EmoCLIP: A Vision-Language Method for Zero-Shot Video Facial Expression Recognition

Understand

Facial Expression Recognition (FER) is a crucial task in affective computing, but its conventional focus on the seven basic emotions limits its applicability to the complex and expanding emotional spectrum.

• To address the issue of new and unseen emotions present in dynamic in-the-wild FER, we propose a novel vision-language model that utilises sample-level text descriptions (i.e.
• captions of the context, expressions or emotional cues) as natural language supervision, aiming to enhance the learning of rich latent representations, for zero-shot classification.
• To test this, we evaluate using zero-shot classification of the model trained on sample-level descriptions on four popular dynamic FER datasets.