  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 toward improved human-machines interactions.
However it comes with the challenging task of designing a computational
representation of emotions. So far, emotions have been represented either
continuously in the 2D Valence/Arousal space or in a discrete manner with
Ekman's 7 basic emotions. Alternatively, Ekman's Facial Action Unit (AU) system
have also been used to caracterize emotions using a codebook of unitary
muscular activations. ABAW3 and ABAW4 Multi-Task Challenges are the first work
to provide a large scale database annotated with those three types of labels.
In this paper we present a transformer based multi-task method for jointly
learning to predict valence arousal, action units and basic emotions. From an
architectural standpoint our method uses a taskwise token approach to
efficiently model the similarities between the tasks. From a learning point of
view we use an uncertainty weighted loss for modelling the difference of
stochasticity between the three tasks annotations.
