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.

该研究提出了一种基于神经过程的方法，使用全局潜在变量模型的概率性语境表示与任务特定预测结合，智能地选择时间上下文，并在四个数据库上进行验证，在情感识别等方面取得了比强基线和现有方法更为一致的进步。