# Cross-Dataset Gaze Estimation by Evidential Inter-intra Fusion

arXiv: https://arxiv.org/abs/2409.04766 · HTML: https://mlsanity.com/paper/2409.04766/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 38 references that are in the map.
- First posted on arXiv 7 Sep 2024.

## Builds on (most cited first, 18 of 18)

- [Deep Evidential Regression](https://mlsanity.com/paper/1910.02600/) (2019)
- [AlphaPose: Whole-Body Regional Multi-Person Pose Estimation and Tracking in Real-Time](https://mlsanity.com/paper/2211.03375/) (2022)
- [Gaze360: Physically Unconstrained Gaze Estimation in the Wild](https://mlsanity.com/paper/1910.10088/) (2019)
- [ETH-XGaze: A Large Scale Dataset for Gaze Estimation under Extreme Head Pose and Gaze Variation](https://mlsanity.com/paper/2007.15837/) (2020)
- [Attentional Feature Fusion](https://mlsanity.com/paper/2009.14082/) (2020)
- [Appearance-based Gaze Estimation With Deep Learning: A Review and Benchmark](https://mlsanity.com/paper/2104.12668/) (2021)
- [Group-aware Contrastive Regression for Action Quality Assessment](https://mlsanity.com/paper/2108.07797/) (2021)
- [PureGaze: Purifying Gaze Feature for Generalizable Gaze Estimation](https://mlsanity.com/paper/2103.13173/) (2021)
- [Generalizing Gaze Estimation with Outlier-guided Collaborative Adaptation](https://mlsanity.com/paper/2107.13780/) (2021)
- [BridgeNet: A Continuity-Aware Probabilistic Network for Age Estimation](https://mlsanity.com/paper/1904.03358/) (2019)
- [Trustworthy Multimodal Regression with Mixture of Normal-inverse Gamma Distributions](https://mlsanity.com/paper/2111.08456/) (2021)
- [Domain Adaptation Gaze Estimation by Embedding with Prediction Consistency](https://mlsanity.com/paper/2011.07526/) (2020)
- [ELFNet: Evidential Local-global Fusion for Stereo Matching](https://mlsanity.com/paper/2308.00728/) (2023)
- [ScaleDet: A Scalable Multi-Dataset Object Detector](https://mlsanity.com/paper/2306.04849/) (2023)
- [Uncertainty estimation for Cross-dataset performance in Trajectory prediction](https://mlsanity.com/paper/2205.07310/) (2022)
- [Gaze Estimation for Assisted Living Environments](https://mlsanity.com/paper/1909.09225/) (2019)
- [Cross-dataset Training for Class Increasing Object Detection](https://mlsanity.com/paper/2001.04621/) (2020)
- [PCFGaze: Physics-Consistent Feature for Appearance-based Gaze Estimation](https://mlsanity.com/paper/2309.02165/) (2023)

## Built on by (most cited first, 1 of 1)

- [DMAGaze: Gaze Estimation Based on Feature Disentanglement and Multi-Scale Attention](https://mlsanity.com/paper/2504.11160/) (2025)

## Abstract (arXiv)

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.