Continual Learning for Remote Physiological Measurement: Minimize Forgetting and Simplify Inference
This work addresses incremental learning for remote physiological measurement, which is an incremental improvement for domain-specific applications in healthcare monitoring.
The paper tackles the problem of catastrophic forgetting in remote photoplethysmography (rPPG) measurement during incremental learning by proposing a novel method called ADDP, which uses adapters, domain prototypes, and feature augmentation to minimize forgetting and simplify inference, with comprehensive experiments demonstrating its effectiveness.
Remote photoplethysmography (rPPG) has gained significant attention in recent years for its ability to extract physiological signals from facial videos. While existing rPPG measurement methods have shown satisfactory performance in intra-dataset and cross-dataset scenarios, they often overlook the incremental learning scenario, where training data is presented sequentially, resulting in the issue of catastrophic forgetting. Meanwhile, most existing class incremental learning approaches are unsuitable for rPPG measurement. In this paper, we present a novel method named ADDP to tackle continual learning for rPPG measurement. We first employ adapter to efficiently finetune the model on new tasks. Then we design domain prototypes that are more applicable to rPPG signal regression than commonly used class prototypes. Based on these prototypes, we propose a feature augmentation strategy to consolidate the past knowledge and an inference simplification strategy to convert potentially forgotten tasks into familiar ones for the model. To evaluate ADDP and enable fair comparisons, we create the first continual learning protocol for rPPG measurement. Comprehensive experiments demonstrate the effectiveness of our method for rPPG continual learning. Source code is available at \url{https://github.com/MayYoY/rPPGDIL}