  Facial video-based remote physiological measurement is a promising research
area for detecting human vital signs (e.g., heart rate, respiration frequency)
in a non-contact way. Conventional approaches are mostly supervised learning,
requiring extensive collections of facial videos and synchronously recorded
photoplethysmography (PPG) signals. To tackle it, self-supervised learning has
recently gained attentions; due to the lack of ground truth PPG signals, its
performance is however limited. In this paper, we propose a novel
self-supervised framework that successfully integrates the popular
vision-language models (VLMs) into the remote physiological measurement task.
Given a facial video, we first augment its positive and negative video samples
with varying rPPG signal frequencies. Next, we introduce a frequency-oriented
vision-text pair generation method by carefully creating contrastive
spatio-temporal maps from positive and negative samples and designing proper
text prompts to describe their relative ratios of signal frequencies. A
pre-trained VLM is employed to extract features for these formed vision-text
pairs and estimate rPPG signals thereafter. We develop a series of generative
and contrastive learning mechanisms to optimize the VLM, including the
text-guided visual map reconstruction task, the vision-text contrastive
learning task, and the frequency contrastive and ranking task. Overall, our
method for the first time adapts VLMs to digest and align the frequency-related
knowledge in vision and text modalities. Extensive experiments on four
benchmark datasets demonstrate that it significantly outperforms state of the
art self-supervised methods.
