The valid and reliable evaluation of affect and affective interaction is key for the advancement of affective computing (AC). Recent breakthroughs in deep (machine) learning and generative AI have boosted the efficiency and generality of affect models by discovering novel representations of users and their context acting on high resolutions of multimodal signals. Such representations, however, are data hungry and in need of large datasets that AC is not able to offer. Moreover, as affect models gradually become larger and more complex, their expressivity, explainability, and transparency becomes increasingly opaque.
This workshop puts an emphasis on state of the art methods in machine learning and their suitability for advancing the reliability, validity, and generality of affective models. We will be investigating entirely new methods, untried in AC, but also methods that can be coupled with traditional and dominant practices in affective modeling. In particular, we encourage submissions that offer visions of particular algorithmic advancements for affect modeling and proof-of-concept case studies showcasing the potential of new sophisticated machine learning methods.
Topics include but are not limited to:
The "What's Next in Affect Modeling?" will be a half day workshop with oral presentations splitted in two parts with a half hour break in between.
The "What’s Next in Affect Modeling?"" workshop is supported by European Union's H2020 research and innovation programme via the TAMED project (GA No. 101003397) and sustAGE project (GA No. 826506).