PhySU-Net: Long Temporal Context Transformer for rPPG with
  Self-Supervised Pre-training
Marko Savic Guoying Zhao
Remote photoplethysmography (rPPG) is a promising technology that consists of
contactless measuring of cardiac activity from facial videos. Most recent
approaches utilize convolutional networks with limited temporal modeling
capability or ignore long temporal context. Supervised rPPG methods are also
severely limited by scarce data availability. In this work, we propose
PhySU-Net, the first long spatial-temporal map rPPG transformer network and a
self-supervised pre-training strategy that exploits unlabeled data to improve
our model. Our strategy leverages traditional methods and image masking to
provide pseudo-labels for self-supervised pre-training. Our model is tested on
two public datasets (OBF and VIPL-HR) and shows superior performance in
supervised training. Furthermore, we demonstrate that our self-supervised
pre-training strategy further improves our model's performance by leveraging
representations learned from unlabeled data.