In recent years, deep learning has achieved innovative advancements in
various fields, including the analysis of human emotions and behaviors.
Initiatives such as the Affective Behavior Analysis in-the-wild (ABAW)
competition have been particularly instrumental in driving research in this
area by providing diverse and challenging datasets that enable precise
evaluation of complex emotional states. This study leverages the Vision
Transformer (ViT) and Transformer models to focus on the estimation of
Valence-Arousal (VA), which signifies the positivity and intensity of emotions,
recognition of various facial expressions, and detection of Action Units (AU)
representing fundamental muscle movements. This approach transcends traditional
Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) based
methods, proposing a new Transformer-based framework that maximizes the
understanding of temporal and spatial features. The core contributions of this
research include the introduction of a learning technique through random frame
masking and the application of Focal loss adapted for imbalanced data,
enhancing the accuracy and applicability of emotion and behavior analysis in
real-world settings. This approach is expected to contribute to the advancement
of emotional computing and deep learning methodologies.

通过使用 Vision Transformer 和 Transformer 模型，该研究关注情感的 Valence-Arousal 估计、各种面部表情的识别和基础肌肉运动的动作元的检测，提出了一种基于 Transformer 的新框架来最大化对时间和空间特征的理解，并引入了基于随机帧遮罩的学习技术以及针对不平衡数据的 Focal 损失的应用，增强了情绪和行为分析在实际场景中的准确性和适用性，预计对情感计算和深度学习方法的发展有所助益。

使用掩码学习的 Transformer 情感识别

Emotion Recognition Using Transformers with Masked Learning

The analysis of emotions expressed in text has numerous applications. In
contrast to categorical analysis, focused on classifying emotions according to
a pre-defined set of common classes, dimensional approaches can offer a more
nuanced way to distinguish between different emotions. Still, dimensional
methods have been less studied in the literature. Considering a valence-arousal
dimensional space, this work assesses the use of pre-trained Transformers to
predict these two dimensions on a continuous scale, with input texts from
multiple languages and domains. We specifically combined multiple annotated
datasets from previous studies, corresponding to either emotional lexica or
short text documents, and evaluated models of multiple sizes and trained under
different settings. Our results show that model size can have a significant
impact on the quality of predictions, and that by fine-tuning a large model we
can confidently predict valence and arousal in multiple languages. We make
available the code, models, and supporting data.

本研究利用预训练转换器对多语言和多领域的输入文本进行情感分析，使用基于趋势 - 唤醒维度的情感分析方法，比传统方法更细致地区分不同的情感，并发现模型大小对预测质量有显著影响，可自信地预测不同语言的趋势性和唤醒性。