Nicolas Audebert
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Nicolas Audebert

Associated Member
Team: Données Complexes, Apprentissage et Représentations
Personal website: https://nicolas.audebert.at
Phone: +33 1 40 27 22 97
Office: 33.1.23

Nicolas Audebert is an associate professor of Computer Science since 2019. His research focuses on representation learning for automated mapping, image generation and interpreation, and video games.

2026

Conference Articles

  1. Le Bellier, G. and Audebert, N. FlowEO: Generative Unsupervised Domain Adaptation for Earth Observation. In 2026 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), Tucson (AZ), United States, 2026. www 

2025

Journal Articles

  1. Ramzi, E.; Audebert, N.; Rambour, C.; Araujo, A.; Bitot, X. and Thome, N. Optimization of Rank Losses for Image Retrieval. In IEEE Transactions on Pattern Analysis and Machine Intelligence: 1-12, 2025. doi  www 
  1. Marsocci, V.; Jia, Y.; Le Bellier, G.; Kerekes, D.; Zeng, L.; Hafner, S.; Gerard, S.; Brune, E.; Yadav, R.; Shibli, A.; Fang, H.; Ban, Y.; Vergauwen, M.; Audebert, N. and Nascetti, A. PANGAEA: Assessing Geospatial Foundation Models Capabilities through a Global and Inclusive Benchmark. In IEEE geoscience and remote sensing magazine: 2-43, 2025. doi  www 
  1. Marsocci, V. and Audebert, N. Cross-sensor self-supervised training and alignment for remote sensing. In IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 18: 12278-12289, 2025. doi  www 

Conference Articles

  1. Okabayashi, A.; Pelletier, C.; Audebert, N.; Corpetti, T. and Courty, N. Flow matching pour la super-résolution d'images satellitaires Sentinel-2. In ORASIS 2025, Le Croisic, France, 2025. www 
  1. Le Bellier, G.; El Hajjar, D.; Breloy, A. and Audebert, N. Modèles de Flux Riemanniens pour l'Interférométrie SAR. In XXXème Colloque Francophone de Traitement du Signal et des Images (GRETSI 2025), Strabourg, France, 2025. www 
  1. Géré, L.; Audebert, N. and Jacquemard, F. Detecting Notational Errors in Digital Music Scores. In International Conference on Technologies for Music Notation and Representation (TENOR) 2025, arXiv, Beijing, China, 2025. doi  www 
  1. Audebert, N. and Breloy, A. 'Evaluation des générateurs d'images `a partir de peu déxemples : calculer le FID avec 10 fois moins d'images, cést possible. In XXXe Colloque Francophone de Traitement du Signal et des Images, Strasbourg, France, 2025. www 
  1. Mérizette, M.; Audebert, N.; Kervella, P. and Verdun, J. 3DSES: an indoor Lidar point cloud segmentation dataset with real and pseudo-labels from a 3D model. In VISAPP, Porto, Portugal, 2025. www 

PhD Theses

  1. Audebert, N. Learning representations from observations. Accreditation to supervise research, Université Paris-Est Sup, 2025.

2024

Conference Articles

  1. Doubinsky, P.; Audebert, N.; Crucianu, M. and Le Borgne, H. Semantic Generative Augmentations for Few-Shot Counting. In IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), Waikoloa, Hawaii, United States, 2024. doi  www 
  1. Géré, L.; Audebert, N. and Rigaux, P. Improved symbolic drum style classification with grammar-based hierarchical representations. In Proceedings of the 25th International Society for Music Information Retrieval Conference, San Francisco, United States, 2024. www 
  1. Le Bellier, G. and Audebert, N. Detecting Out-Of-Distribution Earth Observation Images with Diffusion Models. In 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pages 481-491, IEEE, Seattle, United States, 2024. doi  www 
  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. Okabayashi, A.; Audebert, N.; Donike, S. and Pelletier, C. Cross-sensor super-resolution of irregularly sampled Sentinel-2 time series. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, pages 502-511, IEEE, Seattle, United States, 2024. doi  www 

2023

Conference Articles

  1. Doubinsky, P.; Audebert, N.; Crucianu, M. and Le Borgne, H. Wasserstein Loss for Semantic Editing in the Latent Space of GANs. In CBMI '23: Proceedings of the 20th International Conference on Content-based Multimedia Indexing, Orléans, France, 2023. doi  www 

2022

Journal Articles

  1. Doubinsky, P.; Audebert, N.; Crucianu, M. and Le Borgne, H. Multi-attribute balanced sampling for disentangled GAN controls. In Pattern Recognition Letters, 162: 56-62, 2022. doi  www 
  1. Castillo-Navarro, J.; Le Saux, B.; Boulch, A.; Audebert, N. and Lefèvre, S. Semi-Supervised Semantic Segmentation in Earth Observation: The MiniFrance suite, dataset analysis and multi-task network study. In Machine Learning, 111: 3125-3160, 2022. doi  www 

Conference Articles

  1. Audebert, N. and Laporte, M. Caractérisation du répertoire vocal des chimpanzés par apprentissage profond. In Reconnaissance des Formes, Image, Apprentissage et Perception (RFIAP), Vannes, France, 2022. www 
  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. Boige, R.; Audebert, N.; Rambour, C. and Levieux, G. Now you see me: finding the right observation space to learn diverse behaviours by reinforcement in games. In Conférence sur l'Apprentissage automatique (CAp), Vannes, France, 2022. www 
  1. Cheng, X.; Zayani, R.; Ferecatu, M. and Audebert, N. Efficient Autoprecoder-based deep learning for massive MU-MIMO Downlink under PA Non-Linearities. In IEEE Wireless Communications and Networking Conference, Austin, United States, 2022. www 

Miscellaneous

  1. Doubinsky, P.; Audebert, N.; Crucianu, M. and Le Borgne, H. Contr^ole de la cardinalité par navigation dans léspace latent des GANs. , Poster. www 

2021

Conference Articles

  1. Dang, C.; Randrianarivo, H.; Fournier-S'Niehotta, R. and Audebert, N. Web Image Context Extraction with Graph Neural Networks and Sentence Embeddings on the DOM tree. In Machine Learning and Principles and Practice of Knowledge Discovery in Databases. ECML PKDD 2021, pages 258-267, IEEE, Bilbao, Spain, 2021. doi  www 
  1. Foscarin, F.; Audebert, N. and Fournier-S'Niehotta, R. PKSpell: Data-Driven Pitch Spelling and Key Signature Estimation. In International Society for Music Information Retrieval Conference (ISMIR), Online, India, 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 

2020

Journal Articles

  1. Rambour, C.; Audebert, N.; Koeniguer, E.; Le Saux, B.; Crucianu, M. and Datcu, M. Flood detection in time series of optical and sar images. In ISPRS International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLIII-B2-2020: 1343-1346, 2020. doi  www 

Conference Articles

  1. Dubucq, D.; Audebert, N.; Achard, V.; Alakian, A.; Fabre, S.; Credoz, A.; Deliot, P. and Le Saux, B. A real-world hyperspectral image processing workflow for vegetation stress and hydrocarbon indirect detection. In The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Nice, France, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2020, 2020. doi  www 

2019

Journal Articles

  1. Audebert, N.; Saux, B. Le and Lefèvre, S. Deep Learning for Classification of Hyperspectral Data: A Comparative Review. In IEEE geoscience and remote sensing magazine, 7 (2): 159-173, 2019. doi  www 
  1. Audebert, N.; Boulch, A.; Le Saux, B. and Lefèvre, S. Distance transform regression for spatially-aware deep semantic segmentation. In Computer Vision and Image Understanding, 189: 102809, 2019. doi  www 

Conference Articles

  1. Castillo-Navarro, J.; Audebert, N.; Boulch, A.; Le Saux, B. and Lefèvre, S. What Data are needed for Semantic Segmentation in Earth Observation?. In 2019 Joint Urban Remote Sensing Event (JURSE), pages 1-4, IEEE, Vannes, France, 2019. doi  www 
  1. Audebert, N.; Herold, C.; Slimani, K. and Vidal, C. Multimodal deep networks for text and image-based document classification. In Conférence Nationale sur les Applications Pratiques de l'Intelligence Artificielle (APIA), Toulouse, France, 2019. www 

2018

Journal Articles

  1. Audebert, N.; Le Saux, B. and Lefèvre, S. Beyond RGB: Very High Resolution Urban Remote Sensing With Multimodal Deep Networks. In ISPRS Journal of Photogrammetry and Remote Sensing, 140: 20-32, 2018. doi  www 

Conference Articles

  1. Audebert, N.; Le Saux, B. and Lefèvre, S. Generative Adversarial Networks for Realistic Synthesis of Hyperspectral Samples. In International Geoscience and Remote Sensing Symposium (IGARSS 2018), Valencia, Spain, 2018. doi  www 
  1. Chan-Hon-Tong, A. and Audebert, N. Object detection in remote sensing images with center only. In IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium, Valence, Spain, 2018. doi  www 
  1. Huang, B.; Lu, K.; Audebert, N.; Khalel, A.; Tarabalka, Y.; Malof, J.; Boulch, A.; Le Saux, B.; Collins, L.; Bradbury, K.; Lefèvre, S. and El-Saban, M. Large-scale semantic classification: outcome of the first year of Inria aerial image labeling benchmark. In IGARSS 2018 - IEEE International Geoscience and Remote Sensing Symposium, pages 1-4, Valencia, Spain, 2018. doi  www 
  1. Audebert, N.; Boulch, A.; Le Saux, B. and Lefèvre, S. Segmentation sémantique profonde par régression sur cartes de distances signées. In Reconnaissance des Formes, Image, Apprentissage et Perception (RFIAP), Marne-la-Vallée, France, 2018. www 

PhD Theses

  1. Audebert, N. Classification de données massives de télédétection. Ph.D. Thesis, Université de Bretagne Sud, 2018.

2017

Journal Articles

  1. Boulch, A.; Guerry, J.; Le Saux, B. and Audebert, N. SnapNet: 3D point cloud semantic labeling with 2D deep segmentation networks. In Computers and Graphics, 71: 189-198, 2017. doi  www 
  1. Audebert, N.; Le Saux, B. and Lefèvre, S. Segment-before-Detect: Vehicle Detection and Classification through Semantic Segmentation of Aerial Images. In Remote Sensing, 9 (4): page 1-18, 2017. doi  www 

Conference Articles

  1. Audebert, N.; Le Saux, B. and Lefèvre, S. Réseaux de neurones profonds et fusion de données pour la segmentation sémantique d'images aériennes. In ORASIS, Colleville-sur-Mer, France, 2017. www 
  1. Audebert, N.; Boulch, A.; Randrianarivo, H.; Le Saux, B.; Ferecatu, M.; Lefèvre, S. and Marlet, R. Deep learning for urban remote sensing. In 2017 Joint Urban Remote Sensing Event (JURSE), Duba"i, United Arab Emirates, 2017. doi  www 
  1. Audebert, N.; Le Saux, B. and Lefèvre, S. Couplage de données géographiques participatives et d'images aériennes par apprentissage profond. In GRETSI, Juan-Les-Pins, France, 2017. www 
  1. Audebert, N.; Le Saux, B. and Lefèvre, S. Fusion of Heterogeneous Data in Convolutional Networks for Urban Semantic Labeling (Invited Paper). In Joint Urban Remote Sensing Event 2017, Duba"i, United Arab Emirates, Joint Urban Remote Sensing Event 2017 , 2017. doi  www 
  1. Audebert, N.; Le Saux, B. and Lefèvre, S. Joint Learning from Earth Observation and OpenStreetMap Data to Get Faster Better Semantic Maps. In EARTHVISION 2017 IEEE/ISPRS CVPR Workshop. Large Scale Computer Vision for Remote Sensing Imagery, Honolulu, United States, 2017. doi  www 
  1. Ben Hamida, A.; Benoit, A.; Lambert, P.; Klein, L; Ben Amar, C.; Audebert, N. and Lefèvre, S. DEEP LEARNING FOR SEMANTIC SEGMENTATION OF REMOTE SENSING IMAGES WITH RICH SPECTRAL CONTENT. In IEEE International Geoscience and Remote Sensing Symposium, Fort Worth, United States, 2017. www 

2016

Conference Articles

  1. Audebert, N.; Le Saux, B. and Lefèvre, S. How Useful is Region-based Classification of Remote Sensing Images in a Deep Learning Framework?. In IEEE International Geosciences and Remote Sensing Symposium (IGARSS), Beijing, China, 2016. doi  www 
  1. Audebert, N.; Le Saux, B. and Lefèvre, S. On the usability of deep networks for object-based image analysis. In International Conference on Geographic Object-Based Image Analysis (GEOBIA), Enschede, Netherlands, 2016. www 
  1. Audebert, N.; Le Saux, B. and Lefèvre, S. Semantic Segmentation of Earth Observation Data Using Multimodal and Multi-scale Deep Networks. In Asian Conference on Computer Vision (ACCV16), Taipei, Taiwan, 2016. doi  www 
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