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.
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