Attention is a powerful concept in computer vision. End-to-end networks that learn to focus
selectively on regions of an image or video often perform strongly. However, other image …

Attention networks perform well on diverse computer vision tasks. The core idea is that the
signal of interest is stronger in some pixels (" foreground"), and by selectively focusing …

Camera-based physiological measurement is a growing field with neural models providing
state-the-art-performance. Prior research have explored various" end-to-end" models; …

Remote photoplethysmography (rPPG) based physiological measurement has great
application values in health monitoring, emotion analysis, etc. Existing methods mainly focus …

Video-based remote physiological measurement utilizes face videos to measure the blood
volume change signal, which is also called remote photoplethysmography (rPPG) …

The growing need for technology that supports remote healthcare is being acutely
highlighted by an aging population and the COVID-19 pandemic. In health-related machine …

Non-invasive cardiac sensing has many applications. Cameras specifically are ubiquitous,
low-cost, spatial sensors that can also be used to capture context alongside physiological …

Vital signs are crucial indicators for human health, and researchers are studying contact-free
alternatives to existing wearable vital signs sensors. Unfortunately, most of these designs …

Deep learning approaches currently achieve the state-of-the-art results on camera-based
vital signs measurement. One of the main challenges with using neural models for these …

Video-based physiological signal estimation has been limited primarily to predicting
episodic scores in windowed intervals. While these intermittent values are useful, they …