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
Cite
@article{arxiv.2401.16076,
title = {Find the Cliffhanger: Multi-Modal Trailerness in Soap Operas},
author = {Carlo Bretti and Pascal Mettes and Hendrik Vincent Koops and Daan Odijk and Nanne van Noord},
journal= {arXiv preprint arXiv:2401.16076},
year = {2024}
}