  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.
