  Conventional approaches to facial expression recognition primarily focus on
the classification of six basic facial expressions. Nevertheless, real-world
situations present a wider range of complex compound expressions that consist
of combinations of these basics ones due to limited availability of
comprehensive training datasets. The 6th Workshop and Competition on Affective
Behavior Analysis in-the-wild (ABAW) offered unlabeled datasets containing
compound expressions. In this study, we propose a zero-shot approach for
recognizing compound expressions by leveraging a pretrained visual language
model integrated with some traditional CNN networks.
