远程生理测量的持续学习：最小化遗忘并简化推理
Continual Learning for Remote Physiological Measurement: Minimize Forgetting and Simplify Inference
AI总结：
针对rPPG测量中的持续学习问题，提出ADDP方法，采用适配器微调、域原型、特征增强和推理简化策略，并创建首个评估协议，有效缓解灾难性遗忘。
摘要：
远程光电容积描记法（rPPG）近年来因其能够从面部视频中提取生理信号而受到广泛关注。尽管现有的rPPG测量方法在数据集内和跨数据集场景中表现出令人满意的性能，但它们常常忽略了增量学习场景，即训练数据按顺序呈现，从而导致灾难性遗忘的问题。同时，大多数现有的类增量学习方法不适用于rPPG测量。在本文中，我们提出了一种名为ADDP的新方法来解决rPPG测量的持续学习问题。我们首先采用适配器（adapter）在新任务上高效微调模型。然后，我们设计了比常用类原型更适用于rPPG信号回归的域原型。基于这些原型，我们提出了一种特征增强策略来巩固过去的知识，以及一种推理简化策略，将可能被遗忘的任务转化为模型熟悉的任务。为了评估ADDP并实现公平比较，我们创建了第一个针对rPPG测量的持续学习协议。综合实验证明了我们方法在rPPG持续学习中的有效性。源代码可在\url{https://github.com/MayYoY/rPPGDIL}获取。
英文摘要：
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}
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
