Social-MAE: A Transformer-Based Multimodal Autoencoder for Face and
  Voice
Authors: name: Hugo Bohy, name: Minh Tran, name: Kevin El Haddad, name: Thierry Dutoit, name: Mohammad Soleymani

Human social behaviors are inherently multimodal necessitating the
development of powerful audiovisual models for their perception. In this paper,
we present Social-MAE, our pre-trained audiovisual Masked Autoencoder based on
an extended version of Contrastive Audio-Visual Masked Auto-Encoder (CAV-MAE),
which is pre-trained on audiovisual social data. Specifically, we modify
CAV-MAE to receive a larger number of frames as input and pre-train it on a
large dataset of human social interaction (VoxCeleb2) in a self-supervised
manner. We demonstrate the effectiveness of this model by finetuning and
evaluating the model on different social and affective downstream tasks,
namely, emotion recognition, laughter detection and apparent personality
estimation. The model achieves state-of-the-art results on multimodal emotion
recognition and laughter recognition and competitive results for apparent
personality estimation, demonstrating the effectiveness of in-domain
self-supervised pre-training. Code and model weight are available here
https://github.com/HuBohy/SocialMAE.