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2026","permalink":"https://projet.liris.cnrs.fr/imagine/publications/hal-05412877/","section":"Publications","summary":"","title":"FNOPT: Resolution-Agnostic, Self-Supervised Cloth Simulation using Meta-Optimization with Fourier Neural Operators"},{"content":"","date":null,"permalink":"https://projet.liris.cnrs.fr/imagine/authors/franck-lebourgeois/","section":"Authors","summary":"","title":"Franck Lebourgeois"},{"content":"","date":null,"permalink":"https://projet.liris.cnrs.fr/imagine/authors/frederik-heller/","section":"Authors","summary":"","title":"Frederik Heller"},{"content":"","date":"1 January 2026","permalink":"https://projet.liris.cnrs.fr/imagine/publications/hal-05698652/","section":"Publications","summary":"","title":"Generation of mass-charge pseudo-images"},{"content":"","date":null,"permalink":"https://projet.liris.cnrs.fr/imagine/authors/gislain-lipeme-kouyi/","section":"Authors","summary":"","title":"Gislain Lipeme Kouyi"},{"content":"","date":null,"permalink":"https://projet.liris.cnrs.fr/imagine/authors/godefroy-brisebarre/","section":"Authors","summary":"","title":"Godefroy Brisebarre"},{"content":"","date":null,"permalink":"https://projet.liris.cnrs.fr/imagine/authors/guillaume-duret/","section":"Authors","summary":"","title":"Guillaume Duret"},{"content":"","date":null,"permalink":"https://projet.liris.cnrs.fr/imagine/authors/gwendal-bernardi/","section":"Authors","summary":"","title":"Gwendal Bernardi"},{"content":"","date":"1 January 2026","permalink":"https://projet.liris.cnrs.fr/imagine/publications/hal-05656804/","section":"Publications","summary":"","title":"HFVideoSwin: High-Frequency Spatio-Temporal Features for More Generalizable Deepfake Video Detection"},{"content":"","date":null,"permalink":"https://projet.liris.cnrs.fr/imagine/authors/hui-huang/","section":"Authors","summary":"","title":"Hui Huang"},{"content":"","date":null,"permalink":"https://projet.liris.cnrs.fr/imagine/authors/igor-krawczuk/","section":"Authors","summary":"","title":"Igor Krawczuk"},{"content":" The IMAGINE team at LIRIS specializes in computer vision, learning and pattern recognition. \u0026#8249; \u0026#8250; About the team #The Imagine team at LIRIS specializes in computer vision, learning and pattern recognition. It brings together 27 staff members (one CNRS researcher, 15 Associate Professors, 10 Professors and one CNRS senior researcher) from the CNRS, École Centrale de Lyon, INSA Lyon, Université Claude Bernard Lyon 1 and Université Lumière Lyon 2. In 2026, it also counts among its members 3 teaching assistants, 38 doctoral students and 6 post-doctoral fellows.\nThe various research activities carried out in the Imagine team share the same general objectives aimed at understanding multi-source and multi-sensor images, thus integrating a very wide variety of content around images of human people, objects and scenes in 2D and 3D (natural or urban scenes, aerial and satellite images, faces, etc.), image sequences and video streams, as well as digitized documents (maps, written and printed texts, scores and symbols, etc.). The notion of visual object, in the broad sense, thus constitutes a common denominator of our research.\nThe activities of the Imagine team are divided into different themes related to the implementation of indexing, modeling, classification and content recognition methods (objects, actions, concepts), with particular attention paid to the development of machine learning methods for computer vision.\nThe research carried out in the Imagine team aims to build bridges to cross the semantic gap between, on the one hand, the low-level information present in the raw data (sampled data from the signal), the possible multi-source data from other modalities or sensors (particularly in the case of embedded applications) and the higher semantic level information based on the modeling, classification and identification of content.\nExplore our research topics to see what the team has been working on over the last ten years.\n","date":null,"permalink":"https://projet.liris.cnrs.fr/imagine/","section":"Imagine Team","summary":"","title":"Imagine Team"},{"content":"","date":null,"permalink":"https://projet.liris.cnrs.fr/imagine/authors/iuliia-tkachenko/","section":"Authors","summary":"","title":"Iuliia Tkachenko"},{"content":"","date":null,"permalink":"https://projet.liris.cnrs.fr/imagine/authors/jan-peters/","section":"Authors","summary":"","title":"Jan Peters"},{"content":"","date":null,"permalink":"https://projet.liris.cnrs.fr/imagine/authors/jannick-duchet-rumeau/","section":"Authors","summary":"","title":"Jannick Duchet-Rumeau"},{"content":"","date":null,"permalink":"https://projet.liris.cnrs.fr/imagine/authors/jean-baptiste-schiratti/","section":"Authors","summary":"","title":"Jean-Baptiste Schiratti"},{"content":"","date":null,"permalink":"https://projet.liris.cnrs.fr/imagine/authors/jean-fran%C3%A7ois-g%C3%A9rard/","section":"Authors","summary":"","title":"Jean-François 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Velcin"},{"content":"","date":null,"permalink":"https://projet.liris.cnrs.fr/imagine/authors/kashif-ali-javaid/","section":"Authors","summary":"","title":"Kashif Ali Javaid"},{"content":"","date":null,"permalink":"https://projet.liris.cnrs.fr/imagine/authors/kevin-kernn/","section":"Authors","summary":"","title":"Kevin Kernn"},{"content":"","date":null,"permalink":"https://projet.liris.cnrs.fr/imagine/authors/laure-tougne-rodet/","section":"Authors","summary":"","title":"Laure Tougne Rodet"},{"content":"","date":"1 January 2026","permalink":"https://projet.liris.cnrs.fr/imagine/publications/hal-05584841/","section":"Publications","summary":"","title":"Learning What Matters for Effective Historical Manuscripts Recognition"},{"content":"","date":null,"permalink":"https://projet.liris.cnrs.fr/imagine/authors/l%C3%A9ny-grassot/","section":"Authors","summary":"","title":"Lény Grassot"},{"content":"","date":null,"permalink":"https://projet.liris.cnrs.fr/imagine/authors/leo-schneider/","section":"Authors","summary":"","title":"Leo Schneider"},{"content":"","date":null,"permalink":"https://projet.liris.cnrs.fr/imagine/authors/liming-chen/","section":"Authors","summary":"","title":"Liming Chen"},{"content":"","date":null,"permalink":"https://projet.liris.cnrs.fr/imagine/authors/lionel-guillou/","section":"Authors","summary":"","title":"Lionel Guillou"},{"content":"","date":null,"permalink":"https://projet.liris.cnrs.fr/imagine/authors/magdalena-lederbauer/","section":"Authors","summary":"","title":"Magdalena Lederbauer"},{"content":"","date":null,"permalink":"https://projet.liris.cnrs.fr/imagine/authors/mansour-zoubeirou-a-mayaki/","section":"Authors","summary":"","title":"Mansour Zoubeirou a Mayaki"},{"content":"","date":"1 January 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Atamna"},{"content":"","date":null,"permalink":"https://projet.liris.cnrs.fr/imagine/authors/mihaela-scuturici/","section":"Authors","summary":"","title":"Mihaela Scuturici"},{"content":"","date":null,"permalink":"https://projet.liris.cnrs.fr/imagine/authors/mohammed-el-amine-sehaba/","section":"Authors","summary":"","title":"Mohammed El Amine Sehaba"},{"content":"","date":null,"permalink":"https://projet.liris.cnrs.fr/imagine/authors/mohsen-ardabilian/","section":"Authors","summary":"","title":"Mohsen Ardabilian"},{"content":"","date":"1 January 2026","permalink":"https://projet.liris.cnrs.fr/imagine/publications/hal-05520148/","section":"Publications","summary":"","title":"Multi-Score Reinforcement Learning for High-Tg Polyimide Design"},{"content":"","date":null,"permalink":"https://projet.liris.cnrs.fr/imagine/authors/olivia-p%C3%A9rol/","section":"Authors","summary":"","title":"Olivia Pérol"},{"content":"","date":"1 January 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Efficient Deformation Modeling"},{"content":"","date":null,"permalink":"https://projet.liris.cnrs.fr/imagine/authors/pierre-lemaire/","section":"Authors","summary":"","title":"Pierre Lemaire"},{"content":"","date":null,"permalink":"https://projet.liris.cnrs.fr/imagine/publications/","section":"Publications","summary":"","title":"Publications"},{"content":"","date":"1 January 2026","permalink":"https://projet.liris.cnrs.fr/imagine/publications/hal-05488652/","section":"Publications","summary":"","title":"Real-Time Simulation of Deformable Tactile Sensors and Objects in Robotic Grasping using Graph Neural Networks with Inductive Biases"},{"content":"","date":null,"permalink":"https://projet.liris.cnrs.fr/imagine/authors/r%C3%A9mi-b%C3%A9ranger/","section":"Authors","summary":"","title":"Rémi Béranger"},{"content":"","date":null,"permalink":"https://projet.liris.cnrs.fr/imagine/authors/r%C3%A9mi-cuingnet/","section":"Authors","summary":"","title":"Rémi 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