论文标题
BERT，随机森林和SVM方法的变体，用于多模式情感标志子挑战
Variants of BERT, Random Forests and SVM approach for Multimodal Emotion-Target Sub-challenge
论文作者
论文摘要
近年来，情绪识别已成为计算机视觉中的一个主要问题，这使研究人员竭尽全力克服这项任务的困难。在情感计算领域，情绪识别具有广泛的应用，例如医疗保健，机器人技术，人类计算机的互动。由于其对其他任务的实际重要性，已经针对不同的问题和各种数据源研究了许多技术和方法。然而，从他们那里获得好处的视听和语言方式的全面融合仍然是一个问题。在本文中，我们介绍并讨论了我们的穆斯特群岛次级挑战的分类方法以及数据和结果。对于主题分类，我们整合了两种语言模型，这些模型是阿尔伯特和罗伯塔，以预测10类主题。此外，对于价值和唤醒的分类，SVM和随机森林与特征选择一起使用以增强性能。
Emotion recognition has become a major problem in computer vision in recent years that made a lot of effort by researchers to overcome the difficulties in this task. In the field of affective computing, emotion recognition has a wide range of applications, such as healthcare, robotics, human-computer interaction. Due to its practical importance for other tasks, many techniques and approaches have been investigated for different problems and various data sources. Nevertheless, comprehensive fusion of the audio-visual and language modalities to get the benefits from them is still a problem to solve. In this paper, we present and discuss our classification methodology for MuSe-Topic Sub-challenge, as well as the data and results. For the topic classification, we ensemble two language models which are ALBERT and RoBERTa to predict 10 classes of topics. Moreover, for the classification of valence and arousal, SVM and Random forests are employed in conjunction with feature selection to enhance the performance.
