  Temporal context is key to the recognition of expressions of emotion.
Existing methods, that rely on recurrent or self-attention models to enforce
temporal consistency, work on the feature level, ignoring the task-specific
temporal dependencies, and fail to model context uncertainty. To alleviate
these issues, we build upon the framework of Neural Processes to propose a
method for apparent emotion recognition with three key novel components: (a)
probabilistic contextual representation with a global latent variable model;
(b) temporal context modelling using task-specific predictions in addition to
features; and (c) smart temporal context selection. We validate our approach on
four databases, two for Valence and Arousal estimation (SEWA and AffWild2), and
two for Action Unit intensity estimation (DISFA and BP4D). Results show a
consistent improvement over a series of strong baselines as well as over
state-of-the-art methods.
