Remote photoplethysmography (rPPG) is a promising technology that captures
physiological signals from face videos, with potential applications in medical
health, emotional computing, and biosecurity recognition. The demand for rPPG
tasks has expanded from demonstrating good performance on intra-dataset testing
to cross-dataset testing (i.e., domain generalization). However, most existing
methods have overlooked the prior knowledge of rPPG, resulting in poor
generalization ability. In this paper, we propose a novel framework that
simultaneously utilizes explicit and implicit prior knowledge in the rPPG task.
Specifically, we systematically analyze the causes of noise sources (e.g.,
different camera, lighting, skin types, and movement) across different domains
and incorporate these prior knowledge into the network. Additionally, we
leverage a two-branch network to disentangle the physiological feature
distribution from noises through implicit label correlation. Our extensive
experiments demonstrate that the proposed method not only outperforms
state-of-the-art methods on RGB cross-dataset evaluation but also generalizes
well from RGB datasets to NIR datasets. The code is available at
this https URL

本文提出了一种利用远程光电容积图术（rPPG）任务中的显性和隐性先验知识的新框架，通过系统分析不同领域的噪声源，并将这些先验知识纳入网络进行训练，同时利用两个分支网络通过隐式标签相关性去除噪声并提取生理特征分布，我们的实验证明，该方法不仅在 RGB 跨数据集评估上胜过现有方法，而且能够很好地从 RGB 数据集泛化到 NIR 数据集。

通过显式和隐式先验知识的融合推动可泛化的远程生理测量

Advancing Generalizable Remote Physiological Measurement through the  Integration of Explicit and Implicit Prior Knowledge

Large occlusions result in a significant decline in image classification
accuracy. During inference, diverse types of unseen occlusions introduce
out-of-distribution data to the classification model, leading to accuracy
dropping as low as 50%. As occlusions encompass spatially connected regions,
conventional methods involving feature reconstruction are inadequate for
enhancing classification performance. We introduce LEARN: Latent Enhancing
feAture Reconstruction Network -- An auto-encoder based network that can be
incorporated into the classification model before its classifier head without
modifying the weights of classification model. In addition to reconstruction
and classification losses, training of LEARN effectively combines intra- and
inter-class losses calculated over its latent space -- which lead to
improvement in recovering latent space of occluded data, while preserving its
class-specific discriminative information. On the OccludedPASCAL3D+ dataset,
the proposed LEARN outperforms standard classification models (VGG16 and
ResNet-50) by a large margin and up to 2% over state-of-the-art methods. In
cross-dataset testing, our method improves the average classification accuracy
by more than 5% over the state-of-the-art methods. In every experiment, our
model consistently maintains excellent accuracy on in-distribution data.

通过引入名为 LEARN 的基于自动编码器的网络，该网络可以在不修改分类模型权重的情况下将其合并到分类模型，该方法在大遮挡下的图像分类问题中的性能显著优于标准分类模型，并且比其他最先进的方法提高了 2% 至多达 5% 的分类准确率。