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X-WR-CALNAME:GREYC UMR CNRS 6072 - Groupe de Recherche en Informatique, Image, et Instrumentation de Caen
X-ORIGINAL-URL:https://www.greyc.fr
X-WR-CALDESC:évènements pour GREYC UMR CNRS 6072 - Groupe de Recherche en Informatique, Image, et Instrumentation de Caen
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TZID:Europe/Paris
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DTSTART:20240331T010000
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DTSTART;TZID=Europe/Paris:20240206T100000
DTEND;TZID=Europe/Paris:20240206T110000
DTSTAMP:20260628T181340
CREATED:20240206T085003Z
LAST-MODIFIED:20240206T085003Z
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SUMMARY:Séminaire Algorithmique : « A propos du calcul de la D-base des systèmes de fermeture finis »\, Simon Vilmin (LIS\, Univ. Marseille)
DESCRIPTION:A closure system over a (finite) set X is a collection of subsets of X\, called closed sets\, being closed under intersection and containing X. These systems appear in disguise in numerous fields of mathematics and computer science by means of implicit representations. There are two common representations of a closure system: \n\nimplicational bases (IBs)\, that is sets of rules A –> b meaning « a set containing the set A must contain the element b« ;\nthe family of (meet-)irreducible closed sets\, being the closed sets from which one can rebuild the whole closure system.\n\nIn this talk\, I will first give a general overview of closure systems and their representations. Then\, I will present the results of a collaboration with Kira Adaricheva and Lhouari Nourine regarding the D-base\, particular IB. More precisely\, I will describe algorithms to retrieve (i.e. enumerate) the D-base either from irreducible closed sets or from an arbitrary IB.
URL:https://www.greyc.fr/event/seminaire-algorithmique-a-propos-du-calcul-de-la-d-base-des-systemes-de-fermeture-finis-simon-vilmin-lis-univ-marseille/
LOCATION:Sciences 3- S3 351
CATEGORIES:Amacc,Codag,General,News,Séminaire Algo
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DTSTART;TZID=Europe/Paris:20240207T140000
DTEND;TZID=Europe/Paris:20240207T150000
DTSTAMP:20260628T181340
CREATED:20240118T141704Z
LAST-MODIFIED:20240118T141704Z
UID:11400-1707314400-1707318000@www.greyc.fr
SUMMARY:Ayoub Karine - Beyond Pixels and 3D point Clouds: Exploring Deep Learning for Semantic Image Segmentation and 3D Point Cloud Quality Assessment
DESCRIPTION:In this seminar\, I will discuss some of our contributions centered around deep learning\, specifically addressing two computer vision tasks. The first task will be on semantic image segmentation\, a task aiming to assign a category label to each pixel in input images. While deep learning methods have demonstrated impressive performance in semantic segmentation\, their computationally intensive nature poses challenges for deployment on resource-limited devices. To overcome this weakness\, various methods\, including Knowledge Distillation (KD)\, have been proposed. KD involves training a compacted neural network called student under the supervision of a heavy one called teacher. The challenge is to find\, for a student network\, a better tradeoff between the segmentation quality and its efficiency. In this context\, I will present some of our proposed KD methods that leverage self-attention principles to capture contextual dependencies in both spatial and channel dimensions. For the second task\, it concerns 3D point cloud quality assessment (PCQA). Recently\, PC is considered one of the most widely used data for digital representation to model 3D realistic content in various applications. During the processing pipeline (acquisition\, representation\, compression\, and rendering)\, various degradation may appear in PCs which affect its perceived visual quality. Consequently\, it is essential to develop effective methods that accurately assess the quality of PCs and preserve the quality of the user experience. Unlike 2D images\, PCs are unordered and unstructured\, posing a challenge for direct application of convolutional neural networks using discrete convolutions. To circumvent this limitation\, I will discuss some of our proposed PCQA metrics categorized into projection-based and point-based methods. On the other hand\, I will present some applications of knowledge distillation and quality assessment in cybersecurity.
URL:https://www.greyc.fr/event/ayoub-karine-beyond-pixels-and-3d-point-clouds-exploring-deep-learning-for-semantic-image-segmentation-and-3d-point-cloud-quality-assessment/
LOCATION:Sciences 3- S3 351
CATEGORIES:Séminaire Cryptologie et sécurité
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