论文标题
情感计算的持续学习
Continual Learning for Affective Computing
论文作者
论文摘要
现实世界的应用需要影响感知模型对表达中个体差异敏感。由于每个用户都不同并且表达方式不同，因此这些模型需要对每个人进行个性化，以充分捕获其表达式，从而对其情感状态进行建模。尽管基准上的高性能，但当前的方法在这种适应性方面缺乏。在这项工作中，我们建议使用持续学习（CL）作为情感计算作为发展个性化情感感知的范式。
Real-world application requires affect perception models to be sensitive to individual differences in expression. As each user is different and expresses differently, these models need to personalise towards each individual to adequately capture their expressions and thus, model their affective state. Despite high performance on benchmarks, current approaches fall short in such adaptation. In this work, we propose the use of Continual Learning (CL) for affective computing as a paradigm for developing personalised affect perception.
