标题： EM-Net：使用期望最大化算法的注视估计 显示英文标题

标题： EM-Net: Gaze Estimation with Expectation Maximization Algorithm

摘要： 近年来，眼动估计技术的准确性逐渐提高，但现有方法通常依赖于大型数据集或大型模型来提升性能，这导致对计算资源有较高的要求。 在这一问题方面，本文提出了一种基于深度学习和传统机器学习算法的轻量级眼动估计模型EM-Net，该模型使用了期望最大化算法。 首先，所提出的全局注意力机制（GAM）被添加以提取与眼动估计相关的特征，从而提高模型捕捉全局依赖关系的能力，进而提升其性能。 其次，通过EM模块学习分层特征表示，使模型具有强大的泛化能力，从而减少对样本数量的需求。 实验确认，在仅使用50%训练数据的前提下，与GazeNAS-ETH相比，EM-Net分别在Gaze360、MPIIFaceGaze和RT-Gene数据集上提升了2.2%、2.02%和2.03%的性能。 它在面对高斯噪声干扰时也表现出良好的鲁棒性。 显示英文摘要

摘要： In recent years, the accuracy of gaze estimation techniques has gradually improved, but existing methods often rely on large datasets or large models to improve performance, which leads to high demands on computational resources. In terms of this issue, this paper proposes a lightweight gaze estimation model EM-Net based on deep learning and traditional machine learning algorithms Expectation Maximization algorithm. First, the proposed Global Attention Mechanism(GAM) is added to extract features related to gaze estimation to improve the model's ability to capture global dependencies and thus improve its performance. Second, by learning hierarchical feature representations through the EM module, the model has strong generalization ability, which reduces the need for sample size. Experiments have confirmed that, on the premise of using only 50% of the training data, EM-Net improves the performance of Gaze360, MPIIFaceGaze, and RT-Gene datasets by 2.2%, 2.02%, and 2.03%, respectively, compared with GazeNAS-ETH. It also shows good robustness in the face of Gaussian noise interference.