Knowledge-Spreader: Learning Semi-Supervised Facial Action Dynamics by Consistifying Knowledge Granularity

Recent studies on dynamic facial action unit (AU) detection have extensively relied on dense annotations. However, manual annotations are difficult, time-consuming, and costly. The canonical semi-supervised learning (SSL) methods ignore the consistency, extensibility, and adaptability of structural knowledge across spatial-temporal domains. Furthermore, the reliance on offline design and excessive parameters hinder the efficiency of the learning process. To remedy these issues, we propose a lightweight and on-line semi-supervised framework, a so-called Knowledge-Spreader (KS), to learn AU dynamics with sparse annotations. By formulating SSL as a Progressive Knowledge Distillation (PKD) problem, we aim to infer cross-domain information, specifically from spatial to temporal domains, by consistifying knowledge granularity within Teacher-Students Network. Specifically, KS employs sparsely annotated key-frames to learn AU dependencies as the privileged knowledge. Then, the model spreads the learned knowledge to their unlabeled neighbours by jointly applying knowledge distillation and pseudo-labeling, and completes the temporal information as the expanded knowledge. We term the progressive knowledge distillation as "Knowledge Spreading", which allows our model to learn spatial-temporal knowledge from video clips with only one label allocated. Extensive experiments demonstrate that KS achieves competitive performance as compared to the state of the arts under the circumstances of using only 2% labels on BP4D and 5% labels on DISFA. In addition, we have tested it on our newly developed large-scale comprehensive emotion database BP4D++, which contains considerable samples across well-synchronized and aligned sensor modalities for alleviating the scarcity issue of annotations and identities.