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Glimpse: A Gaze-Based Measure of Temporal Salience.

V Javier TraverJudith ZoríoLuis A Leiva
Published in: Sensors (Basel, Switzerland) (2021)
Temporal salience considers how visual attention varies over time. Although visual salience has been widely studied from a spatial perspective, its temporal dimension has been mostly ignored, despite arguably being of utmost importance to understand the temporal evolution of attention on dynamic contents. To address this gap, we proposed Glimpse, a novel measure to compute temporal salience based on the observer-spatio-temporal consistency of raw gaze data. The measure is conceptually simple, training free, and provides a semantically meaningful quantification of visual attention over time. As an extension, we explored scoring algorithms to estimate temporal salience from spatial salience maps predicted with existing computational models. However, these approaches generally fall short when compared with our proposed gaze-based measure. Glimpse could serve as the basis for several downstream tasks such as segmentation or summarization of videos. Glimpse's software and data are publicly available.
Keyphrases
  • functional connectivity
  • working memory
  • machine learning
  • deep learning
  • big data
  • data analysis