Not Only Consistency: Enhance Test-Time Adaptation with Spatio-temporal Inconsistency for Remote Physiological Measurement

• URL: http://arxiv.org/abs/2507.07908v1
• Date: Thu, 10 Jul 2025 16:39:49 GMT
• Title: Not Only Consistency: Enhance Test-Time Adaptation with Spatio-temporal Inconsistency for Remote Physiological Measurement
• Authors: Xiao Yang, Yuxuan Fan, Can Liu, Houcheng Su, Weichen Guo, Jiyao Wang, Dengbo He,
• Abstract summary: Remote photo signalsplesthysmography has emerged as a promising non-invasive method for monitoring the camera.<n>We propose a fully Test-Time Adaptation (TTA) strategy tailored for r tasks in this work.<n>Our method consistently outperforms existing techniques, presenting state-of-the-art performance in real-time self-text-supervised adaptation.
• Score: 3.979038581055512
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Remote photoplethysmography (rPPG) has emerged as a promising non-invasive method for monitoring physiological signals using the camera. Although various domain adaptation and generalization methods were proposed to promote the adaptability of deep-based rPPG models in unseen deployment environments, considerations in aspects like privacy concerns and real-time adaptation restrict their application in real-world deployment. Thus, we aim to propose a novel fully Test-Time Adaptation (TTA) strategy tailored for rPPG tasks in this work. Specifically, based on prior knowledge in physiology and our observations, we noticed not only there is spatio-temporal consistency in the frequency domain of rPPG signals, but also that inconsistency in the time domain was significant. Given this, by leveraging both consistency and inconsistency priors, we introduce an innovative expert knowledge-based self-supervised \textbf{C}onsistency-\textbf{i}n\textbf{C}onsistency-\textbf{i}ntegration (\textbf{CiCi}) framework to enhances model adaptation during inference. Besides, our approach further incorporates a gradient dynamic control mechanism to mitigate potential conflicts between priors, ensuring stable adaptation across instances. Through extensive experiments on five diverse datasets under the TTA protocol, our method consistently outperforms existing techniques, presenting state-of-the-art performance in real-time self-supervised adaptation without accessing source data. The code will be released later.

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