An Adaptive Knowledge Distillation Framework for Short-Form Remote Physiological Measurement

Video-based remote physiological measurement, enabling non-contact monitoring of physiological signals through facial videos, demonstrates promising prospects in remote medicine and health monitoring. To take advantage of long-term dependency information from cardiac activity, previous methods commonly rely on long video sequences for measurement. These approaches are highly susceptible to various internal and external factors and struggle to capture fluctuations in physiological signals over extended measurement periods reliably. Unlike previous long-form video-based methods, we propose a novel adaptive knowledge distillation framework that utilizes short-form videos. This approach aims to mitigate the impact of internal and external factors while accurately capturing physiological signal fluctuations by reducing the required video length for precise measurement. Specifically, we first propose a long-short-term distillation framework that uses a teacher model to capture long-term dependencies from long-form videos and leverages this capability to guide the training of the student model on short-form videos. Subsequently, we propose a frame-level dynamic prior mechanism (FDPM) that replaces student features similar to teacher features with the corresponding teacher features and dynamically adjusts their proportions. Furthermore, we design an instance adaptive distillation mechanism (IADM) module that dynamically regulates the distillation temperature and the loss weight to develop an optimal distillation strategy for each sample. Finally, we conduct systematic experiments across three benchmark datasets and various video lengths. Experimental results demonstrate the effectiveness of our method, especially in short-form video scenarios.