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Learning to Model Task-Oriented Attention.

Xiaochun Zou1, Xinbo Zhao2, Jian Wang2

  • 1School of Electronics and Information, Northwestern Polytechnical University, Chang'an Campus, P.O. Box 886, Xi'an, Shaanxi 710129, China.

Computational Intelligence and Neuroscience
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Summary
This summary is machine-generated.

This study introduces a new model for predicting human visual attention during specific search tasks. Incorporating target information significantly improves the accuracy of predicting where people look in images.

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Area of Science:

  • Computer Vision
  • Human-Computer Interaction
  • Cognitive Science

Background:

  • Predicting human visual attention is crucial for graphics, design, and HCI applications.
  • Existing saliency models often focus on free-viewing tasks and lack task-specific semantic understanding, leading to inaccurate predictions for goal-directed behavior.

Purpose of the Study:

  • To develop a more accurate model for predicting task-oriented visual attention.
  • To investigate the role of target information in guiding visual search within complex scenes.

Main Methods:

  • Collected eye-tracking data from 11 subjects performing specific search tasks across 1307 images.
  • Utilized annotation data of 2,511 segmented objects with detailed semantic attributes.
  • Developed a saliency model integrating bottom-up image features with target position features.

Main Results:

  • The developed model demonstrated improved prediction of fixation locations compared to traditional free-viewing models.
  • Experimental results highlighted the significant contribution of target information in predicting task-driven visual attention.

Conclusions:

  • Task-oriented visual attention models must incorporate target-specific information for accurate predictions.
  • This research provides a foundation for more effective saliency prediction in goal-directed human-computer interaction and image analysis.