mlsanityData to 14 Oct 2026

A Comparative Study of Scanpath Models in Graph-Based Visualization

arXiv 2503.24160 ↗First posted 31 Mar 2025cs.HC cs.CVcited by 0

Not on the map. Why?

Abstract (arXiv)

Information Visualization (InfoVis) systems utilize visual representations to enhance data interpretation. Understanding how visual attention is allocated is essential for optimizing interface design. However, collecting Eye-tracking (ET) data presents challenges related to cost, privacy, and scalability. Computational models provide alternatives for predicting gaze patterns, thereby advancing InfoVis research. In our study, we conducted an ET experiment with 40 participants who analyzed graphs while responding to questions of varying complexity within the context of digital forensics. We compared human scanpaths with synthetic ones generated by models such as DeepGaze, UMSS, and Gazeformer. Our research evaluates the accuracy of these models and examines how question complexity and number of nodes influence performance. This work contributes to the development of predictive modeling in visual analytics, offering insights that can enhance the design and effectiveness of InfoVis systems.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 13 of its 56 references that are in the map. First posted on arXiv 31 Mar 2025.

Builds on

13 of 56 references.

References · 13

  1. 2015CAT2000: A Large Scale Fixation Dataset for Boosting Saliency Researchcited by 52
  2. 2023Gazeformer: Scalable, Effective and Fast Prediction of Goal-Directed Human Attentioncited by 38
  3. 2018PathGAN: Visual Scanpath Prediction with Generative Adversarial Networkscited by 32
  4. 2020Predicting Visual Importance Across Graphic Design Typescited by 30
  5. 2017SaltiNet: Scan-path Prediction on 360 Degree Images using Saliency Volumescited by 29
  6. 2023TempSAL -- Uncovering Temporal Information for Deep Saliency Predictioncited by 25
  7. 2021ScanGAN360: A Generative Model of Realistic Scanpaths for 360circ Imagescited by 23
  8. 2023Eyettention: An Attention-based Dual-Sequence Model for Predicting Human Scanpaths during Readingcited by 19
  9. 2023Synthesizing Human Gaze Feedback for Improved NLP Performancecited by 16
  10. 2024Trends, Applications, and Challenges in Human Attention Modellingcited by 9
  11. 2025Chartist: Task-driven Eye Movement Control for Chart Readingcited by 8
  12. 2017BubbleView: an interface for crowdsourcing image importance maps and tracking visual attentioncited by 3
1 more
  1. 2024Impact of Design Decisions in Scanpath Modelingcited by 2

Built on by

Not cited yet.