Abstract
This paper tackles the problem of passive gaze estimation using both event and frame data. Considering inherently different physiological structures, it's intractable to accurately estimate purely based on a given state. Thus, we reformulate the gaze estimation as the quantification of state transitions from the current state to several prior registered anchor states. Technically, we propose a two-stage learning-based gaze estimation framework to divide the whole gaze estimation process into a coarse-to-fine process of anchor state selection and final gaze location. Moreover, to improve generalization ability, we align a group of local experts with a student network, where a novel denoising distillation algorithm is introduced to utilize denoising diffusion technique to iteratively remove inherent noise of event data. Extensive experiments demonstrate the effectiveness of the proposed method, which greatly surpasses state-of-the-art methods by a large extent of 15$\%$. The code will be publicly available at this https URL.
Abstract (translated)
本论文通过同时考虑事件和帧数据，解决了被动眼动估计的问题。考虑到固有生理结构的不同，仅基于给定状态进行准确估计是不可能的。因此，我们将目光估计重新建模为从当前状态到几个已注册的先验锚定状态的状态转移量的量化。技术上，我们提出了一个基于两级学习的光注意力估计框架，将整个目光估计过程划分为锚定状态选择和最终眼动位置的粗细过程。此外，为了提高泛化能力，我们将一组局部专家与学生网络对齐，引入了一种新的去噪蒸馏算法，利用去噪扩散技术迭代地去除事件数据固有噪声。大量实验证明，所提出的方法的有效性超出了现有方法的很大程度，其性能提高到了15%以上。代码将在这个 https URL 上公开。
URL
https://arxiv.org/abs/2404.00548
