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I-MPN: inductive message passing network for efficient human-in-the-loop annotation of mobile eye tracking data.

Hoang H Le1,2,3, Duy M H Nguyen4,5,6, Omair Shahzad Bhatti7

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This study introduces a new human-centered learning algorithm for automated object recognition in mobile eye-tracking. The method improves efficiency and performance in dynamic visual processing tasks.

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Human-centered AIScene Recognition

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Area of Science:

  • Cognitive Psychology
  • Computer Vision
  • Human-Computer Interaction

Background:

  • Understanding human visual processing in dynamic environments is key for psychology and user-centered design.
  • Manual analysis of mobile eye-tracking data is time-consuming and inefficient.
  • Automated methods are needed to analyze complex visual information from egocentric recordings.

Purpose of the Study:

  • To develop a novel human-centered learning algorithm for automated object recognition in mobile eye-tracking settings.
  • To improve the efficiency and accuracy of analyzing visual data from dynamic environments.
  • To enable better understanding of human visual information processing.

Main Methods:

  • Integration of an object detector with a spatial relation-aware inductive message-passing network (I-MPN).
  • Utilizing node profile information and object correlations for learning embedding functions.
  • Employing an interactive-based learning approach with user feedback for smaller annotated samples.

Main Results:

  • Significant performance improvements over fixed training/testing algorithms on three distinct video sequences.
  • Demonstrated efficiency in data annotation processes.
  • Outperformed prior interactive methods in object recognition tasks within mobile eye-tracking data.

Conclusions:

  • The proposed algorithm offers an efficient and effective solution for automated object recognition in mobile eye-tracking.
  • The human-centered learning approach facilitates rapid adaptation and efficient reasoning in dynamic contexts.
  • This method advances the analysis of visual information processing in real-world environments.