  Film media is a rich form of artistic expression. Unlike photography, and
short videos, movies contain a storyline that is deliberately complex and
intricate in order to engage its audience. In this paper we present a large
scale study comparing the effectiveness of visual, audio, text, and
metadata-based features for predicting high-level information about movies such
as their genre or estimated budget. We demonstrate the usefulness of
content-based methods in this domain in contrast to human-based and
metadata-based predictions in the era of deep learning. Additionally, we
provide a comprehensive study of temporal feature aggregation methods for
representing video and text and find that simple pooling operations are
effective in this domain. We also show to what extent different modalities are
complementary to each other. To this end, we also introduce Moviescope, a new
large-scale dataset of 5,000 movies with corresponding movie trailers (video +
audio), movie posters (images), movie plots (text), and metadata.
