Movie Box Office Prediction With Self-Supervised and Visually Grounded Pretraining
This work addresses uncertainty in movie investments for producers and studios, but it is incremental as it builds on existing pretraining methods.
The paper tackles the problem of predicting movie box office revenue by using self-supervised pretraining and visual grounding of content keywords from movie posters, achieving a 14.5% relative performance gain compared to a finetuned BERT model.
Investments in movie production are associated with a high level of risk as movie revenues have long-tailed and bimodal distributions. Accurate prediction of box-office revenue may mitigate the uncertainty and encourage investment. However, learning effective representations for actors, directors, and user-generated content-related keywords remains a challenging open problem. In this work, we investigate the effects of self-supervised pretraining and propose visual grounding of content keywords in objects from movie posters as a pertaining objective. Experiments on a large dataset of 35,794 movies demonstrate significant benefits of self-supervised training and visual grounding. In particular, visual grounding pretraining substantially improves learning on movies with content keywords and achieves 14.5% relative performance gains compared to a finetuned BERT model with identical architecture.