  Remote photoplethysmography (rPPG) has been widely applied to measure heart
rate from face videos. To increase the generalizability of the algorithms,
domain generalization (DG) attracted increasing attention in rPPG. However,
when rPPG is extended to simultaneously measure more vital signs (e.g.,
respiration and blood oxygen saturation), achieving generalizability brings new
challenges. Although partial features shared among different physiological
signals can benefit multi-task learning, the sparse and imbalanced target label
space brings the seesaw effect over task-specific feature learning. To resolve
this problem, we designed an end-to-end Mixture of Low-rank Experts for
multi-task remote Physiological measurement (PhysMLE), which is based on
multiple low-rank experts with a novel router mechanism, thereby enabling the
model to adeptly handle both specifications and correlations within tasks.
Additionally, we introduced prior knowledge from physiology among tasks to
overcome the imbalance of label space under real-world multi-task physiological
measurement. For fair and comprehensive evaluations, this paper proposed a
large-scale multi-task generalization benchmark, named Multi-Source Synsemantic
Domain Generalization (MSSDG) protocol. Extensive experiments with MSSDG and
intra-dataset have shown the effectiveness and efficiency of PhysMLE. In
addition, a new dataset was collected and made publicly available to meet the
needs of the MSSDG.
