When recognizing emotions, subtle nuances in displays of emotion generate
ambiguity or uncertainty in emotion perception. Emotion uncertainty has been
previously interpreted as inter-rater disagreement among multiple annotators.
In this paper, we consider a more common and challenging scenario: modeling
emotion uncertainty when only single emotion labels are available. From a
Bayesian perspective, we propose to use deep ensembles to capture uncertainty
for multiple emotion descriptors, i.e., action units, discrete expression
labels and continuous descriptors. We further apply iterative
self-distillation. Iterative distillation over multiple generations
significantly improves performance in both emotion recognition and uncertainty
estimation. Our method generates single student models that provide accurate
estimates of uncertainty for in-domain samples and a student ensemble that can
detect out-of-domain samples. Our experiments on emotion recognition and
uncertainty estimation using the Aff-wild2 dataset demonstrate that our
algorithm gives more reliable uncertainty estimates than both Temperature
Scaling and Monte Carol Dropout.

本研究通过采用贝叶斯观点，利用深度集成模型来捕捉多种情绪描述符，包括行动单位、离散表情标签和连续描述符，进而应用迭代自蒸馏技术，提出一种用于情绪识别的方法，并针对仅有单一情绪标签可用的情况下，实现情绪不确定性的建模和估计。实验结果表明，与 Temperature Scaling 和 Monte Carol Dropout 相比，我们的算法具有更可靠的不确定性估计。