Find the Cliffhanger: Multi-Modal Trailerness in Soap Operas

January 29, 2024 · Entered Twilight · 🏛 Conference on Multimedia Modeling

Repo contents: .gitignore, GTSTDataset.py, GTST_datamodule.py, README.md, base_run.py, config, environment.yml, late_fusion_frame_level.py, losses.py, main.py, tables.py, utils.py

Authors Carlo Bretti, Pascal Mettes, Hendrik Vincent Koops, Daan Odijk, Nanne van Noord arXiv ID 2401.16076 Category cs.CV: Computer Vision Cross-listed cs.MM Citations 5 Venue Conference on Multimedia Modeling Repository https://github.com/carlobretti/cliffhanger ⭐ 2 Last Checked 3 months ago

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