Find the Cliffhanger: Multi-modal Trailerness in Soap Operas

Open Access
Authors
Publication date 2024
Host editors
  • S. Rudinac
  • A. Hanjalic
  • C. Liem
  • M. Worring
  • B.Þ. Jónsson
  • B. Liu
  • Y. Yamakata
Book title MultiMedia Modeling
Book subtitle 30th International Conference, MMM 2024, Amsterdam, The Netherlands, January 29–February 2, 2024 : proceedings
ISBN
  • 9783031533075
ISBN (electronic)
  • 9783031533082
Series Lecture Notes in Computer Science
Event 30th International Conference on MultiMedia Modeling, MMM 2024
Volume | Issue number II
Pages (from-to) 199–212
Publisher Cham: Springer
Organisations
  • Faculty of Science (FNWI) - Informatics Institute (IVI)
Abstract
Creating a trailer requires carefully picking out and piecing together brief enticing moments out of a longer video, making it a challenging and time-consuming task. This requires selecting moments based on both visual and dialogue information. We introduce a multi-modal method for predicting the trailerness to assist editors in selecting trailer-worthy moments from long-form videos. We present results on a newly introduced soap opera dataset, demonstrating that predicting trailerness is a challenging task that benefits from multi-modal information. Code is available at https://github.com/carlobretti/cliffhanger.
Document type Conference contribution
Language English
Published at https://doi.org/10.1007/978-3-031-53308-2_15
Other links https://github.com/carlobretti/cliffhanger
Downloads
978-3-031-53308-2_15 (Final published version)
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