Abstract
Current deep learning powered appearance based uncertainty-aware gaze estimation models produce inconsistent and unreliable uncertainty estimation that limits their adoptions in downstream applications. In this study, we propose a workflow to improve the accuracy of uncertainty estimation using probability calibration with a few post hoc samples. The probability calibration process employs a simple secondary regression model to compensate for inaccuracies in estimated uncertainties from the deep learning model. Training of the secondary model is detached from the main deep learning model and thus no expensive weight tuning is required. The added calibration process is lightweight and relatively independent from the deep learning process, making it fast to run and easy to implement. We evaluated the effectiveness of the calibration process under four potential application scenarios with two datasets that have distinctive image characteristics due to the data collection setups. The calibration process is most effective when the calibration and testing data share similar characteristics. Even under suboptimal circumstances that calibration and testing data differ, the calibration process can still make corrections to reduce prediction errors in uncertainty estimates made by uncalibrated models.
Abstract (translated)
当前基于深度学习的外观不确定性感知凝视估计模型所产生的不确定性估计不一致且不可靠，这限制了它们在下游应用中的采用。在这项研究中，我们提出了一种使用概率校准和少量后处理样本来提高不确定度估计准确性的工作流程。概率校准过程利用一个简单的二次回归模型来补偿深度学习模型估算的不确定性中的不准确性。该二次模型的训练独立于主要的深度学习模型，因此无需昂贵的权重调整。所增加的校准过程轻量级且相对独立于深度学习过程，使其运行快速且易于实施。 我们使用具有不同图像特征的两个数据集，在四种潜在的应用场景下评估了校准过程的有效性（这些特性源于数据收集设置）。当校准和测试数据具有一些相似特征时，校准过程最有效。即使在不太理想的情况下，即校准和测试数据存在差异时，校准过程仍能进行修正以减少未经校准模型做出的不确定性预测误差。
URL
https://arxiv.org/abs/2501.14894
