用面部动作单元提升复杂情绪持续学习效果，模型轻量高效
High Semantic Features for the Continual Learning of Complex Emotions: a Lightweight Solution
- 用面部动作单元作为稳定语义特征，避免旧任务遗忘
- 在CFEE数据集上增量学习复杂情绪准确率达75%
- 模型轻量，内存占用小，适合资源受限场景
增量学习因新任务学习导致旧任务灾难性遗忘而复杂。本文聚焦复杂情绪识别，先学习基础情绪，再像人类一样逐步学习复合情绪。研究发现，描述面部肌肉运动的面部动作单元（Action Units）具有非瞬时性与高语义性，优于浅层和深层卷积神经网络提取的特征。利用该特性，本方法在增量学习复杂情绪时于CFEE数据集上达到75%的准确率，且性能优于现有先进方法。此外，模型设计轻量化，内存占用小。
原文摘要 · Abstract (English)
Incremental learning is a complex process due to potential catastrophic forgetting of old tasks when learning new ones. This is mainly due to transient features that do not fit from task to task. In this paper, we focus on complex emotion recognition. First, we learn basic emotions and then, incrementally, like humans, complex emotions. We show that Action Units, describing facial muscle movements, are non-transient, highly semantical features that outperform those extracted by both shallow and deep convolutional neural networks. Thanks to this ability, our approach achieves interesting results when learning incrementally complex, compound emotions with an accuracy of 0.75 on the CFEE dataset and can be favorably compared to state-of-the-art results. Moreover, it results in a lightweight model with a small memory footprint.
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