  Facial expression recognition (FER) is an essential task for understanding
human behaviors. As one of the most informative behaviors of humans, facial
expressions are often compound and variable, which is manifested by the fact
that different people may express the same expression in very different ways.
However, most FER methods still use one-hot or soft labels as the supervision,
which lack sufficient semantic descriptions of facial expressions and are less
interpretable. Recently, contrastive vision-language pre-training (VLP) models
(e.g., CLIP) use text as supervision and have injected new vitality into
various computer vision tasks, benefiting from the rich semantics in text.
Therefore, in this work, we propose CLIPER, a unified framework for both static
and dynamic facial Expression Recognition based on CLIP. Besides, we introduce
multiple expression text descriptors (METD) to learn fine-grained expression
representations that make CLIPER more interpretable. We conduct extensive
experiments on several popular FER benchmarks and achieve state-of-the-art
performance, which demonstrates the effectiveness of CLIPER.
