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<p>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 frequency-centric 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 frequency-related generative and contrastive learning mechanisms to optimize the VLM, including the text-guided visual 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. Our codes will be available at <a href="https://github.com/yuezijie/Bootstrapping-VLM-for-Frequency-centric-Self-supervised-Remote-Physiological-Measurement">https://github.com/yuezijie/Bootstrapping-VLM-for-Frequency-centric-Self-supervised-Remote-Physiological-Measurement</a>.</p>

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Bootstrapping Vision-Language Models for Frequency-Centric Self-Supervised Remote Physiological Measurement

  • 期刊论文

  • 发表日期：

    2025年2月21日

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  • Zijie Yue,
  • Miaojing Shi,
  • Hanli Wang,
  • Shuai Ding,
  • Qijun Chen,
  • Shanlin Yang

摘要

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 frequency-centric 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 frequency-related generative and contrastive learning mechanisms to optimize the VLM, including the text-guided visual 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. Our codes will be available at https://github.com/yuezijie/Bootstrapping-VLM-for-Frequency-centric-Self-supervised-Remote-Physiological-Measurement.