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.

翻译：电影制作投资因票房收入呈长尾且双峰分布而具有较高风险。准确的票房收入预测可降低不确定性并鼓励投资。然而，为演员、导演及用户生成内容相关关键词学习有效表征仍是一个具有挑战性的开放问题。本研究探讨了自监督预训练的效果，并提出将电影海报中的物体作为内容关键词的视觉对齐预训练目标。在包含35,794部电影的大规模数据集上的实验表明，自监督训练与视觉对齐具有显著优势。特别地，视觉对齐预训练显著提升了含内容关键词的电影学习效果，与采用相同架构的微调BERT模型相比，实现了14.5%的相对性能提升。