ArticleJournal of vision2022
DeepGaze III: Modeling free-viewing human scanpaths with deep learning.
Article in Journal of vision, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed.
- Active vision is linked to category selectivity in the individual brain.Nature human behaviour · 2026Article
- Eyettention II: A dual-sequence architecture for modeling fixation location, within-word landing position, and fixation duration in reading.Behavior research methods · 2026Article
- Saccade length consistency during reading and shape-scanning.Attention, perception & psychophysics · 2026Article
- Active Vision in Driving: Joint Modeling of Scanpaths and Risk Perception.Journal of eye movement research · 2026Article
- During natural vision, semantic novelty modulates fixation-related processing in primate cortex.bioRxiv : the preprint server for biology · 2026Article
- Color needs luminance for visual selection during scene search.Attention, perception & psychophysics · 2026Article
- Horizontal saccade bias results from combination of saliency anisotropies and egocentric biases.Scientific reports · 2026Article
- Predictive remapping and allocentric coding as consequences of energy efficiency in recurrent neural network models of active vision.Patterns (New York, N.Y.) · 2026Article
- Pre-viewing context and visual attention in museum scenes: an exploratory eye-tracking study.Frontiers in psychology · 2026Article
- Visual attention graph.Behavior research methods · 2025Article
- Effort and salience jointly drive saccade selection.Psychonomic bulletin & review · 2025Article
- Cortical networks involved in judging the attention of others.Cerebral cortex (New York, N.Y. : 1991) · 2025Article
- Effort drives saccade selection.eLife · 2025Article
- A robotics-inspired scanpath model reveals the importance of uncertainty and semantic object cues for gaze guidance in dynamic scenes.Journal of vision · 2025Article
- Vision toolkit part 3. Scanpaths and derived representations for gaze behavior characterization: a review.Frontiers in physiology · 2025Review
- Unifying Top-down and Bottom-up Scanpath Prediction Using Transformers.Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition · 2024Article
- Oculomotor routines for perceptual judgments.Journal of vision · 2024Article
- Inactivation of face-selective neurons alters eye movements when free viewing faces.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- A review of machine learning in scanpath analysis for passive gaze-based interaction.Frontiers in artificial intelligence · 2024Review
- Saliency models perform best for women's and young adults' fixations.Communications psychology · 2023Article
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3 authors.
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Abstract
Humans typically move their eyes in "scanpaths" of fixations linked by saccades. Here we present DeepGaze III, a new model that predicts the spatial location of consecutive fixations in a free-viewing scanpath over static images. DeepGaze III is a deep learning-based model that combines image information with information about the previous fixation history to predict where a participant might fixate next. As a high-capacity and flexible model, DeepGaze III captures many relevant patterns in the human scanpath data, setting a new state of the art in the MIT300 dataset and thereby providing insight into how much information in scanpaths across observers exists in the first place. We use this insight to assess the importance of mechanisms implemented in simpler, interpretable models for fixation selection. Due to its architecture, DeepGaze III allows us to disentangle several factors that play an important role in fixation selection, such as the interplay of scene content and scanpath history. The modular nature of DeepGaze III allows us to conduct ablation studies, which show that scene content has a stronger effect on fixation selection than previous scanpath history in our main dataset. In addition, we can use the model to identify scenes for which the relative importance of these sources of information differs most. These data-driven insights would be difficult to accomplish with simpler models that do not have the computational capacity to capture such patterns, demonstrating an example of how deep learning advances can be used to contribute to scientific understanding.
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