解决可泛化远程生理测量中的域冲突问题
Resolve Domain Conflicts for Generalizable Remote Physiological Measurement
AI总结：
针对远程光电容积描记法（rPPG）跨数据集存在的标签与属性冲突问题，提出域协调框架DOHA，通过相位协调策略与超平面协调优化提升模型泛化性，实验验证其在多协议下显著提升现有方法性能。
摘要：
远程光电容积描记法（rPPG）技术因可无创监测多种生理指标而日益普及，广泛应用于多媒体交互、医疗保健和情绪分析领域。现有rPPG方法利用多个数据集进行训练以提升模型泛化性，但往往忽略了不同数据集间潜在的冲突问题：（1）实例层面上，生理信号标签与面部视频间存在不同相位延迟导致的标签冲突；（2）头部运动、光照变化、肤色类型等引发的分布偏移造成的属性冲突。为解决该问题，我们提出了域协调框架（DOmain-HArmonious framework，DOHA）。具体而言，我们首先提出一种相位协调策略，以消除不确定的相位延迟，同时保留生理信号的时间变化特征。随后，我们设计了超平面协调优化方法，以减少无关的属性偏移，推动模型优化向适配更多有效场景的全局解方向收敛。实验结果表明，在多种协议下，DOHA均显著提升了现有方法的性能。我们的代码可在https://github.com/SWY666/rPPG-DOHA获取。
英文摘要：
Remote photoplethysmography (rPPG) technology has become increasingly popular due to its non-invasive monitoring of various physiological indicators, making it widely applicable in multimedia interaction, healthcare, and emotion analysis. Existing rPPG methods utilize multiple datasets for training to enhance the generalizability of models. However, they often overlook the underlying conflict issues across different datasets, such as (1) label conflict resulting from different phase delays between physiological signal labels and face videos at the instance level, and (2) attribute conflict stemming from distribution shifts caused by head movements, illumination changes, skin types, etc. To address this, we introduce the DOmain-HArmonious framework (DOHA). Specifically, we first propose a harmonious phase strategy to eliminate uncertain phase delays and preserve the temporal variation of physiological signals. Next, we design a harmonious hyperplane optimization that reduces irrelevant attribute shifts and encourages the model's optimization towards a global solution that fits more valid scenarios. Our experiments demonstrate that DOHA significantly improves the performance of existing methods under multiple protocols. Our code is available at https://github.com/SWY666/rPPG-DOHA.
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