  Video-based facial affect analysis has recently attracted increasing
attention owing to its critical role in human-computer interaction. Previous
studies mainly focus on developing various deep learning architectures and
training them in a fully supervised manner. Although significant progress has
been achieved by these supervised methods, the longstanding lack of large-scale
high-quality labeled data severely hinders their further improvements.
Motivated by the recent success of self-supervised learning in computer vision,
this paper introduces a self-supervised approach, termed Self-supervised Video
Facial Affect Perceiver (SVFAP), to address the dilemma faced by supervised
methods. Specifically, SVFAP leverages masked facial video autoencoding to
perform self-supervised pre-training on massive unlabeled facial videos.
Considering that large spatiotemporal redundancy exists in facial videos, we
propose a novel temporal pyramid and spatial bottleneck Transformer as the
encoder of SVFAP, which not only enjoys low computational cost but also
achieves excellent performance. To verify the effectiveness of our method, we
conduct experiments on nine datasets spanning three downstream tasks, including
dynamic facial expression recognition, dimensional emotion recognition, and
personality recognition. Comprehensive results demonstrate that SVFAP can learn
powerful affect-related representations via large-scale self-supervised
pre-training and it significantly outperforms previous state-of-the-art methods
on all datasets. Codes will be available at https://github.com/sunlicai/SVFAP.
