# Appearance-Based Gaze Estimation Using Dilated-Convolutions

arXiv: https://arxiv.org/abs/1903.07296 · HTML: https://mlsanity.com/paper/1903.07296/

## Key facts

- Cited by 46 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Mar 2019.

## Built on by (most cited first, 30 of 46)

- [Appearance-based Gaze Estimation With Deep Learning: A Review and Benchmark](https://mlsanity.com/paper/2104.12668/) (2021)
- [Gaze Estimation using Transformer](https://mlsanity.com/paper/2105.14424/) (2021)
- [L2CS-Net: Fine-Grained Gaze Estimation in Unconstrained Environments](https://mlsanity.com/paper/2203.03339/) (2022)
- [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)
- [Automatic Gaze Analysis: A Survey of Deep Learning based Approaches](https://mlsanity.com/paper/2108.05479/) (2021)
- [Adaptive Feature Fusion Network for Gaze Tracking in Mobile Tablets](https://mlsanity.com/paper/2103.11119/) (2021)
- [DVGaze: Dual-View Gaze Estimation](https://mlsanity.com/paper/2308.10310/) (2023)
- [End-to-end Video Gaze Estimation via Capturing Head-face-eye Spatial-temporal Interaction Context](https://mlsanity.com/paper/2310.18131/) (2023)
- [An End-to-End Review of Gaze Estimation and its Interactive Applications on Handheld Mobile Devices](https://mlsanity.com/paper/2307.00122/) (2023)
- [NeRF-Gaze: A Head-Eye Redirection Parametric Model for Gaze Estimation](https://mlsanity.com/paper/2212.14710/) (2022)
- [Rotation-Constrained Cross-View Feature Fusion for Multi-View Appearance-based Gaze Estimation](https://mlsanity.com/paper/2305.12704/) (2023)
- [Enhancing 3D Gaze Estimation in the Wild using Weak Supervision with Gaze Following Labels](https://mlsanity.com/paper/2502.20249/) (2025)
- [CrossGaze: A Strong Method for 3D Gaze Estimation in the Wild](https://mlsanity.com/paper/2402.08316/) (2024)
- [Gaze Estimation with an Ensemble of Four Architectures](https://mlsanity.com/paper/2107.01980/) (2021)
- [Towards Self-Supervised Gaze Estimation](https://mlsanity.com/paper/2203.10974/) (2022)
- [FLAME: Facial Landmark Heatmap Activated Multimodal Gaze Estimation](https://mlsanity.com/paper/2110.04828/) (2021)
- [HAZE-Net: High-Frequency Attentive Super-Resolved Gaze Estimation in Low-Resolution Face Images](https://mlsanity.com/paper/2209.10167/) (2022)
- [Modeling State Shifting via Local-Global Distillation for Event-Frame Gaze Tracking](https://mlsanity.com/paper/2404.00548/) (2024)
- [Toward Super-Resolution for Appearance-Based Gaze Estimation](https://mlsanity.com/paper/2303.10151/) (2023)
- [FR-Net:A Light-weight FFT Residual Net For Gaze Estimation](https://mlsanity.com/paper/2305.11875/) (2023)
- [Appearance Debiased Gaze Estimation via Stochastic Subject-Wise Adversarial Learning](https://mlsanity.com/paper/2401.13865/) (2024)
- [Differential Contrastive Training for Gaze Estimation](https://mlsanity.com/paper/2502.20128/) (2025)
- [PCFGaze: Physics-Consistent Feature for Appearance-based Gaze Estimation](https://mlsanity.com/paper/2309.02165/) (2023)
- [Learning Unsupervised Gaze Representation via Eye Mask Driven Information Bottleneck](https://mlsanity.com/paper/2407.00315/) (2024)
- [Merging Multiple Datasets for Improved Appearance-Based Gaze Estimation](https://mlsanity.com/paper/2409.00912/) (2024)
- [Gaze Estimation Approach Using Deep Differential Residual Network](https://mlsanity.com/paper/2208.04298/) (2022)
- [Dynamic Neural Portraits](https://mlsanity.com/paper/2211.13994/) (2022)
- [Towards Precision in Appearance-based Gaze Estimation in the Wild](https://mlsanity.com/paper/2302.02353/) (2023)
- [Confidence-aware 3D Gaze Estimation and Evaluation Metric](https://mlsanity.com/paper/2303.10062/) (2023)

## Abstract (arXiv)

Appearance-based gaze estimation has attracted more and more attention because of its wide range of applications. The use of deep convolutional neural networks has improved the accuracy significantly. In order to improve the estimation accuracy further, we focus on extracting better features from eye images. Relatively large changes in gaze angles may result in relatively small changes in eye appearance. We argue that current architectures for gaze estimation may not be able to capture such small changes, as they apply multiple pooling layers or other downsampling layers so that the spatial resolution of the high-level layers is reduced significantly. To evaluate whether the use of features extracted at high resolution can benefit gaze estimation, we adopt dilated-convolutions to extract high-level features without reducing spatial resolution. In cross-subject experiments on the Columbia Gaze dataset for eye contact detection and the MPIIGaze dataset for 3D gaze vector regression, the resulting Dilated-Nets achieve significant (up to 20.8%) gains when compared to similar networks without dilated-convolutions. Our proposed Dilated-Net achieves state-of-the-art results on both the Columbia Gaze and the MPIIGaze datasets.