Find the Cliffhanger: Multi-Modal Trailerness in Soap Operas
This work addresses the time-consuming task of trailer creation for video editors, but it is incremental as it applies existing multi-modal techniques to a new dataset.
The paper tackles the problem of automatically selecting trailer-worthy moments from long-form videos by introducing a multi-modal method to predict trailerness, demonstrating that this task is challenging and benefits from using both visual and dialogue information.
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