no code implementations • 25 Aug 2022 • Jay Morgan, Adeline Paiement, Christian Klinke
We explore different strategies to integrate prior domain knowledge into the design of a deep neural network (DNN).
1 code implementation • 17 Jul 2022 • Kaiyang Zhou, Adeline Paiement, Majid Mirmehdi
We address the problem of people detection in RGB-D data where we leverage depth information to develop a region-of-interest (ROI) selection method that provides proposals to two color and depth CNNs.
1 code implementation • 12 Apr 2022 • Joseph Jenkins, Adeline Paiement, Yann Ourmières, Julien Le Sommer, Jacques Verron, Clément Ubelmann, Hervé Glotin
Reconstructions of Lagrangian drift, for example for objects lost at sea, are often uncertain due to unresolved physical phenomena within the data.
no code implementations • 22 Jan 2021 • Jay Morgan, Adeline Paiement, Arno Pauly, Monika Seisenberger
Deep Neural Networks (DNNs) have often supplied state-of-the-art results in pattern recognition tasks.
no code implementations • 1 Jan 2021 • Jay Morgan, Adeline Paiement, Nuria Lorenzo-Dus, Anina Kinzel, Matteo Di Cristofaro
Online grooming (OG) of children is a pervasive issue in an increasingly interconnected world.
no code implementations • 23 Nov 2020 • Felix Richards, Adeline Paiement, Xianghua Xie, Elisabeth Sola, Pierre-Alain Duc
Robustness to transformation is desirable in many computer vision tasks, given that input data often exhibits pose variance.
no code implementations • 11 Aug 2020 • Faegheh Sardari, Adeline Paiement, Sion Hannuna, Majid Mirmehdi
We propose a view-invariant method towards the assessment of the quality of human movements which does not rely on skeleton data.
no code implementations • 25 Sep 2019 • Jay Morgan, Adeline Paiement, Christian Klinke
Our method is extensively evaluated on a augmented version of the QM9 dataset that includes unstable molecules, as well as a new dataset of infinite- and finite-size crystals, and is compared with the Message Passing Neural Network (MPNN).
no code implementations • 27 Jul 2016 • Lili Tao, Tilo Burghardt, Majid Mirmehdi, Dima Damen, Ashley Cooper, Sion Hannuna, Massimo Camplani, Adeline Paiement, Ian Craddock
We present a new framework for vision-based estimation of calorific expenditure from RGB-D data - the first that is validated on physical gas exchange measurements and applied to daily living scenarios.
no code implementations • 14 Jun 2016 • Massimo Camplani, Adeline Paiement, Majid Mirmehdi, Dima Damen, Sion Hannuna, Tilo Burghardt, Lili Tao
Finally, we present a brief comparative evaluation of the performance of those works that have applied their methods to these datasets.