跨数据集注视估计的证际内-际间融合方法
Cross-Dataset Gaze Estimation by Evidential Inter-intra Fusion
AI总结：
针对跨数据集注视估计中分布偏移导致性能下降的问题，提出证际内-际间融合框架，通过单数据集分支与跨数据集分支结合及不确定性估计，提升源域和未见域的泛化性能。
摘要：
在复杂多样环境中实现准确可靠的注视预测仍然具有挑战性。幸运的是，在现实世界应用中获取多样化的注视数据集是直接的。我们发现，联合训练这些数据集可以显著提高注视估计的泛化能力，这一点在以往的工作中被忽视了。然而，由于不同数据集之间固有的分布偏移，简单地混合多个数据集虽然获得了更好的泛化能力，却降低了原始域的性能。为了解决“跨数据集注视估计”的问题，我们提出了一种新颖的证际内-际间融合（Evidential Inter-intra Fusion, EIF）框架，用于训练一个在所有源域和未见域上均表现良好的跨数据集模型。具体来说，我们为各种数据集构建独立的单数据集分支，其中数据空间在每个数据集内被划分为重叠的子空间用于局部回归，并进一步创建一个跨数据集分支来整合来自单数据集分支的可泛化特征。此外，基于正态和逆伽马（Normal and Inverse-Gamma, NIG）分布的证际回归器被设计为在预测注视之外额外提供不确定性估计。在此基础上，我们提出的框架通过混合正态逆伽马（Mixture of Normal Inverse-Gamma, MoNIG）分布实现了每个数据集内多个局部回归器之间的证际内融合以及多个分支之间的证际间融合。实验表明，我们的方法在源域和未见域上均持续取得了显著的改进。
英文摘要：
Achieving accurate and reliable gaze predictions in complex and diverse environments remains challenging. Fortunately, it is straightforward to access diverse gaze datasets in real-world applications. We discover that training these datasets jointly can significantly improve the generalization of gaze estimation, which is overlooked in previous works. However, due to the inherent distribution shift across different datasets, simply mixing multiple dataset decreases the performance in the original domain despite gaining better generalization abilities. To address the problem of ``cross-dataset gaze estimation'', we propose a novel Evidential Inter-intra Fusion EIF framework, for training a cross-dataset model that performs well across all source and unseen domains. Specifically, we build independent single-dataset branches for various datasets where the data space is partitioned into overlapping subspaces within each dataset for local regression, and further create a cross-dataset branch to integrate the generalizable features from single-dataset branches. Furthermore, evidential regressors based on the Normal and Inverse-Gamma (NIG) distribution are designed to additionally provide uncertainty estimation apart from predicting gaze. Building upon this foundation, our proposed framework achieves both intra-evidential fusion among multiple local regressors within each dataset and inter-evidential fusion among multiple branches by Mixture \textbfof Normal Inverse-Gamma (MoNIG distribution. Experiments demonstrate that our method consistently achieves notable improvements in both source domains and unseen domains.
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。
