Zobrazeno 1 - 10
of 51
pro vyhledávání: '"Patrick Perez"'
Publikováno v:
The Journal of Quality in Education, Vol 4, Iss 4 (2017)
Fondé principalement sur une enquête par entretiens auprês des acteurs chargés de l'élaboration de la maquette budgétaire et des indicateurs LOLF, ce travail décrit ces processus de quantification comme le produit de rapports de force entre le
Externí odkaz:
https://doaj.org/article/900e6edd2c704c358837aaedcd17b150
Autor:
Patrick Perez Ramos Silva
Publikováno v:
Biblioteca Digital de Teses e Dissertações da UFMGUniversidade Federal de Minas GeraisUFMG.
This work presents the evaluation results of two microplane models (the Explicit Microplane Model and the Microplane model with Relaxed Kinematic Constraint) to nonlinear analysis of concrete structures by finite element method. The main elements of
Externí odkaz:
http://hdl.handle.net/1843/FACO-5JVLJR
Autor:
Awet Haileslassie Gebrehiwot, Patrik Vacek, David Hurych, Karel Zimmermann, Patrick Perez, Tomas Svoboda
Automatic pseudo-labeling is a powerful tool to tap into large amounts of sequential unlabeled data. It is specially appealing in safety-critical applications of autonomous driving, where performance requirements are extreme, datasets are large, and
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8d494455b0953c13a9299f3cde39eb66
Publikováno v:
IEEE/CVF International Conference on Computer Vision (ICCV)
IEEE/CVF International Conference on Computer Vision (ICCV), Oct 2021, Montreal (virtuel), Canada
IEEE/CVF International Conference on Computer Vision (ICCV), Oct 2021, Montreal (virtuel), Canada
In this work, we address the task of unsupervised domain adaptation (UDA) for semantic segmentation in presence of multiple target domains: The objective is to train a single model that can handle all these domains at test time. Such a multi-target a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7c74012e3d8b74d797c3e6b64a3f174c
https://hal.archives-ouvertes.fr/hal-03482751
https://hal.archives-ouvertes.fr/hal-03482751
Publikováno v:
International Conference on Computer Vision (ICCV) 2021
International Conference on Computer Vision (ICCV) 2021, Oct 2021, Montreal (virtual), Canada
International Conference on Computer Vision (ICCV) 2021, Oct 2021, Montreal (virtual), Canada
Understanding the scene around the ego-vehicle is key to assisted and autonomous driving. Nowadays, this is mostly conducted using cameras and laser scanners, despite their reduced performances in adverse weather conditions. Automotive radars are low
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::66ce1957869bf4ddcddfb940ac3dd0c7
https://hal.telecom-paris.fr/hal-03324900/document
https://hal.telecom-paris.fr/hal-03324900/document
Deep Neural Networks (DNNs) are a critical component for self-driving vehicles. They achieve impressive performance by reaping information from high amounts of labeled data. Yet, the full complexity of the real world cannot be encapsulated in the tra
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::901c581cd11e36144a5e871660264e08
http://arxiv.org/abs/2103.13905
http://arxiv.org/abs/2103.13905
Publikováno v:
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022, 45 (2), pp.1533-1544. ⟨10.1109/TPAMI.2022.3159589⟩
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022, 45 (2), pp.1533-1544. ⟨10.1109/TPAMI.2022.3159589⟩
Domain adaptation is an important task to enable learning when labels are scarce. While most works focus only on the image modality, there are many important multi-modal datasets. In order to leverage multi-modality for domain adaptation, we propose
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7b3cb575c4af1ccfe4ca8743ce2940a2
http://arxiv.org/abs/2101.07253
http://arxiv.org/abs/2101.07253
With the rapid advances in generative adversarial networks (GANs), the visual quality of synthesised scenes keeps improving, including for complex urban scenes with applications to automated driving. We address in this work a continual scene generati
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::71eac08cd9b06d029228a20a78419292
Publikováno v:
Revue Française de Socio-Economie
Revue Française de Socio-Economie, Paris : La Découverte, 2020, Hors-séri (en lutte), pp.219. ⟨10.3917/rfse.spe2020.0219⟩
Revue Française de Socio-Economie, 2020, Hors-série (en lutte), pp.219-240. ⟨10.3917/rfse.spe2020.0219⟩
Revue Française de Socio-Economie, Paris : La Découverte, 2020, Hors-série (en lutte), pp.219. ⟨10.3917/rfse.spe2020.0219⟩
Revue Française de Socio-Economie, Paris : La Découverte, 2020, Hors-série (en lutte), pp.219-240. ⟨10.3917/rfse.spe2020.0219⟩
Revue Française de Socio-Economie, Paris : La Découverte, 2020, Hors-séri (en lutte), pp.219. ⟨10.3917/rfse.spe2020.0219⟩
Revue Française de Socio-Economie, 2020, Hors-série (en lutte), pp.219-240. ⟨10.3917/rfse.spe2020.0219⟩
Revue Française de Socio-Economie, Paris : La Découverte, 2020, Hors-série (en lutte), pp.219. ⟨10.3917/rfse.spe2020.0219⟩
Revue Française de Socio-Economie, Paris : La Découverte, 2020, Hors-série (en lutte), pp.219-240. ⟨10.3917/rfse.spe2020.0219⟩
International audience
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9eab89bf4e371c29c01a4a602c7d4f3f
https://halshs.archives-ouvertes.fr/halshs-03513030
https://halshs.archives-ouvertes.fr/halshs-03513030