CLIPER: A Unified Vision-Language Framework for In-the-Wild Facial Expression Recognition
This addresses the need for more interpretable and semantically rich facial expression recognition systems, though it appears to be an incremental improvement by adapting existing CLIP methodology to this specific domain.
The authors tackled the problem of facial expression recognition by proposing CLIPER, a unified vision-language framework based on CLIP that uses text descriptors for supervision instead of traditional one-hot labels, achieving state-of-the-art performance on multiple benchmarks.
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.