Thome

Nicolas Thome

Membre associé
Team: Données Complexes, Apprentissage et Représentations
Personal website: http://cedric.cnam.fr/~thomen/
Phone: +33 1 58 80 85 48
Office: 37.1.41

2024

Articles de revue

  1. Sun, R.; Masson, C.; Hénaff, G.; Thome, N. and Cord, M. Semantic augmentation by mixing contents for semi-supervised learning. In Pattern Recognition, 145: 109909, 2024. doi  www 
  1. Dalsasso, E.; Rambour, C.; Trouvé, N. and Thome, N. MERLIN-Seg: self-supervised despeckling for label-efficient semantic segmentation. In Computer Vision and Image Understanding, 241, 2024. doi  www 
  1. Karmim, Y.; Ramzi, E.; Fournier-S 'Niehotta, R. and Thome, N. ITEM: Improving Training and Evaluation of Message-Passing based GNNs for top-k recommendation. In Transactions on Machine Learning Research Journal, 2024. www 

Articles de conférence

  1. Lafon, M.; Ramzi, E.; Rambour, C.; Audebert, N. and Thome, N. GalLoP: Learning Global and Local Prompts for Vision-Language Models. In The 18th European Conference on Computer Vision ECCV 2024, arXiv, Milan, Italy, 2024. doi  www 
  1. Karmim, Y.; Lafon, M.; Fournier S'Niehotta, R. and Thome, N. Supra-Laplacian Encoding for Transformer on Dynamic Graphs. In The Thirty-eighth Annual Conference on Neural Information Processing Systems, Vancouver (CA), Canada, 2024. www 
  1. Karmim, Y.; Yang, L.; Fournier-S'Niehotta, R.; Chatelain, C.; Adam, S. and Thome, N. Temporal receptive field in dynamic graph learning: A comprehensive analysis. In MLG Workshop at ECML-PKDD, Vilnius (Lituanie), France, 2024. www 

2023

Articles de revue

  1. Brochet, C.; Raynaud, L.; Thome, N.; Plu, M. and Rambour, C. Multivariate Emulation of Kilometer-Scale Numerical Weather Predictions with Generative Adversarial Networks: A Proof of Concept. In Artificial Intelligence for the Earth Systems, 2 (4), 2023. doi  www 

Articles de conférence

  1. Themyr, L.; Rambour, C.; Thome, N.; Collins, T. and Hostettler, A. Full Contextual Attention for Multi-resolution Transformers in Semantic Segmentation. In 2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), pages 3223-3232, IEE Computer Society, Waikoloa, United States, 2023. doi  www 
  1. Janny, S.; Beneteau, A.; Nadri, M.; Digne, J.; Thome, N. and Wolf, C. EAGLE: Large-Scale Learning of Turbulent Fluid Dynamics with Mesh Transformers. In International Conference on Learning Representation, Kigali, Rwanda, 2023. www 
  1. Lafon, M.; Ramzi, E.; Rambour, C. and Thome, N. Hybrid Energy Based Model in the Feature Space for Out-of-Distribution Detection. In International Conference on Machine Learning, Honololu, Hawaii, United States, 2023. www 

Non publié

  1. Thome, N. and Wolf, C. Histoire des réseaux de neurones et du deep learning en traitement des signaux et des images. , working paper or preprint. www 
  1. Coquenet, D.; Rambour, C.; Dalsasso, E. and Thome, N. Leveraging Vision-Language Foundation Models for Fine-Grained Downstream Tasks. , working paper or preprint. www 

2022

Articles de revue

  1. Corbière, C.; Thome, N.; Saporta, A.; Vu, T-H.; Cord, M. and Perez, P. Confidence Estimation via Auxiliary Models. In IEEE Transactions on Pattern Analysis and Machine Intelligence, 44 (10): 6043-6055, 2022. doi  www 
  1. Thome, N.; Soler, L. and Petit, O. 3D Spatial Priors for Semi-Supervised Organ Segmentation with Deep Convolutional Neural Networks. In International Journal of Computer Assisted Radiology and Surgery, 17 (1): 129-139, 2022. doi  www 
  1. Le Guen, V. and Thome, N. Deep Time Series Forecasting with Shape and Temporal Criteria. In IEEE Transactions on Pattern Analysis and Machine Intelligence, 45 (1): 342-355, 2022. doi  www 

Articles de conférence

  1. Ramzi, E.; Audebert, N.; Thome, N.; Rambour, C. and Bitot, X. Hierarchical Average Precision Training for Pertinent Image Retrieval. In ECCV 2022, Tel-Aviv, Israel, 2022. www 
  1. Bavu, '.; Pujol, H.; Garcia, A.; Langrenne, C.; Hengy, S.; Rassy, O.; Thome, N.; Karmim, Y.; Schertzer, S. and Matwyschuk, A. Deeplomatics: A deep-learning based multimodal approach for aerial drone detection and localization. In QUIET DRONES Second International e-Symposium on UAV/UAS Noise, Paris, France, QUIET DRONES 2022 SECOND INTERNATIONAL SYMPOSIUM ON NOISE FROM UASs/UAVs and eVTOLs SYMPOSIUM PROCEEDINGS , 2022. www 
  1. Le Guen, V.; Rambour, C. and Thome, N. Complementing Brightness Constancy with Deep Networks for Optical Flow Prediction. In ECCV 2022, Springer, Tel Aviv, Israel, Lecture Notes in Computer Science, vol 13681 , 2022. doi  www 

2021

Articles de revue

  1. Yin, Y.; Le Guen, V.; Don`a, J.; de Bézenac, E.; Ayed, I.; Thome, N. and Gallinari, P. Augmenting physical models with deep networks for complex dynamics forecasting. In Journal of Statistical Mechanics: Theory and Experiment, 2021 (12): 124012, 2021. doi  www 
  1. Petit, O.; Thome, N. and Soler, L. Iterative Confidence Relabeling with Deep ConvNets for Organ Segmentation with Partial Labels. In Computerized Medical Imaging and Graphics: 101938, 2021. doi  www 

Articles de conférence

  1. Yin, Y.; Le Guen, V.; Dona, J.; Ayed, I.; de Bézenac, E.; Thome, N. and Gallinari, P. Augmenting physical models with deep networks for complex dynamics forecasting. In Ninth International Conference on Learning Representations ICLR 2021, Vienna (virtual), Austria, 2021. www 
  1. Petit, O.; Thome, N.; Rambour, C.; Themyr, L.; Collins, T. and Soler, L. U-Net Transformer: Self and Cross Attention for Medical Image Segmentation. In MICCAI workshop MLMI, Strasbourg (virtuel), France, 2021. www 
  1. Ramzi, E.; Thome, N.; Rambour, C.; Audebert, N. and Bitot, X. Robust and Decomposable Average Precision for Image Retrieval. In Thirty-fifth Conference on Neural Information Processing Systems (NeurIPS 2021), Sydney, Australia, 2021. www 
  1. Corbière, C.; Lafon, M.; Thome, N.; Cord, M. and Pérez, P. Beyond First-Order Uncertainty Estimation with Evidential Models for Open-World Recognition. In ICML 2021 Workshop on Uncertainty and Robustness in Deep Learning, Virtual, Austria, 2021. www 

2020

Articles de conférence

  1. Le Guen, V. and Thome, N. Disentangling Physical Dynamics from Unknown Factors for Unsupervised Video Prediction. In Computer Vision and Pattern Recognition 2020 (CVPR), Seattle, United States, 2020. doi  www 
  1. Le Guen, V. and Thome, N. A Deep Physical Model for Solar Irradiance Forecasting with Fisheye Images. In CVPR OmniCV worshop 2020, Seattle, United States, 2020. doi  www 
  1. Le Guen, V. and Thome, N. Probabilistic Time Series Forecasting with Structured Shape and Temporal Diversity. In NeurIPS 2020, Vancouver, Canada, 2020. www 

2019

Articles de revue

  1. Blot, M.; Picard, D.; Thome, N. and Cord, M. Distributed Optimization for Deep Learning with Gossip Exchange. In Neurocomputing, 330: 287-296, 2019. doi  www 
  1. Durand, T.; Thome, N. and Cord, M. Exploiting Negative Evidence for Deep Latent Structured Models. In IEEE Transactions on Pattern Analysis and Machine Intelligence, 41 (2): 337-351, 2019. doi  www 

Articles de conférence

  1. Ben-Younes, H.; Cadene, R.; Thome, N. and Cord, M. BLOCK: Bilinear Superdiagonal Fusion for Visual Question Answering and Visual Relationship Detection. In AAAI 2019 - 33rd AAAI Conference on Artificial Intelligence, Honolulu, United States, 2019. www 
  1. Le Guen, V. and Thome, N. Prévision de l'irradiance solaire par réseaux de neurones profonds `a l'aide de caméras au sol. In GRETSI 2019, Lille, France, 2019. www 
  1. Cadene, R.; Ben-Younes, H.; Cord, M. and Thome, N. MUREL: Multimodal Relational Reasoning for Visual Question Answering. In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, United States, 2019. www 
  1. Le Guen, V. and Thome, N. Shape and Time Distortion Loss for Training Deep Time Series Forecasting Models. In Advances in Neural Information Processing Systems 32 (NeurIPS 2019), Vancouver, Canada, Advances in Neural Information Processing Systems 32 (NIPS 2019) proceedings 4191--4203, 2019. www 
  1. Corbière, C.; Thome, N.; Bar-Hen, A.; Cord, M. and Pérez, P. Addressing Failure Prediction by Learning Model Confidence. In 33rd Conference on Neural Information Processing Systems (NeurIPS 2019), pages 2898-2909, Curran Associates, Inc., Vancouver, Canada, 2019. www 

Divers

  1. Le, T-L.; Thome, N.; Bernard, S.; Bismuth, V. and Patoureaux, F. Multitask Classification and Segmentation for Cancer Diagnosis in Mammography. , Annotation cost is a bottleneck for collecting massive data in mammography, especially for training deep neural networks. In this paper, we study the use of heterogeneous levels of annotation granularity to improve predictive performances. More precisely, we introduce a multi-task learning scheme for training convolutional neural network (ConvNets), which combines segmentation and classification, using image-level and pixel-level annotations. In this way, different objectives can be used to regularize training by sharing intermediate deep representations. Successful experiments are carried out on the Digital Database of Screening Mammography (DDSM) to validate the relevance of the proposed approach. www 

2018

Articles de revue

  1. Mordan, T.; Thome, N.; Henaff, G. and Cord, M. End-to-End Learning of Latent Deformable Part-Based Representations for Object Detection. In International Journal of Computer Vision, 2018. doi  www 
  1. Chevalier, M.; Thome, N.; Henaff, G. and Cord, M. Classifying low-resolution images by integrating privileged information in deep CNNs. In Pattern Recognition Letters, 116: 29-35, 2018. doi  www 
  1. Durand, T.; Thome, N. and Cord, M. SyMIL: MinMax Latent SVM for Weakly Labeled Data. In IEEE Transactions on Neural Networks and Learning Systems, 29 (12): 6099-6112, 2018. doi  www 

Articles de conférence

  1. Robert, T.; Thome, N. and Cord, M. HybridNet: Classification and Reconstruction Cooperation for Semi-supervised Learning. In ECCV 2018 - 15th European Conference on Computer Vision, pages 158-175, Springer, Munich, Germany, Lecture Notes in Computer Science 11211, 2018. doi  www 
  1. Petit, O.; Thome, N.; Charnoz, A.; Hostettler, A. and Soler, L. Handling Missing Annotations for Semantic Segmentation with Deep ConvNets. In MICCAI workshop DLMIA, pages 20-28, Springer, Grenade, Spain, Lecture Notes in Computer Science book series (LNIP,volume 11045) , 2018. doi  www 
  1. Blot, M.; Robert, T.; Thome, N. and Cord, M. SHADE: Information-Based Regularization for Deep Learning. In ICIP 2018 - 25th IEEE International Conference on Image Processing, pages 813-817, IEEE, Athènes, Greece, 2018. doi  www 
  1. Mordan, T.; Thome, N.; Henaff, G. and Cord, M. Revisiting Multi-Task Learning with ROCK: a Deep Residual Auxiliary Block for Visual Detection. In 32nd Conference on Neural Information Processing Systems (NeurIPS), Montréal, Canada, 2018. www 
  1. Carvalho, M.; Cadène, R.; Picard, D.; Soulier, L.; Thome, N. and Cord, M. Cross-Modal Retrieval in the Cooking Context. In SIGIR '18: The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, pages 35-44, ACM Press, Ann Arbor, Michigan, United States, 2018. doi  www 

2017

Articles de revue

  1. Wang, X.; Thome, N. and Cord, M. Gaze Latent Support Vector Machine for Image Classification Improved by Weakly Supervised Region Selection. In Pattern Recognition, 72: 59-71, 2017. doi  www 

Articles de conférence

  1. Mordan, T.; Thome, N.; Cord, M. and Henaff, G. Deformable Part-based Fully Convolutional Network for Object Detection. In British Machine Vision Conference (BMVC), London, United Kingdom, 2017. www 
  1. Durand, T.; Mordan, T.; Thome, N. and Cord, M. WILDCAT: Weakly Supervised Learning of Deep ConvNets for Image Classification, Pointwise Localization and Segmentation. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2017), pages 5957-5966, Honolulu, HI, United States, 2017. doi  www 
  1. Ben-Younes, H.; Cadene, R.; Cord, M. and Thome, N. MUTAN: Multimodal Tucker Fusion for Visual Question Answering. In 2017 IEEE International Conference on Computer Vision (ICCV), pages 2631-2639, IEEE, Venice, Italy, 2017 IEEE International Conference on Computer Vision (ICCV) , 2017. doi  www 

2016

Articles de revue

  1. Law, M. T.; Thome, N. and Cord, M. Learning a Distance Metric from Relative Comparisons between Quadruplets of Images. In International Journal of Computer Vision: 1-30, 2016. doi  www 

Articles de conférence

  1. Carvalho, M.; Cord, M.; Avila, S.; Thome, N. and Valle, E. Deep Neural Networks Under Stress. In IEEE International Conference on Image Processing (ICIP 2016), Phoenix, AZ, United States, 2016. doi  www 
  1. Durand, T.; Thome, N. and Cord, M. WELDON: Weakly Supervised Learning of Deep Convolutional Neural Networks. In 29th IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2016), Las Vegas, NV, United States, 2016. www 
  1. Blot, M.; Cord, M. and Thome, N. Max-min convolutional neural networks for image classification. In ICIP 2016 - IEEE International Conference on Image Processing, pages 3678-3682, IEEE, Phoenix, United States, 2016. doi  www 
  1. Chevalier, M.; Thome, N.; Cord, M.; Fournier, J.; Henaff, G. and Dusch, E. LOW RESOLUTION CONVOLUTIONAL NEURAL NETWORK FOR AUTOMATIC TARGET RECOGNITION. In 7th International Symposium on Optronics in Defence and Security, Paris, France, 2016. www 
  1. Wang, X.; Thome, N. and Cord, M. GAZE LATENT SUPPORT VECTOR MACHINE FOR IMAGE CLASSIFICATION. In IEEE International Conference on Image Processing (ICIP), Phoenix, AZ, United States, 2016. doi  www 

2015

Articles de conférence

  1. Law, M. T.; Thome, N.; Ganc carski, S. and Cord, M. Apprentissage de métrique appliqué `a la détection de changement de page Web et aux attributs relatifs. In CORIA 2015 - Conférence en Recherche d'Infomations et Applications - 12th French Information Retrieval Conference, Paris, France, 2015. www 
  1. Wang, X.; Kumar, D.; Thome, N.; Cord, M. and Precioso, F. RECIPE RECOGNITION WITH LARGE MULTIMODAL FOOD DATASET. In IEEE International Conference on Multimedia & Expo (ICME), workshop CEA, Turin, Italy, 2015. doi  www 
  1. Le Barz, C.; Thome, N.; Cord, M.; Herbin, S. and Sanfourche, M. Absolute geo-localization thanks to Hidden Markov Model and exemplar-based metric learning. In 6th international Workshop on Computer Vision in Vehicle Technology, CHICAGO, United States, 2015. www 
  1. Chevalier, M.; Thome, N.; Cord, M.; Fournier, J.; Henaff, G. and Dusch, E. LR-CNN FOR FINE-GRAINED CLASSIFICATION WITH VARYING RESOLUTION. In IEEE International Conference on Image Processing, Québec city, Canada, 2015. www 
  1. Durand, T.; Thome, N. and Cord, M. MANTRA: Minimum Maximum Latent Structural SVM for Image Classification and Ranking. In IEEE International Conference on Computer Vision (ICCV15), Santiago, Chile, 2015. www 

Divers

  1. Le Barz, C.; Thome, N.; Cord, M.; Herbin, S. and Sanfourche, M. EXEMPLAR BASED METRIC LEARNING FOR ROBUST VISUAL LOCALIZATION. , Poster. www 

2014

Articles de revue

  1. Theriault, C.; Thome, N.; Cord, M. and Pérez, P. Perceptual principles for video classification with Slow Feature Analysis. In IEEE Journal of Selected Topics in Signal Processing, 8 (3): 428-437, 2014. doi  www 
  1. Goh, H.; Thome, N.; Cord, M. and Lim, J-H. Learning Deep Hierarchical Visual Feature Coding. In IEEE Transactions on Neural Networks and Learning Systems, 25 (12): 2212-2225, 2014. doi  www 
  1. Minetto, R.; Thome, N.; Cord, M.; Leite, N. J. and Stolfi, J. SnooperText: A Text Detection System for Automatic Indexing of Urban Scenes. In Computer Vision and Image Understanding, 122: 92-104, 2014. doi  www 

Chapitres d'ouvrage

  1. Law, M. T.; Thome, N. and Cord, M. Bag-of-Words Image Representation: Key Ideas and Further Insight. In Fusion in Computer Vision - Understanding Complex Visual Content, pages 29-52, Springer, Advances in Computer Vision and Pattern Recognition , 2014. doi  www 

Articles de conférence

  1. Le Barz, C.; Thome, N.; Cord, M.; Herbin, S. and Sanfourche, M. Global Robot Ego-localization Combining Image Retrieval and HMM-based Filtering. In 6th Workshop on Planning, Perception and Navigation for Intelligent Vehicles, pages 6 p., Chicago, United States, 2014. www 
  1. Durand, T.; Picard, D.; Thome, N. and Cord, M. SEMANTIC POOLING FOR IMAGE CATEGORIZATION USING MULTIPLE KERNEL LEARNING. In IEEE International Conference on Image Processing, pages -, Institute of Electrical and Electronics Engineers, Paris, France, 2014. www 
  1. Law, M. T; Thome, N. and Cord, M. Fantope Regularization in Metric Learning. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 1051-1058, Columbus, Ohio, United States, 2014. doi  www 
  1. Dulac-Arnold, G.; Denoyer, L.; Thome, N.; Cord, M. and Gallinari, P. Sequentially Generated Instance-Dependent Image Representations for Classification. In International Conference on Learning Representations, ICLR 2014, Banff, Canada, 2014. www 
  1. Durand, T.; Thome, N.; Cord, M. and Picard, D. Incremental learning of latent structural SVM for weakly supervised image classification. In IEEE International Conference on Image Processing, pages 4246-4250, IEEE, Paris, France, 2014. doi  www 

2013

Articles de revue

  1. Theriault, C.; Thome, N. and Cord, M. Extended Coding and Pooling in the HMAX Model. In IEEE Transactions on Image Processing, 22 (2): 764-777, 2013. doi  www 
  1. Minetto, R.; Thome, N.; Cord, M.; Leite, N. J. and Stolfi, J. T-HOG: an Effective Gradient-Based Descriptor for Single Line Text Regions. In Pattern Recognition, 46 (3): 1078-1090, 2013. doi  www 
  1. Picard, D.; Thome, N. and Cord, M. JKernelMachines: A Simple Framework for Kernel Machines. In Journal of Machine Learning Research, 14: 1417-1421, 2013. www 
  1. Avila, S.; Thome, N.; Cord, M.; Valle, E. and de Albuquerque Ara'ujo, A. Pooling in Image Representation: the Visual Codeword Point of View. In Computer Vision and Image Understanding, 117 (5): 453-465, 2013. doi  www 

Articles de conférence

  1. Avila, S.; Thome, N.; Cord, M.; Valle, E. and de Albuquerque Ara'ujo, A. Extended Bag-of-Words Formalism for Image Classification. In Brazilian Symposium on Computer Graphics and Image Processing, Arequipa, Peru, 2013. www 
  1. Theriault, C.; Thome, N. and Cord, M. Dynamic Scene Classification: Learning Motion Descriptors with Slow Features Analysis. In IEEE Conference on Computer Vision and Pattern Recognition, pages 2603-2610, IEEE, Portland, OR, United States, 2013. doi  www 
  1. Law, M. T; Thome, N. and Cord, M. Quadruplet-Wise Image Similarity Learning. In IEEE International Conference on Computer Vision (ICCV), pages 249-256, Sydney, Australia, 2013. doi  www 
  1. Goh, H.; Thome, N.; Cord, M. and Lim, J-H. Top-Down Regularization of Deep Belief Networks. In Advances in Neural Information Processing Systems 26, pages 1878-1886, Lake Tahoe, United States, 2013. www 
  1. Durand, T.; Thome, N.; Cord, M. and Avila, S. Image classification using object detectors. In IEEE International Conference on Image Processing, pages 4340-4344, Melbourne, Australia, 2013. doi  www 

2012

Articles de conférence

  1. Law, M. T.; Thome, N.; Ganc carski, S. and Cord, M. Structural and Visual Comparisons for Web Page Archiving. In 12th edition of the ACM Symposium on Document Engineering, DocEng'12, pages 117-120, ACM, Paris, France, 2012. doi  www 
  1. Law, M. T.; Sureda Gutierrez, C.; Thome, N.; Ganc carski, S. and Cord, M. Structural and Visual Similarity Learning for Web Page Archiving. In 10th workshop on Content-Based Multimedia Indexing (CBMI), pages 1-6, IEEE, Annecy, France, 2012. doi  www 
  1. Law, M. T.; Thome, N. and Cord, M. Hybrid Pooling Fusion in the BoW Pipeline. In ECCV 2012 Workshop on Information fusion in Computer Vision for Concept Recognition (ECCV-IFCVCR 2012), pages 355-364, Springer, Florence, Italy, Lecture Notes in Computer Science 7585, 2012. doi  www 
  1. Picard, D.; Thome, N.; Cord, M. and Rakotomamonjy, A. Learning geometric combinations of Gaussian kernels with alternating Quasi-Newton algorithm. In 20th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, pages 79-84, Bruges, Belgium, 2012. www 
  1. Guyomard, J.; Thome, N.; Cord, M. and Artières, T. Contextual Detection of Drawn Symbols in Old Maps. In International Conference on Image Processing (ICIP), pages 837-840, IEEE, Orlando, Florida, United States, 2012. doi  www 
  1. Goh, H.; Thome, N.; Cord, M. and Lim, J-H. Unsupervised and supervised visual codes with restricted Boltzmann machines. In 12th European conference on Computer Vision, pages 298-311, Florence, Italy, Lecture Notes in Computer Science 7576, 2012. doi  www 
  1. Iovan, C.; Picard, D.; Thome, N. and Cord, M. Classification of Urban Scenes from Geo-referenced Images in Urban Street-View Context. In Machine Learning and Applications (ICMLA), 2012 11th International Conference on, pages 339-344, Boca Raton, Florida, United States, 2012. www 
  1. Boufarguine, M.; Thome, N.; Guitteny, V. and Precioso, F. Suivi 3D Monoculaire pour un Système de Vidéosurveillance `a l'aide d'un Modèle de Mouvement et un Modèle d'Apparence. In RFIA 2012 (Reconnaissance des Formes et Intelligence Artificielle), Lyon, France, 2012. www 

2011

Articles de revue

  1. Thome, N.; Vacavant, A.; Robinault, L. and Miguet, S. A cognitive and video-based approach for multinational License Plate Recognition. In Machine Vision and Applications, 22 (2): 389-407, 2011. doi  www 

Articles de conférence

  1. Minetto, R.; Thome, N.; Cord, M.; Stolfi, J.; Precioso, F.; Guyomard, J. and Leite, N. J. Text Detection and Recognition in Urban Scenes. In International Conference on Computer Vision (ICCV): Workshop on Computer Vision for Remote Sensing of the Environment, pages 227-234, IEEE, Barcelona, Spain, 2011. doi  www 
  1. Precioso, F.; Cord, M.; Gorisse, D. and Thome, N. Efficient bag-of-feature kernel representation for image similarity search. In ICIP 2011 - IEEE International Conference on Image Processing, pages 109-112, IEEE, Bruxelles, Belgium, 2011. doi  www 
  1. Theriault, C.; Thome, N. and Cord, M. HMAX-S: DEEP SCALE REPRESENTATION FOR BIOLOGICALLY INSPIRED IMAGE CATEGORIZATION. In IEEE International Conference on Image Processing, pages 1261-1264, IEEE, Brussels, Belgium, 2011. doi  www 
  1. Avila, S.; Thome, N.; Cord, M.; Valle, E. and de Albuquerque Ara'ujo, A. BOSSA: extended BoW formalism for image classification. In IEEE International Conference on Image Processing (ICIP), pages 2909-2912, IEEE, Brussels, Belgium, 2011. doi  www 
  1. Goh, H.; Łukasz, K.; Lim, J-H.; Thome, N. and Cord, M. Learning Invariant Color Features with Sparse Topographic Restricted Boltzmann Machines. In ICIP 2011 - IEEE International Conference on Image Processing, pages 1241-1244, Brussels, Belgium, 2011. doi  www 
  1. Minetto, R.; Thome, N.; Cord, M.; Leite, N. J. and Stolfi, J. SNOOPERTRACK: TEXT DETECTION AND TRACKING FOR OUTDOOR VIDEOS. In IEEE International Conference on Image Processing (ICIP), pages 505-508, IEEE, Brussels, Belgium, 2011. doi  www 

2010

Articles de conférence

  1. Goh, H.; Thome, N. and Cord, M. Biasing Restricted Boltzmann Machines to Manipulate Latent Selectivity and Sparsity. In NIPS 2010 Workshop on Deep Learning and Unsupervised Feature Learning, Vancouver, Canada, 2010. www 
  1. Miguet, S.; Ilyas, A.; Pop, I.; Robinault, L.; Scuturici, M. and Thome, N. Analyse de l'activité humaine dans les séquences vidéo. In Ecole de Préparation `a la Recherche Appliquée : Vidéosurveillance Industrielle et Sécuritaire, pages inconnue, Ile de Kerkennah, Tunisia, 2010. www 
  1. Merad, D.; Aziz, K-E. and Thome, N. Fast People Counting using Head Detection from Skeleton Graph. In IEEE International Conference on Advanced Video and Signal based Surveillance (AVSS), pages 233-240, IEEE, Boston, MA, United States, 2010. doi  www 
  1. Picard, D.; Thome, N. and Cord, M. An efficient System for combining complementary kernels in complex visual categorization tasks. In ICIP 2010 - 17th IEEE International Conference on Image Processing, pages 3877-3880, IEEE, Hong Kong, Hong Kong SAR China, 2010. doi  www 
  1. Minetto, R.; Thome, N.; Cord, M.; Fabrizio, J. and Marcotegui, B. Snoopertext: A multiresolution system for text detection in complex visual scenes. In ICIP 2010 - 17th IEEE International Conference on Image Processing, pages 3861-3864, IEEE, Hong-Kong, Hong Kong SAR China, 2010. doi  www 

2008

Articles de revue

  1. Thome, N.; Miguet, S. and Ambellouis, S. A Real-Time, Multi-View Fall Detection System: a LHMM-Based Approach. In IEEE Transactions on Circuits and Systems for Video Technology, 18 (11): 1522-1532, 2008. doi  www 
  1. Thome, N.; Merad, D. and Miguet, S. Learning Articulated Appearance Models for Tracking Humans: a Spectral Graph Matching Approach. In Signal Processing: Image Communication, 23 (10): 769-787, 2008. doi  www 

2007

Articles de conférence

  1. Thome, N. and Vacavant, A. A Combined Statistical-Structural Strategy for Alphanumeric Recognition. In 3rd International Symposium on Visual Computing (ISCV 2007), pages 529-538, Springer, Lake Tahoe, United States, 2007. doi  www 

Non publié

  1. Thome, N. Représentations hiérarchiques et discriminantes pour la reconnaissance des formes, l'identification des personnes et l'analyse des mouvements dans les séquences d'images. , working paper or preprint. www 

2006

Articles de conférence

  1. Thome, N. and Miguet, S. A HHMM-Based Approach for Robust Fall Detection. In 9th International Conference on Control, Automation, Robotics & Vision, ICARCV'06, pages 1-8, IEEE, Singapore, Singapore, 2006. doi  www 
  1. Thome, N.; Merad, D. and Miguet, S. Human Body Part Labeling and Tracking Using Graph Matching Theory. In International Conference on Advanced Video and Signal based Surveillance (IEEE AVSS), pages 38-38, IEEE Computer Society, Sydney, Australia, 2006. www 

2005

Articles de conférence

  1. Thome, N. and Miguet, S. A Robust Appearance Model for Tracking Human Motions. In AVSS (IEEE International Conference on Advanced Video and Signal-Based Surveillance), pages 528-533, Como, Italy, 2005. www 
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