论文标题
Meta-RPPG：使用转导元学习器进行远程心率估计
Meta-rPPG: Remote Heart Rate Estimation Using a Transductive Meta-Learner
论文作者
论文摘要
远程心率估计是对心率的测量，没有与受试者进行任何身体接触的情况，并且在这项工作中使用远程照相学（RPPG）完成。通常使用摄像机收集RPPG信号，并限制对多种促成因素敏感，例如肤色，照明状况和面部结构的变化。当培训数据丰富时，端到端监督学习方法表现良好，涵盖了与测试数据分布或部署期间不会偏离太多的分布。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的元学习器，该元元学习器在测试期间采用未标记的样品（部署），以进行自我监督的重量调整（也称为转导推断），从而快速适应分布变化。使用这种方法，我们在Mahnob-HCI和UBFC-RPPG上实现了最先进的性能。
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.
