Signal and image processing

TSI - Signal and image processing

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The COMmunity of ExperTs TSI is interested in the following topics :

Analysis and processing of optical and radar satellite images : Compression, restoration, segmentation, classification…
Extraction and analysis of scientific measurements : Altimetry, sounding…
Architectures and supports for Embedded processes : Optimization of complexity, architecture…
Transmission and localization : Channel coding, filtering, estimation…
Propagation - in connection with the Community of Experts ECM (Electromagnetism and Microwave Circuit)

Guillaume EYNARD-BONTEMPS
Ingénieur Calcul Scientifique
Xavier BOULANGER
Ingénieur
Emilie Robert
community moderator

The bureau

Space Agencies

R&D publique & IRT

Laurent CASTANET

ONERA

Propagation radioélectrique, radiocommunications

Industrial Prime Contractor

Public Research (laboratories, Instituts, Higher School, ...)

Tous les événements futurs

9 July 2026
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DATAVIZ : (Outils de) Visualisation avec ou sans Python par l'Atelier Numérique de l'OMP

TSI - Signal and image processing

L'Atelier Numérique de l'OMP propose le 9 juillet 2026 à partir de 08h45 :Une journée DATAVIZ: Principes, bonnes pratiques et outils de visualisation avec ou sans programmation en salle Coriolis de l'OMP.Le programme en cours de finalisation est en ligne à cette adresse : https://sourcesup.renater.fr/wiki/atelieromp/visu_jt_09juillet2026Le matin : 1/2 journée thématique avec des présentations. 60 places maximum.L'après-midi : un atelier pour améliorer ses visualisations. 24 places maximum. Merci de vous inscrire par l'intermédiaire de ...

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24 March 2026
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MILO Seminar : Adaptation of foundation model: from Transformer adapters to VLM test-time prompt tuning

TSI - Signal and image processing

Adaptation of foundation model: from Transformer adapters to VLM test-time prompt tuning With the advent of foundation models, model development has shifted from tailoring task-specific models to training large models capable of addressing a wide range of tasks. One way of using foundation models is to directly use them with the pretrained weights from a very optimized training. This has the advantage of not using any training resources, and of exploiting ...

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