Zobrazeno 1 - 10
of 23
pro vyhledávání: '"Rommel, Cédric"'
We introduce a novel approach for 3D whole-body pose estimation, addressing the challenge of scale- and deformability- variance across body parts brought by the challenge of extending the 17 major joints on the human body to fine-grained keypoints on
Externí odkaz:
http://arxiv.org/abs/2407.10220
Autor:
Letzelter, Victor, Perera, David, Rommel, Cédric, Fontaine, Mathieu, Essid, Slim, Richard, Gael, Pérez, Patrick
Winner-takes-all training is a simple learning paradigm, which handles ambiguous tasks by predicting a set of plausible hypotheses. Recently, a connection was established between Winner-takes-all training and centroidal Voronoi tessellations, showing
Externí odkaz:
http://arxiv.org/abs/2406.04706
Autor:
Rommel, Cédric, Letzelter, Victor, Samet, Nermin, Marlet, Renaud, Cord, Matthieu, Pérez, Patrick, Valle, Eduardo
We propose ManiPose, a manifold-constrained multi-hypothesis model for human-pose 2D-to-3D lifting. We provide theoretical and empirical evidence that, due to the depth ambiguity inherent to monocular 3D human pose estimation, traditional regression
Externí odkaz:
http://arxiv.org/abs/2312.06386
Autor:
Rommel, Cédric, Valle, Eduardo, Chen, Mickaël, Khalfaoui, Souhaiel, Marlet, Renaud, Cord, Matthieu, Pérez, Patrick
We present an innovative approach to 3D Human Pose Estimation (3D-HPE) by integrating cutting-edge diffusion models, which have revolutionized diverse fields, but are relatively unexplored in 3D-HPE. We show that diffusion models enhance the accuracy
Externí odkaz:
http://arxiv.org/abs/2309.01575
Autor:
Aristimunha, Bruno, de Camargo, Raphael Y., Pinaya, Walter H. Lopez, Chevallier, Sylvain, Gramfort, Alexandre, Rommel, Cedric
Electroencephalography (EEG) decoding is a challenging task due to the limited availability of labelled data. While transfer learning is a promising technique to address this challenge, it assumes that transferable data domains and task are known, wh
Externí odkaz:
http://arxiv.org/abs/2308.02408
Objective: The use of deep learning for electroencephalography (EEG) classification tasks has been rapidly growing in the last years, yet its application has been limited by the relatively small size of EEG datasets. Data augmentation, which consists
Externí odkaz:
http://arxiv.org/abs/2206.14483
Designing learning systems which are invariant to certain data transformations is critical in machine learning. Practitioners can typically enforce a desired invariance on the trained model through the choice of a network architecture, e.g. using con
Externí odkaz:
http://arxiv.org/abs/2202.02142
Data augmentation is a key element of deep learning pipelines, as it informs the network during training about transformations of the input data that keep the label unchanged. Manually finding adequate augmentation methods and parameters for a given
Externí odkaz:
http://arxiv.org/abs/2106.13695
Autor:
Rommel, Cédric
Cette thèse porte sur l'utilisation de données de vols pour l'optimisation de trajectoires de montée vis-à-vis de la consommation de carburant.Dans un premier temps nous nous sommes intéressé au problème d'identification de modèles de la dyna
Externí odkaz:
http://www.theses.fr/2018SACLX066/document
Autor:
Rommel, Cédric1 (AUTHOR), Frédéric Bonnans, J.1 (AUTHOR) frederic.bonnans@inria.fr, Gregorutti, Baptiste2 (AUTHOR), Martinon, Pierre3 (AUTHOR)
Publikováno v:
ESAIM: Probability & Statistics. 7/27/2021, Vol. 25, p1-30. 30p.
