6 citations · 8 across the 5 of their papers we have counts for
5 papers
Fighting over-fitting with quantization for learning deep neural networks on noisy labels
Gauthier Tallec, Edouard Yvinec, Arnaud Dapogny +1
The rising performance of deep neural networks is often empirically attributed to an increase in the available computational power, which allows complex models to be trained upon l…
Fighting noise and imbalance in Action Unit detection problems
Gauthier Tallec, Arnaud Dapogny, Kevin Bailly
Action Unit (AU) detection aims at automatically caracterizing facial expressions with the muscular activations they involve. Its main interest is to provide a low-level face repre…
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…
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…