Publications 2008-2025

• 2025
  • H. Sahbi : “Label-Efficient Skeleton-based Recognition with Stable Graph Convnets”, The 2025 IEEE International Conference on Content-Based Multimedia Indexing (IEEE CBMI), Dublin, Ireland (2025)
  • H. Sahbi : “GENERATIVE VIRTUAL EXEMPLAR LEARNING FOR LABEL-EFFICIENT SATELLITE IMAGE CHANGE DETECTION”, The IEEE International Geoscience and Remote Sensing Symposium (IEEE IGARSS), Brisbane, Australia (2025)
  • H. Sahbi : “INVERTIBLE NETWORKS FOR IMAGE AUGMENTATION IN INTERACTIVE SATELLITE IMAGE CHANGE DETECTION”, The IEEE International Geoscience and Remote Sensing Symposium (IEEE IGARSS), Brisbane, Australia (2025)
• 2024
  • V. Enescu, H. Sahbi : “Learning Classwise Untangled Continuums for Conditional Normalizing Flows”, https://link.springer.com/conference/accv (à paraitre), Hanoi, Viet Nam (2024)
  • V. Enescu, H. Sahbi : “Learning Conditionally Untangled Latent Spaces using Fixed Point Iteration”, The British Machine Vision Conference (BMVC), Glasgow, United Kingdom (2024)
  • M. Jiu, J. Mei, H. Sahbi, X. Jiang, M. Xu : “Sparse Context Transformer for Few-Shot Object Detection”, The International Conference on Artificial Intelligence (PRICAI), Kyoto, Japan (2024)
  • H. Sahbi : “Coarse-to-Fine Pruning of Graph Convolutional Networks for Skeleton-based Recognition”, The International Conference on Content-based Multimedia Indexing (CBMI), Reykjavik, Iceland (2024)
  • V. Enescu, H. Sahbi : “Conditional normalizing flows for nonlinear remote sensing image augmentation and classification”, IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium, Athens, Greece, pp. 10264-10268, (IEEE), (ISBN: 979-8-3503-6032-5) (2024)
  • H. Sahbi : “Coarse-to-Fine Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition”, International Conference on Machine Learning (ICML), Vienna, Austria (2024)
  • R. Marsal, F. Chabot, A. Loesch, W. Grolleau, H. Sahbi : “MonoProb: self-supervised monocular depth estimation with interpretable incertainty”, Online proceedings of the 2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), Waikoloa, HI, USA, 2024, Waikoloa, HI, United States, pp. 3625-3634, (IEEE), (ISBN: 979-8-3503-1892-0) (2024)
  • H. Sahbi : “DAMP: distribution-aware magnitude pruning for budget-sensitive graph convolutional networks”, ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Seoul, Korea, Republic of, pp. 3070-3074, (IEEE), (ISBN: 979-8-3503-4485-1) (2024)
  • H. Sahbi : “TCMP: end-to-end topologically consistent magnitude pruning for miniaturized graph convolutional networks”, ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Seoul, Korea, Republic of, pp. 3065-3069, (IEEE), (ISBN: 979-8-3503-4485-1) (2024)
  • Ah. Mazari, H. Sahbi : “Deep multiple aggregation networks for action recognition”, International Journal of Multimedia Information Retrieval, vol. 13, pp. 9, (Springer) (2024)
• 2023
  • H. Sahbi : “MRMP: Multi-Rate Magnitude Pruning of Graph Convolutional Networks”, ES-FoMo Workshop at the International Conference on Machine Learning – ICML 2023, Honululu, United States (2023)
  • H. Sahbi, S. Deschamps : “ADVERSARIAL LABEL-EFFICIENT SATELLITE IMAGE CHANGE DETECTION”, IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, Pasadena, United States, pp. 5794-5797, (IEEE) (2023)
  • H. Sahbi, S. Deschamps : “DEEP-NET INVERSION FOR FRUGAL SATELLITE IMAGE CHANGE DETECTION”, IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, Pasadena, United States, pp. 6470-6473, (IEEE) (2023)
  • H. Sahbi : “Phase-field Models for Lightweight Graph Convolutional Networks”, 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Vancouver, Canada, pp. 4644-4650, (IEEE) (2023)
  • R. Marsal, F. Chabot, A. Loesch, H. Sahbi : “BrightFlow: Brightness-Change-Aware Unsupervised Learning of Optical Flow”, 2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), Waikoloa, United States, pp. 2060-2069, (IEEE), (ISBN: 978-1-6654-9346-8) (2023)
• 2022
  • R. Dupont, M. Alaoui, H. Sahbi, A. Lebois : “EXTRACTING EFFECTIVE SUBNETWORKS WITH GUMBEL-SOFTMAX”, 2022 IEEE International Conference on Image Processing (ICIP), Bordeaux, France, pp. 931-935, (IEEE) (2022)
  • H. Sahbi : “TOPOLOGICALLY-CONSISTENT MAGNITUDE PRUNING FOR VERY LIGHTWEIGHT GRAPH CONVOLUTIONAL NETWORKS”, 2022 IEEE International Conference on Image Processing (ICIP), Bordeaux, France, pp. 3495-3499, (IEEE) (2022)
  • S. Deschamps, H. Sahbi : “Reinforcement-based Display Selection for Frugal Learning”, International Conference on Pattern Recognition (ICPR), Montréal, Canada (2022)
  • H. Sahbi, S. Deschamps : “LEARNING VIRTUAL EXEMPLARS FOR LABEL-EFFICIENT SATELLITE IMAGE CHANGE DETECTION”, IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium, Kuala Lumpur, Malaysia, pp. 1197-1200, (IEEE) (2022)
  • S. Deschamps, H. Sahbi : “REINFORCEMENT-BASED FRUGAL LEARNING FOR INTERACTIVE SATELLITE IMAGE CHANGE DETECTION”, IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium, Kuala Lumpur, Malaysia, pp. 627-630, (IEEE) (2022)
  • M. Jiu, H. Sahbi : “Context-Aware Deep Kernel Networks for Image Annotation”, Neurocomputing, vol. 474, pp. 154-167, (Elsevier) (2022)
• 2021
  • H. Sahbi, S. Deschamps, A. Stoian : “Frugal Learning for Interactive Satellite Image Change Detection”, 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, Brussels, Belgium, pp. 2811-2814 (2021)
  • H. Sahbi : “Learning Laplacians in Chebyshev Graph Convolutional Networks”, 2021 IEEE/CVF International Conference on Computer Vision (ICCV), DLGC, Montreal (virtual), Canada, pp. 2064-2075 (2021)
  • H. Sahbi : “Lightweight Connectivity In Graph Convolutional Networks For Skeleton-Based Recognition”, 2021 IEEE International Conference on Image Processing (ICIP), Anchorage, AK (virtual), United States, pp. 2329-2333 (2021)
  • R. Dupont, H. Sahbi, G. Michel : “Weight Reparametrization for Budget-Aware Network Pruning”, 2021 IEEE International Conference on Image Processing (ICIP), Anchorage, AK (virtual), United States, pp. 789-793 (2021)
  • M. Jiu, H. Sahbi : “DHCN: Deep Hierarchical Context Networks For Image Annotation”, ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Toronto, ON, Canada (2021)
  • H. Sahbi, H. Zhan : “FFNB: Forgetting-Free Neural Blocks for Deep Continual Learning”, The British Machine Vision Conference (BMVC), Virtual, United Kingdom (2021)
• 2020
  • H. Sahbi : “Kernel-based Graph Convolutional Networks”, IAPR ICPR, Milan / Virtuel, Italy (2020)
• 2019
  • H. Sahbi : “Deep Total Variation Support Vector Networks”, 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW), Seoul, Korea, Republic of, pp. 3028-3038, (IEEE) (2019)
  • Th. Garbay, O. Chuquimia, A. Pinna, H. Sahbi, X. Dray, B. Granado : “Distilling the knowledge in CNN for WCE screening tool”, 2019 Conference on Design and Architectures for Signal and Image Processing (DASIP), Montreal, Canada, pp. 19-22, (IEEE) (2019)
  • Ah. Mazari, H. Sahbi : “MLGCN: Multi-Laplacian Graph Convolutional Networks for Human Action Recognition”, The British Machine Vision Conference (BMVC), Cardiff, United Kingdom (2019)
  • A. Dutta, H. Sahbi : “Stochastic Graphlet Embedding”, IEEE Transactions on Neural Networks and Learning Systems, vol. 30 (8), pp. 2369-2382, (IEEE) (2019)
  • O. Chuquimia, Th. Garbay, W. Xu, A. Pinna, X. Dray, H. Sahbi, B. Granado : “Study to integrate CNN inside a WCE to realize a screening tool”, Journées d'Etude sur la TéléSanté, Paris, France (2019)
  • Ah. Mazari, H. Sahbi : “Deep Temporal Pyramid Design For Action Recognition”, IEEE International Conference on Acoustic, Speech and Signal Processing, ICASSP, Brighton, United Kingdom, pp. 2077-2081, (IEEE) (2019)
  • H. Sahbi : “Learning CCA Representations for Misaligned Data”, European Conference on Computer Vision, ECCV - Workshop, vol. 11132, Munich, Germany, pp. 468-485, (Springer) (2019)
  • M. Jiu, H. Sahbi : “Deep representation design from deep kernel networks”, Pattern Recognition, vol. 88, pp. 447-457, (Elsevier) (2019)
• 2018
  • H. Sahbi : “Structured Scene Decoding with Finite State Machines”, IEEE International Conference on Image Processing, ICIP, Athens, Greece, pp. 485-489, (IEEE) (2018)
  • M. Jiu, H. Sahbi, L. Qi : “Deep Context Networks for Image Annotation”, IEEE International Conference on Pattern Recognition (ICPR), Beijing, China, pp. 2422-2427, (IEEE) (2018)
  • T. Postadjian, A. Le Bris, H. Sahbi, C. Mallet : “Domain Adaptation for Large Scale Classification of Very High Resolution Satellite Images with Deep Convolutional Neural Networks”, IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Valencia, Spain, pp. 3623-3626, (IEEE) (2018)
  • Q. Oliveau, H. Sahbi : “Semi-Supervised Deep Attribute Networks for Fine-Grained Ship Category Recognition”, IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain, pp. 6871-6874, (IEEE) (2018)
  • T. Postadjian, A. Le Bris, C. Mallet, H. Sahbi : “Superpixel Partitioning of Very High Resolution Satellite Images for Large-Scale Classification Perspectives with Deep Convolutional Neural Networks”, IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Valencia, Spain, pp. 1328-1331, (IEEE) (2018)
  • Q. Oliveau, H. Sahbi : “From Transductive to Inductive Semi-Supervised Attributes for Ship Category Recognition”, IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Valencia, Spain, pp. 4827-4830, (IEEE) (2018)
  • T. Postadjian, A. Le Bris, H. Sahbi, C. Mallet : “Classification à très large échelle d’images satellite à très haute résolution spatiale par réseaux de neurones convolutifs”, Revue Française de Photogrammétrie et de Télédétection, vol. 217-218 (n° spécial), colloque CFPT 2018, pp. 73-86, (Société Française de Photogrammétrie et de Télédétection) (2018)
• 2017
  • T. Postadjian, A. Le Bris, H. Sahbi, C. Mallet : “Investigating the potential of deep neural networks for large-scale classification of very high resolution satellite images”, ISPRS International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. IV-1/W1, Hanovre, Germany, pp. 183-190, (Copernicus GmbH (Copernicus Publications)) (2017)
• 2011
  • H. Sahbi, X. Li, S. Tollari, H. Glotin, Ph. Mulhem, A. Fakeri‑Tabrizi, Zh. Zhao, P. Gallinari : “Apprentissage automatique pour l’annotation d’images”, chapitre de Sémantique et multimodalité en analyse de l'information, Traité RTA, série Recherche d'information et web, pp. 367-381, (Hermès-Lavoisier), (ISBN: 2-7462-3139-5) (2011)
• 2009
  • H. Glotin, A. Fakeri‑Tabrizi, Ph. Mulhem, M. Ferecatu, Zh. Zhao, S. Tollari, G. Quenot, H. Sahbi, E. Dumont, P. Gallinari : “Comparison of Various AVEIR Visual Concept Detectors with an Index of Carefulness”, Image Cross Language Evaluation Forum (ImageClef 2009), vol. 1175, CEUR Workshop Proceedings, Corfu, Greece, (ceur-ws.org) (2009)
  • S. Tollari, Ph. Mulhem, M. Ferecatu, H. Glotin, M. Detyniecki, P. Gallinari, H. Sahbi, Zh. Zhao : “A Comparative Study of Diversity Methods for Hybrid Text and Image Retrieval Approaches”, Evaluating Systems for Multilingual and Multimodal Information Access, vol. 5706, Lecture Notes in Computer Science, Aarhus, Denmark, pp. 585-592, (Springer) (2009)
• 2008
  • S. Tollari, M. Detyniecki, M. Ferecatu, H. Glotin, Ph. Mulhem, M.‑R. Amini, A. Fakeri‑Tabrizi, P. Gallinari, H. Sahbi, Zh. Zhao : “Consortium AVEIR at ImageCLEFphoto 2008: on the fusion of runs”, Working Notes for the CLEF 2008 workshop, vol. 1174, CEUR-WS, Aarhus, Denmark, (CEUR) (2008)