  Dynamic facial expression recognition (DFER) is essential to the development
of intelligent and empathetic machines. Prior efforts in this field mainly fall
into supervised learning paradigm, which is severely restricted by the limited
labeled data in existing datasets. Inspired by recent unprecedented success of
masked autoencoders (e.g., VideoMAE), this paper proposes MAE-DFER, a novel
self-supervised method which leverages large-scale self-supervised pre-training
on abundant unlabeled data to largely advance the development of DFER. Since
the vanilla Vision Transformer (ViT) employed in VideoMAE requires substantial
computation during fine-tuning, MAE-DFER develops an efficient local-global
interaction Transformer (LGI-Former) as the encoder. Moreover, in addition to
the standalone appearance content reconstruction in VideoMAE, MAE-DFER also
introduces explicit temporal facial motion modeling to encourage LGI-Former to
excavate both static appearance and dynamic motion information. Extensive
experiments on six datasets show that MAE-DFER consistently outperforms
state-of-the-art supervised methods by significant margins (e.g., +6.30\% UAR
on DFEW and +8.34\% UAR on MAFW), verifying that it can learn powerful dynamic
facial representations via large-scale self-supervised pre-training. Besides,
it has comparable or even better performance than VideoMAE, while largely
reducing the computational cost (about 38\% FLOPs). We believe MAE-DFER has
paved a new way for the advancement of DFER and can inspire more relevant
research in this field and even other related tasks. Codes and models are
publicly available at https://github.com/sunlicai/MAE-DFER.
