Search Results for author: Adeline Paiement

Found 10 papers, 1 papers with code

Domain-informed graph neural networks: a quantum chemistry case study

no code implementations25 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).

Detecting Humans in RGB-D Data with CNNs

1 code implementation17 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.

A DNN Framework for Learning Lagrangian Drift With Uncertainty

no code implementations12 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.

Adaptive Neighbourhoods for the Discovery of Adversarial Examples

no code implementations22 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.

Learnable Gabor modulated complex-valued networks for orientation robustness

no code implementations23 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.

Data Augmentation Translation

VI-Net: View-Invariant Quality of Human Movement Assessment

no code implementations11 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.

3D Action Recognition Action Analysis +1

VIMPNN: A physics informed neural network for estimating potential energies of out-of-equilibrium systems

no code implementations25 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).

Drug Discovery

Calorie Counter: RGB-Depth Visual Estimation of Energy Expenditure at Home

no code implementations27 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.

Multiple Human Tracking in RGB-D Data: A Survey

no code implementations14 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.

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