71 citations · 97 across the 31 of their papers we have counts for
6 papers · 2 filters
Multi-Task Transformer with uncertainty modelling for Face Based Affective Computing
Gauthier Tallec, Jules Bonnard, Arnaud Dapogny +1
Face based affective computing consists in detecting emotions from face images. It is useful to unlock better automatic comprehension of human behaviours and could pave the way tow…
SInGE: Sparsity via Integrated Gradients Estimation of Neuron Relevance
Edouard Yvinec, Arnaud Dapogny, Matthieu Cord +1
The leap in performance in state-of-the-art computer vision methods is attributed to the development of deep neural networks. However it often comes at a computational price which…
SPIQ: Data-Free Per-Channel Static Input Quantization
Edouard Yvinec, Arnaud Dapogny, Matthieu Cord +1
Computationally expensive neural networks are ubiquitous in computer vision and solutions for efficient inference have drawn a growing attention in the machine learning community.…
Privileged Attribution Constrained Deep Networks for Facial Expression Recognition
Jules Bonnard, Arnaud Dapogny, Ferdinand Dhombres +1
Facial Expression Recognition (FER) is crucial in many research domains because it enables machines to better understand human behaviours. FER methods face the problems of relative…
Multi-label Transformer for Action Unit Detection
Gauthier Tallec, Edouard Yvinec, Arnaud Dapogny +1
Action Unit (AU) Detection is the branch of affective computing that aims at recognizing unitary facial muscular movements. It is key to unlock unbiased computational face represen…
Multi-Order Networks for Action Unit Detection
Gauthier Tallec, Arnaud Dapogny, Kevin Bailly
Action Units (AU) are muscular activations used to describe facial expressions. Therefore accurate AU recognition unlocks unbiaised face representation which can improve face-based…