论文标题
改进是改变：通过学习情绪变化来改善情绪预测
To Improve Is to Change: Towards Improving Mood Prediction by Learning Changes in Emotion
论文作者
论文摘要
尽管术语情绪和情感是密切相关的，并且经常互换使用，但它们是根据其持续时间，强度和归因而区分的。迄今为止，几乎没有任何计算模型（a）检查了情绪识别，并且（b）在他们的分析中对情绪和情绪状态之间的相互作用进行了建模。在本文中，作为迈向情绪预测的第一步，我们提出了一个框架，该框架既利用了主导的情感（或情绪）标签，又是AFEW-VA数据库上的情感变化标签。评估单模式（仅使用情绪标签训练）和多模式（接受情绪和情绪变化标签的训练）的实验证实，将情绪变化信息纳入网络训练过程中可以显着提高情绪预测的表现，从而突出表现出对情感和情绪的重要性，以提高情绪表现的表现。
Although the terms mood and emotion are closely related and often used interchangeably, they are distinguished based on their duration, intensity and attribution. To date, hardly any computational models have (a) examined mood recognition, and (b) modelled the interplay between mood and emotional state in their analysis. In this paper, as a first step towards mood prediction, we propose a framework that utilises both dominant emotion (or mood) labels, and emotional change labels on the AFEW-VA database. Experiments evaluating unimodal (trained only using mood labels) and multimodal (trained with both mood and emotion change labels) convolutional neural networks confirm that incorporating emotional change information in the network training process can significantly improve the mood prediction performance, thus highlighting the importance of modelling emotion and mood simultaneously for improved performance in affective state recognition.
