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  1. Home
  3. Unifying Top-down And Bottom-up Scanpath Prediction Using Transformers.
  1. Home
  3. Unifying Top-down And Bottom-up Scanpath Prediction Using Transformers.

Related Experiment Video

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
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Published on: April 21, 2023

Unifying Top-down and Bottom-up Scanpath Prediction Using Transformers.

Zhibo Yang1,2, Sounak Mondal1, Seoyoung Ahn1

  • 1Stony Brook University.

Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition
|June 8, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

The Human Attention Transformer (HAT) is a novel model predicting both top-down and bottom-up visual attention. It achieves state-of-the-art results in predicting human eye movements and enhances interpretability.

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Published on: April 21, 2023

Area of Science:

  • Computer Science
  • Cognitive Science
  • Neuroscience

Background:

  • Existing visual attention models typically focus on either top-down or bottom-up attention control.
  • These models are often evaluated using distinct visual search and free-viewing tasks.

Purpose of the Study:

  • To introduce a unified model, the Human Attention Transformer (HAT), capable of predicting both top-down and bottom-up attention control.
  • To establish a new state-of-the-art in computational models of human visual attention.

Main Methods:

  • Developed a novel transformer-based architecture incorporating a simplified foveated retina for spatio-temporal awareness.
  • Implemented a sequential dense prediction architecture outputting dense heatmaps for each fixation, avoiding discretization issues.

Main Results:

  • HAT achieves state-of-the-art performance in predicting human fixation scanpaths across visual search (target-present, target-absent) and free-viewing tasks.
  • The model enhances the interpretability of human gaze behavior.
  • Demonstrated effectiveness, generality, and interpretability in computational attention.

Conclusions:

  • HAT represents a significant advancement in computational attention, offering a unified approach to modeling visual attention.
  • The model's success and interpretability are expected to drive future research in attention prediction.
  • HAT provides a robust framework for understanding human behavior in attention-demanding scenarios.