CLIPER: A Unified Vision-Language Framework for In-the-Wild Facial Expression Recognition

Understand

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.