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DeepGaze III: Modeling free-viewing human scanpaths with deep learning.

Matthias Kümmerer^1,2, Matthias Bethge^1,3, Thomas S A Wallis^4,5

¹University of Tübingen, Tübingen, Germany.

Journal of Vision

|April 26, 2022

Summary

This summary is machine-generated.

DeepGaze III, a new deep learning model, predicts human eye movements (scanpaths) by integrating image content and viewing history. It achieves state-of-the-art results, revealing scene content

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Area of Science:

Computational Neuroscience
Computer Vision
Cognitive Science

Background:

Human eye movements follow predictable patterns called scanpaths, consisting of fixations and saccades.
Understanding these scanpaths is crucial for fields like human-computer interaction and artificial intelligence.

Purpose of the Study:

To develop a novel deep learning model, DeepGaze III, for predicting human fixation locations in scanpaths over static images.
To investigate the relative contributions of scene content and fixation history in guiding eye movements.

Main Methods:

DeepGaze III, a deep learning model, was developed to predict the next fixation point in a scanpath.
The model integrates visual information from images with the history of previous fixations.
Ablation studies were performed to assess the impact of different model components.

Main Results:

DeepGaze III achieved state-of-the-art performance on the MIT300 dataset for scanpath prediction.
Scene content was found to have a stronger influence on fixation selection than prior scanpath history.
The model identified specific scenes where the interplay between content and history varied in importance.

Conclusions:

Deep learning models like DeepGaze III can effectively capture complex patterns in human scanpath data.
Scene content plays a dominant role in directing human gaze compared to immediate viewing history.
DeepGaze III provides valuable insights into the mechanisms of visual attention and fixation selection.