# Gaze Label Alignment: Alleviating Domain Shift for Gaze Estimation

arXiv: https://arxiv.org/abs/2412.15601 · HTML: https://mlsanity.com/paper/2412.15601/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 37 references that are in the map.
- First posted on arXiv 20 Dec 2024.

## Builds on (most cited first, 21 of 21)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [SGDR: Stochastic Gradient Descent with Warm Restarts](https://mlsanity.com/paper/1608.03983/) (2016)
- [Generalizing to Unseen Domains: A Survey on Domain Generalization](https://mlsanity.com/paper/2103.03097/) (2021)
- [Unified Deep Supervised Domain Adaptation and Generalization](https://mlsanity.com/paper/1709.10190/) (2017)
- [Learning from Simulated and Unsupervised Images through Adversarial Training](https://mlsanity.com/paper/1612.07828/) (2016)
- [Gaze360: Physically Unconstrained Gaze Estimation in the Wild](https://mlsanity.com/paper/1910.10088/) (2019)
- [Eye Tracking for Everyone](https://mlsanity.com/paper/1606.05814/) (2016)
- [Appearance-Based Gaze Estimation in the Wild](https://mlsanity.com/paper/1504.02863/) (2015)
- [ETH-XGaze: A Large Scale Dataset for Gaze Estimation under Extreme Head Pose and Gaze Variation](https://mlsanity.com/paper/2007.15837/) (2020)
- [It's Written All Over Your Face: Full-Face Appearance-Based Gaze Estimation](https://mlsanity.com/paper/1611.08860/) (2016)
- [Domain Generalization for Medical Imaging Classification with Linear-Dependency Regularization](https://mlsanity.com/paper/2009.12829/) (2020)
- [A Coarse-to-Fine Adaptive Network for Appearance-Based Gaze Estimation](https://mlsanity.com/paper/2001.00187/) (2020)
- [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)
- [Domain Adaptation Gaze Estimation by Embedding with Prediction Consistency](https://mlsanity.com/paper/2011.07526/) (2020)
- [A Differential Approach for Gaze Estimation](https://mlsanity.com/paper/1904.09459/) (2019)
- [CLIP-Gaze: Towards General Gaze Estimation via Visual-Linguistic Model](https://mlsanity.com/paper/2403.05124/) (2024)
- [NeRF-Gaze: A Head-Eye Redirection Parametric Model for Gaze Estimation](https://mlsanity.com/paper/2212.14710/) (2022)
- [LG-Gaze: Learning Geometry-aware Continuous Prompts for Language-Guided Gaze Estimation](https://mlsanity.com/paper/2411.08606/) (2024)
- [Efficient Eye Typing with 9-direction Gaze Estimation](https://mlsanity.com/paper/1707.00548/) (2017)
- [SalGaze: Personalizing Gaze Estimation Using Visual Saliency](https://mlsanity.com/paper/1910.10603/) (2019)

## Built on by (most cited first, 3 of 3)

- [Hybrid-Domain Adaptative Representation Learning for Gaze Estimation](https://mlsanity.com/paper/2511.13222/) (2025)
- [Real-time Appearance-based Gaze Estimation for Open Domains](https://mlsanity.com/paper/2603.26945/) (2026)
- [GMGaze: MoE-Based Context-Aware Gaze Estimation with CLIP and Multiscale Transformer](https://mlsanity.com/paper/2605.00799/) (2026)

## Abstract (arXiv)

Gaze estimation methods encounter significant performance deterioration when being evaluated across different domains, because of the domain gap between the testing and training data. Existing methods try to solve this issue by reducing the deviation of data distribution, however, they ignore the existence of label deviation in the data due to the acquisition mechanism of the gaze label and the individual physiological differences. In this paper, we first point out that the influence brought by the label deviation cannot be ignored, and propose a gaze label alignment algorithm (GLA) to eliminate the label distribution deviation. Specifically, we first train the feature extractor on all domains to get domain invariant features, and then select an anchor domain to train the gaze regressor. We predict the gaze label on remaining domains and use a mapping function to align the labels. Finally, these aligned labels can be used to train gaze estimation models. Therefore, our method can be combined with any existing method. Experimental results show that our GLA method can effectively alleviate the label distribution shift, and SOTA gaze estimation methods can be further improved obviously.