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Models predicting video interestingness often prioritize visual aspects while neglecting audio and the overall audiovisual perspective. They typically depend on less interpretable deep learning techniques to enhance prediction efficiency. Video production usually focuses on grammar analysis of trends and viral content rather than exploring signal behaviour or human perception, which are important in other creative fields. This work aims to develop a model that integrates audio, visual, and audiovisual elements, analyzing key instants based on distinct visual and sound characteristics. Handcrafted features are implemented in order to obtain off-the-shelf cues for audiovisual production, which aspires to create potentially interesting content for the viewers.

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Short Video Interestingness: A Machine Learning Approach to Determine Creative Cues in Audiovisual Production

  • 会议论文

  • 发表日期：

    2025年

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  • Claudia Rabaioli,
  • Alessandra Grossi,
  • Francesca Gasparini

摘要

Models predicting video interestingness often prioritize visual aspects while neglecting audio and the overall audiovisual perspective. They typically depend on less interpretable deep learning techniques to enhance prediction efficiency. Video production usually focuses on grammar analysis of trends and viral content rather than exploring signal behaviour or human perception, which are important in other creative fields. This work aims to develop a model that integrates audio, visual, and audiovisual elements, analyzing key instants based on distinct visual and sound characteristics. Handcrafted features are implemented in order to obtain off-the-shelf cues for audiovisual production, which aspires to create potentially interesting content for the viewers.