86-94, 2015. ,
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
Fully Convolutional Multi-Class Multiple Instance Learning, International Conference on Learning Representations Workshop (ICLR-W) (cit, p.26, 2015. ,
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
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
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
Marginal Structured SVM with Hidden Variables, International Conference on Machine Learning (ICML) (cit, p.24, 2014. ,
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
Learning to Segment Object Candidates, Advances in Neural Information Processing Systems (NIPS) (cit, p.16, 2015. ,
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
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
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
Recognizing indoor scenes, 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp.28-104, 2009. ,
DOI : 10.1109/CVPR.2009.5206537
Hidden Conditional Random Fields, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.29, issue.10, 2007. ,
DOI : 10.1109/TPAMI.2007.1124
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
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
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
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
SVM Classifier Estimation from Group Probabilities, International Conference on Machine Learning (ICML), 2010. ,
Learning representations by back-propagating errors, Nature, vol.11, issue.13, 1986. ,
ImageNet Large Scale Visual Recognition Challenge, International Journal of Computer Vision (IJCV), vol.2, issue.100, pp.61-81, 2015. ,
Object-Centric Spatial Pooling for Image Classification, European Conference on Computer Vision (ECCV), 2012. ,
DOI : 10.1007/978-3-642-33709-3_1
Multi-Instance Learning with Any Hypothesis Class, Journal of Machine Learning Research (JMLR), 2012. ,
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
Introduction to Modern Information Retrieval, 1986. ,
Convolutional Neural Fabrics, Advances in Neural Information Processing Systems (NIPS) (cit, p.15, 2016. ,
URL : https://hal.archives-ouvertes.fr/hal-01359150
Distributed message passing for large scale graphical models, CVPR 2011, 2011. ,
DOI : 10.1109/CVPR.2011.5995642
Efficient Structured Prediction with Latent Variables for General Graphical Models, International Conference on Machine Learning (ICML) (cit, p.22, 2012. ,
Overfeat: Integrated recognition, localization and detection using convolutional networks, International Conference on Learning Representations (ICLR) (cit, p.13, 2014. ,
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
Pegasos, Proceedings of the 24th international conference on Machine learning, ICML '07, 2011. ,
DOI : 10.1145/1273496.1273598
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
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
Very Deep Convolutional Networks for Large-Scale Image Recognition, International Conference on Learning Representations (ICLR) (cit, pp.75-85, 2015. ,
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
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
Training Deep Neural Networks via Direct Loss Minimization, International Conference on Machine Learning (ICML), 2016. ,
On the Convergence of the Concave-Convex Procedure, Advances in Neural Information Processing Systems (NIPS) (cit, p.37, 2009. ,
Training Very Deep Networks, Advances in Neural Information Processing Systems (NIPS) (cit, p.15, 2015. ,
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
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
On the importance of initialization and momentum in deep learning, International Conference on Machine Learning (ICML), 2013. ,
Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning, 2016. ,
Going deeper with convolutions, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.13-16, 2015. ,
DOI : 10.1109/CVPR.2015.7298594
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
Max-Margin Markov Networks, Advances in Neural Information Processing Systems (NIPS) (cit, p.22, 2003. ,
Attention Networks for Weakly Supervised Object Localization, Procedings of the British Machine Vision Conference 2016, p.27, 2016. ,
DOI : 10.5244/C.30.52
RMSprop Gradient Optimization, 2012. ,
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
Large Margin Methods for Structured and Interdependent Output Variables, Journal of Machine Learning Research (JMLR), vol.24, p.55, 2005. ,
Principles of risk minimization for learning theory, Advances in Neural Information Processing Systems (NIPS) (cit, p.12, 1991. ,
The Nature of Statistical Learning Theory, 1995. ,
The Caltech-UCSD Birds-200-2011 Dataset, p.28, 2011. ,
Robust and discriminative distance for multi-instance learning, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2012. ,
Solving the Multiple-Instance Problem: A Lazy Learning Approach, International Conference on Machine Learning (ICML), 2000. ,
Visual Tracking with Fully Convolutional Networks, 2015 IEEE International Conference on Computer Vision (ICCV), p.16, 2015. ,
DOI : 10.1109/ICCV.2015.357
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
Max- Margin Multiple-Instance Dictionary Learning, International Conference on Machine Learning (ICML) (cit, p.68, 2013. ,
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
CNN: Single-label to Multi-label, 2014. ,
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
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
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. ,
Aggregated Residual Transformations for Deep Neural Networks, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016. ,
DOI : 10.1109/CVPR.2017.634
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
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
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
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
Kernel Latent SVM for Visual Recognition, Advances in Neural Information Processing Systems (NIPS) (cit, pp.47-48, 2012. ,
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
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
?SVM for Learning with Label Proportions, International Conference on Machine Learning (ICML) (cit, pp.94-113, 2013. ,
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
The Concave-Convex Procedure, Neural Computation (cit, pp.37-55, 2003. ,
DOI : 10.1162/08997660260028674
A MultiPath Network for Object Detection, Procedings of the British Machine Vision Conference 2016, p.16, 2016. ,
DOI : 10.5244/C.30.15
ADADELTA: An Adaptive Learning Rate Method, 2012. ,
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
Stochastic pooling for regularization of deep convolutional neural networks, International Conference on Learning Representations (ICLR) (cit, p.16, 2013. ,
MILEAGE: Multiple Instance LEArning with Global Embedding, International Conference on Machine Learning (ICML) (cit, p.45, 2013. ,
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
Instance-Level Segmentation with Deep Densely Connected MRFs, 2016. ,
DOI : 10.1109/cvpr.2016.79
URL : http://arxiv.org/pdf/1512.06735
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
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
Semantic understanding of scenes through the ade20k dataset, 2016. ,
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
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
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
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
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 ,