# Deep semantic gaze embedding and scanpath comparison for expertise classification during OPT viewing

arXiv: https://arxiv.org/abs/2003.13987 · HTML: https://mlsanity.com/paper/2003.13987/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Mar 2020.

## Built on by (most cited first, 17 of 17)

- [Generalizing Gaze Estimation with Outlier-guided Collaborative Adaptation](https://mlsanity.com/paper/2107.13780/) (2021)
- [MedEyes: Learning Dynamic Visual Focus for Medical Progressive Diagnosis](https://mlsanity.com/paper/2511.22018/) (2025)
- [Human Attention in Fine-grained Classification](https://mlsanity.com/paper/2111.01628/) (2021)
- [GazeOnce: Real-Time Multi-Person Gaze Estimation](https://mlsanity.com/paper/2204.09480/) (2022)
- [3DGazeNet: Generalizing Gaze Estimation with Weak-Supervision from Synthetic Views](https://mlsanity.com/paper/2212.02997/) (2022)
- [Test-Time Personalization with Meta Prompt for Gaze Estimation](https://mlsanity.com/paper/2401.01577/) (2024)
- [UVAGaze: Unsupervised 1-to-2 Views Adaptation for Gaze Estimation](https://mlsanity.com/paper/2312.15644/) (2023)
- [LEyes: A Lightweight Framework for Deep Learning-Based Eye Tracking using Synthetic Eye Images](https://mlsanity.com/paper/2309.06129/) (2023)
- [PCFGaze: Physics-Consistent Feature for Appearance-based Gaze Estimation](https://mlsanity.com/paper/2309.02165/) (2023)
- [When Eye-Tracking Meets Machine Learning: A Systematic Review on Applications in Medical Image Analysis](https://mlsanity.com/paper/2403.07834/) (2024)
- [GazeCLIP: Enhancing Gaze Estimation Through Text-Guided Multimodal Learning](https://mlsanity.com/paper/2401.00260/) (2023)
- [Vision-language models for decoding provider attention during neonatal resuscitation](https://mlsanity.com/paper/2404.01207/) (2024)
- [Differentiating Surgeon Expertise Solely by Eye Movement Features](https://mlsanity.com/paper/2102.08155/) (2021)
- [Contrastive Weighted Learning for Near-Infrared Gaze Estimation](https://mlsanity.com/paper/2211.03073/) (2022)
- [A Trainable Feature Extractor Module for Deep Neural Networks and Scanpath Classification](https://mlsanity.com/paper/2403.12493/) (2024)
- [NMF-Based Analysis of Mobile Eye-Tracking Data](https://mlsanity.com/paper/2404.03417/) (2024)
- [See Through the Noise: Improving Domain Generalization in Gaze Estimation](https://mlsanity.com/paper/2604.16562/) (2026)

## Abstract (arXiv)

Modeling eye movement indicative of expertise behavior is decisive in user evaluation. However, it is indisputable that task semantics affect gaze behavior. We present a novel approach to gaze scanpath comparison that incorporates convolutional neural networks (CNN) to process scene information at the fixation level. Image patches linked to respective fixations are used as input for a CNN and the resulting feature vectors provide the temporal and spatial gaze information necessary for scanpath similarity comparison.We evaluated our proposed approach on gaze data from expert and novice dentists interpreting dental radiographs using a local alignment similarity score. Our approach was capable of distinguishing experts from novices with 93% accuracy while incorporating the image semantics. Moreover, our scanpath comparison using image patch features has the potential to incorporate task semantics from a variety of tasks