mlsanityData to 14 Oct 2026

Find the Cliffhanger: Multi-Modal Trailerness in Soap Operas

arXiv 2401.16076 ↗First posted 29 Jan 2024cs.CV cs.MMcited by 0

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Abstract (arXiv)

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

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 24 of its 45 references that are in the map. First posted on arXiv 29 Jan 2024.

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24 of 45 references.

References · 24

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12 more
  1. 2016Video Summarization with Long Short-term Memorycited by 142
  2. 2018Summarizing Videos with Attentioncited by 69
  3. 2017Video Summarization with Attention-Based Encoder-Decoder Networkscited by 63
  4. 2019Hierarchical Recurrent Neural Network for Video Summarizationcited by 44
  5. 2020Learning Trailer Moments in Full-Length Moviescited by 37
  6. 2020Exploring global diverse attention via pairwise temporal relation for video summarizationcited by 29
  7. 2021GPT2MVS: Generative Pre-trained Transformer-2 for Multi-modal Video Summarizationcited by 26
  8. 2020Query-controllable Video Summarizationcited by 24
  9. 2021Hierarchical Multimodal Transformer to Summarize Videoscited by 19
  10. 2021Finding the Right Moment: Human-Assisted Trailer Creation via Task Compositioncited by 12
  11. 2018From Trailers to Storylines: An Efficient Way to Learn from Moviescited by 10
  12. 2020Realistic Video Summarization through VISIOCITY: A New Benchmark and Evaluation Frameworkcited by 3

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