Remote heart rate estimation is the measurement of heart rate without any physical contact with the subject and is accomplished using remote photoplethysmography (rPPG) in this work. rPPG signals are usually collected using a video camera with a limitation of being sensitive to multiple contributing factors, e.g. variation in skin tone, lighting condition and facial structure. End-to-end supervised learning approach performs well when training data is abundant, covering a distribution that doesn't deviate too much from the distribution of testing data or during deployment. To cope with the unforeseeable distributional changes during deployment, we propose a transductive meta-learner that takes unlabeled samples during testing (deployment) for a self-supervised weight adjustment (also known as transductive inference), providing fast adaptation to the distributional changes. Using this approach, we achieve state-of-the-art performance on MAHNOB-HCI and UBFC-rPPG.

中文翻译：

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Meta-rPPG：使用转导元学习器的远程心率估计

远程心率估计是在与受试者没有任何身体接触的情况下测量心率，在这项工作中使用远程光电容积描记 (rPPG) 来完成。rPPG 信号通常使用摄像机收集，其限制是对多种影响因素敏感，例如肤色、光照条件和面部结构的变化。当训练数据丰富时，端到端监督学习方法表现良好，覆盖的分布与测试数据的分布或部署过程中没有太大偏差。为了应对部署过程中不可预见的分布变化，我们提出了一种转导元学习器，它在测试（部署）期间采用未标记的样本进行自我监督权重调整（也称为转导推理），提供对分布变化的快速适应。使用这种方法，我们在 MAHNOB-HCI 和 UBFC-rPPG 上实现了最先进的性能。