A Case Study of Deep Learning-Based Multi-Modal Methods for Labeling the Presence of Questionable Content in Movie Trailers

Abstract

In this work, we explore different approaches to combine modalities for the problem of automated age-suitability rating of movie trailers. First, we introduce a new dataset containing videos of movie trailers in English downloaded from IMDB and YouTube, along with their corresponding age-suitability rating labels. Secondly, we propose a multi-modal deep learning pipeline addressing the movie trailer age suitability rating problem. This is the first attempt to combine video, audio, and speech information for this problem, and our experimental results show that multi-modal approaches significantly outperform the best mono and bimodal models in this task.

A Smart Movie Suitability Rating System Based on Subtitle

“…They segmented the entire movie into shots and classify these shots into violence and non-violence classes using a lightweight DL model. Shafaei et al [30] developed a multimodal DL pipeline addressing the movie trailer age suitability rating problem. They attempted to combine video, audio, and speech information.…”