Support vector machines for multiple-instance learning, NIPS, 2003. ,
NetVLAD: CNN architecture for weakly supervised place recognition, CVPR, 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
Optimizing average precision using weakly supervised data, CVPR, p.5, 2014. ,
URL : https://hal.archives-ouvertes.fr/hal-00984699
Object and Action Classification with Latent Window Parameters, IJCV, 2013. 1 ,
DOI : 10.1007/s11263-013-0646-8
Tutorial: Visual learning with weak supervision ,
Return of the Devil in the Details: Delving Deep into Convolutional Nets, Proceedings of the British Machine Vision Conference 2014 ,
DOI : 10.5244/C.28.6
Semantic image segmentation with deep convolutional nets and fully connected crfs, 2015. ,
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
MANTRA: Minimum Maximum Latent Structural SVM for Image Classification and Ranking, 2015 IEEE International Conference on Computer Vision (ICCV), 2006. ,
DOI : 10.1109/ICCV.2015.311
URL : https://hal.archives-ouvertes.fr/hal-01343784
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 (VOC) Challenge, International Journal of Computer Vision, vol.73, issue.2, 2007. ,
DOI : 10.1007/s11263-009-0275-4
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
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
Learning Deep Hierarchical Visual Feature Coding, IEEE Transactions on Neural Networks and Learning Systems, 2014. ,
DOI : 10.1109/TNNLS.2014.2307532
URL : https://hal.archives-ouvertes.fr/hal-01185465
Multi-scale Orderless Pooling of Deep Convolutional Activation Features, ECCV, 2014 ,
DOI : 10.1007/978-3-319-10584-0_26
Spatial pyramid pooling in deep convolutional networks for visual recognition, ECCV, 2006. ,
Caffe, Proceedings of the ACM International Conference on Multimedia, MM '14, 2014. ,
DOI : 10.1145/2647868.2654889
Blocks That Shout: Distinctive Parts for Scene Classification, 2013 IEEE Conference on Computer Vision and Pattern Recognition, 2013. ,
DOI : 10.1109/CVPR.2013.124
Imagenet classification with deep convolutional neural networks, NIPS. 2012. 1 ,
Self-paced learning for latent variable models, NIPS, 2010. ,
Video Event Detection by Inferring Temporal Instance Labels, 2014 IEEE Conference on Computer Vision and Pattern Recognition, 2014. ,
DOI : 10.1109/CVPR.2014.288
Fantope Regularization in Metric Learning, 2014 IEEE Conference on Computer Vision and Pattern Recognition, 2014. ,
DOI : 10.1109/CVPR.2014.138
URL : https://hal.archives-ouvertes.fr/hal-01094074
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
Multiple instance learning for soft bags via top instances, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) ,
DOI : 10.1109/CVPR.2015.7299056
Object bank: A high-level image representation for scene classification & semantic feature sparsification, NIPS, 2010. ,
Network in network, ICLR, 2014. ,
URL : https://hal.archives-ouvertes.fr/hal-01460127
Microsoft COCO: Common Objects in Context, ECCV, 2005. ,
DOI : 10.1007/978-3-319-10602-1_48
Fully convolutional networks for semantic segmentation, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015. ,
DOI : 10.1109/CVPR.2015.7298965
Efficient optimization for average precision svm, 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, 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), 2006. ,
DOI : 10.1109/CVPR.2015.7298668
URL : https://hal.archives-ouvertes.fr/hal-01015140
Scene recognition and weakly supervised object localization with deformable part-based models, 2011 International Conference on Computer Vision, 2011. ,
DOI : 10.1109/ICCV.2011.6126383
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) ,
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 ,
DOI : 10.1109/CVPR.2012.6248001
Automatic discovery and optimization of parts for image classification, 2006. ,
Fisher Kernels on Visual Vocabularies for Image Categorization, 2007 IEEE Conference on Computer Vision and Pattern Recognition, 2007. ,
DOI : 10.1109/CVPR.2007.383266
Recognizing indoor scenes, 2009 IEEE Conference on Computer Vision and Pattern Recognition, 2009. ,
DOI : 10.1109/CVPR.2009.5206537
Objectcentric spatial pooling for image classification, ECCV, 2012. 1 ,
Latent Pyramidal Regions for Recognizing Scenes, ECCV, 2012 ,
DOI : 10.1007/978-3-642-33715-4_17
Robust object recognition with cortex-like mechanisms. PAMI, 2007. ,
Discriminative spatial saliency for image classification, 2012 IEEE Conference on Computer Vision and Pattern Recognition, 2012. ,
DOI : 10.1109/CVPR.2012.6248093
URL : https://hal.archives-ouvertes.fr/hal-00714311
Very deep convolutional networks for large-scale image recognition, 2006. ,
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
Learning Discriminative Part Detectors for Image Classification and Cosegmentation, 2013 IEEE International Conference on Computer Vision ,
DOI : 10.1109/ICCV.2013.422
URL : https://hal.archives-ouvertes.fr/hal-00932380
Dynamic Scene Classification: Learning Motion Descriptors with Slow Features Analysis, 2013 IEEE Conference on Computer Vision and Pattern Recognition, 2013. ,
DOI : 10.1109/CVPR.2013.336
Extended Coding and Pooling in the HMAX Model, IEEE Transactions on Image Processing, vol.22, issue.2, p.2013 ,
DOI : 10.1109/TIP.2012.2222900
Learning structural SVMs with latent variables, Proceedings of the 26th Annual International Conference on Machine Learning, ICML '09, 2009. ,
DOI : 10.1145/1553374.1553523
?svm for learning with label proportions, ICML, 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, p.5, 2007. ,
DOI : 10.1145/1277741.1277790
PANDA: Pose Aligned Networks for Deep Attribute Modeling, 2014 IEEE Conference on Computer Vision and Pattern Recognition, 2014. ,
DOI : 10.1109/CVPR.2014.212
Learning Deep Features for Scene Recognition using Places Database, NIPS, vol.5, issue.2 6, 2014. ,