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Related Experiment Video

Updated: Oct 9, 2025

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
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An annotated video dataset for computing video memorability.

Rukiye Savran Kiziltepe1, Lorin Sweeney2, Mihai Gabriel Constantin3

  • 1University of Essex, UK.

Data in Brief
|December 22, 2021
PubMed
Summary
This summary is machine-generated.

This study explored video memorability using a large dataset of short clips. Human annotations revealed factors influencing both short-term and long-term recall of online video content.

Keywords:
Human memoryMachine learningMediaeval benchmarkVideo memorability

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Area of Science:

  • Cognitive Psychology
  • Computer Vision
  • Human-Computer Interaction

Background:

  • Understanding video memorability is crucial for content creation and recommendation systems.
  • Short-form videos are increasingly prevalent, necessitating research into their cognitive impact.
  • Existing datasets often lack comprehensive annotations for memorability prediction.

Purpose of the Study:

  • To create and analyze a dataset of short-form videos annotated for memorability.
  • To investigate factors influencing short-term and long-term video recall.
  • To facilitate research in computational video memorability prediction.

Main Methods:

  • Collected 1275 user annotations on video memorability (short-term and long-term recall).
  • Utilized an online memory game format for data collection, measuring recognition accuracy and reaction times.
  • Extracted video features including text captions and image-level features from key frames.

Main Results:

  • The dataset provides a rich resource for studying human memory for video content.
  • Analysis of annotations can reveal patterns correlating with video memorability.
  • The dataset was successfully used in the Video Memorability task at MediaEval 2020.

Conclusions:

  • The developed dataset enables further research into predicting video memorability.
  • Understanding memorability factors can improve video content design and delivery.
  • This work contributes to the field of affective computing and multimedia analysis.