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This paper presents a novel visual-language model called DFER-CLIP, which isbased on the CLIP model and designed for in-the-wild Dynamic Facial ExpressionRecognition (DFER). Specifically, the proposed DFER-CLIP consists of a visualpart and a textual part. For the visual part, based on the CLIP image encoder,a temporal model consisting of several Transformer encoders is introduced forextracting temporal facial expression features, and the final feature embeddingis obtained as a learnable "class" token. For the textual part, we use asinputs textual descriptions of the facial behaviour that is related to theclasses (facial expressions) that we are interested in recognising -- thosedescriptions are generated using large language models, like ChatGPT. This, incontrast to works that use only the class names and more accurately capturesthe relationship between them. Alongside the textual description, we introducea learnable token which helps the model learn relevant context information foreach expression during training. Extensive experiments demonstrate theeffectiveness of the proposed method and show that our DFER-CLIP also achievesstate-of-the-art results compared with the current supervised DFER methods onthe DFEW, FERV39k, and MAFW benchmarks. Code is publicly available athttps://github.com/zengqunzhao/DFER-CLIP.

Prompting Visual-Language Models for Dynamic Facial Expression Recognition