Edema is a common symptom of kidney disease, and quantitative measurement of edema is desired. This paper presents a method to estimate the degree of edema from facial images taken before and after dialysis of renal failure patients. As tasks to estimate the degree of edema, we perform pre- and post-dialysis classification and body weight prediction. We develop a multi-patient pre-training framework for acquiring knowledge of edema and transfer the pre-trained model to a model for each patient. For effective pre-training, we propose a novel contrastive representation learning, called weight-aware supervised momentum contrast (WeightSupMoCo). WeightSupMoCo aims to make feature representations of facial images closer in similarity of patient weight when the pre- and post-dialysis labels are the same. Experimental results show that our pre-training approach improves the accuracy of pre- and post-dialysis classification by 15.1% and reduces the mean absolute error of weight prediction by 0.243 kg compared with training from scratch. The proposed method accurately estimate the degree of edema from facial images; our edema estimation system could thus be beneficial to dialysis patients.

我们使用隐式表达式从事件数据提出了一部新颖的运动跟踪框架。我们的框架使用预先训练的事件生成MLP命名为隐式事件生成器（IEG），并且通过基于从当前状态估计的所观察到的事件和生成的事件之间的差异来更新其状态（位置和速度）来进行运动跟踪。差异由IEG隐式计算。与传统的显式方法不同，需要密集的计算来评估差异，我们的隐式方法直接从稀疏事件数据实现有效状态更新。我们的稀疏算法特别适用于计算资源和电池寿命有限的移动机器人应用。为了验证我们对现实数据的方法的有效性，我们将其应用于AR标记跟踪应用程序。我们已经证实，我们的框架在噪音和背景混乱存在下的现实环境中运作良好。