Continual Learning for Affective Computing
This addresses the need for personalized affective computing models in real-world applications, but it appears incremental as it applies an existing CL approach to a new domain.
The paper tackles the problem of personalizing affect perception models to individual differences in expression by proposing Continual Learning (CL) as a paradigm, aiming to improve adaptation beyond current high-performing benchmarks.
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.