  Movie genre classification is an active research area in machine learning.
However, due to the limited labels available, there can be large semantic
variations between movies within a single genre definition. We expand these
'coarse' genre labels by identifying 'fine-grained' semantic information within
the multi-modal content of movies. By leveraging pre-trained 'expert' networks,
we learn the influence of different combinations of modes for multi-label genre
classification. Using a contrastive loss, we continue to fine-tune this
'coarse' genre classification network to identify high-level intertextual
similarities between the movies across all genre labels. This leads to a more
'fine-grained' and detailed clustering, based on semantic similarities while
still retaining some genre information. Our approach is demonstrated on a newly
introduced multi-modal 37,866,450 frame, 8,800 movie trailer dataset,
MMX-Trailer-20, which includes pre-computed audio, location, motion, and image
embeddings.
