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GVT2RPM: An Empirical Study for General Video Transformer Adaptation to Remote Physiological Measurement by Wang, Hao; Ahn, Euijoon; Kim, Jinman is a scholarly article available to read on EtoBox.

What is GVT2RPM: An Empirical Study for General Video Transformer Adaptation to Remote Physiological Measurement about?

Remote physiological measurement (RPM) is an essential tool for healthcare monitoring as it enables the measurement of physiological signs, e.g., heart rate, in a remote setting via physical wearables. Recently, with facial videos, we have seen rapid advancements in video-based RPMs. However, adopting facial videos for RPM in the clinical setting largely depends on the accuracy and robustness (work across patient populations). Fortunately, the capability of the state-of-the-art transformer architecture in general (natural) video understanding has resulted in marked improvements and has been translated to facial understanding, including RPM. However, existing RPM methods usually need RPM-specific modules, e.g., temporal difference convolution and handcrafted feature maps. Although these customized modules can increase accuracy, they are not demonstrated for their robustness across datasets. Further, due to their customization of the transformer architecture, they cannot use the advancements made in general video transformers (GVT). In this study, we interrogate the GVT architecture and empirically analyze how the training designs, i.e., data pre-processing and network configurations

Author

Wang, Hao; Ahn, Euijoon; Kim, Jinman

Published

2024

Language

EN