# Enhancing accuracy of uncertainty estimation in appearance-based gaze tracking with probabilistic evaluation and calibration

arXiv: https://arxiv.org/abs/2501.14894 · HTML: https://mlsanity.com/paper/2501.14894/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 28 references that are in the map.
- First posted on arXiv 24 Jan 2025.

## Builds on (most cited first, 11 of 11)

- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles](https://mlsanity.com/paper/1612.01474/) (2016)
- [What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?](https://mlsanity.com/paper/1703.04977/) (2017)
- [A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges](https://mlsanity.com/paper/2011.06225/) (2020)
- [Accurate Uncertainties for Deep Learning Using Calibrated Regression](https://mlsanity.com/paper/1807.00263/) (2018)
- [Gaze360: Physically Unconstrained Gaze Estimation in the Wild](https://mlsanity.com/paper/1910.10088/) (2019)
- [Appearance-Based Gaze Estimation in the Wild](https://mlsanity.com/paper/1504.02863/) (2015)
- [It's Written All Over Your Face: Full-Face Appearance-Based Gaze Estimation](https://mlsanity.com/paper/1611.08860/) (2016)
- [Few-Shot Adaptive Gaze Estimation](https://mlsanity.com/paper/1905.01941/) (2019)
- [L2CS-Net: Fine-Grained Gaze Estimation in Unconstrained Environments](https://mlsanity.com/paper/2203.03339/) (2022)
- [Suppressing Uncertainty in Gaze Estimation](https://mlsanity.com/paper/2412.12890/) (2024)

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

- [Decision-Aware Uncertainty Evaluation of Vision-Language Model-Based Early Action Anticipation for Human-Robot Interaction](https://mlsanity.com/paper/2603.10061/) (2026)

## Abstract (arXiv)

Accurately knowing uncertainties in appearance-based gaze tracking is critical for ensuring reliable downstream applications. Due to the lack of individual uncertainty labels, current uncertainty-aware approaches adopt probabilistic models to acquire uncertainties by following distributions in the training dataset. Without regulations, this approach lets the uncertainty model build biases and overfits the training data, leading to poor performance when deployed. We first presented a strict proper evaluation metric from the probabilistic perspective based on comparing the coverage probability between prediction and observation to provide quantitative evaluation for better assessment on the inferred uncertainties. We then proposed a correction strategy based on probability calibration to mitigate biases in the estimated uncertainties of the trained models. Finally, we demonstrated the effectiveness of the correction strategy with experiments performed on two popular gaze estimation datasets with distinctive image characteristics caused by data collection settings.