Find the Cliffhanger: Multi-modal Trailerness in Soap Operas
| Authors | |
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| Publication date | 2024 |
| Host editors |
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| Book title | MultiMedia Modeling |
| Book subtitle | 30th International Conference, MMM 2024, Amsterdam, The Netherlands, January 29–February 2, 2024 : proceedings |
| ISBN |
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| ISBN (electronic) |
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| 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 |
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| 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.
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| 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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