  Learning computer vision models from (and for) movies has a long-standing
history. While great progress has been attained, there is still a need for a
pretrained multimodal model that can perform well in the ever-growing set of
movie understanding tasks the community has been establishing. In this work, we
introduce Long-range Multimodal Pretraining, a strategy, and a model that
leverages movie data to train transferable multimodal and cross-modal encoders.
Our key idea is to learn from all modalities in a movie by observing and
extracting relationships over a long-range. After pretraining, we run ablation
studies on the LVU benchmark and validate our modeling choices and the
importance of learning from long-range time spans. Our model achieves
state-of-the-art on several LVU tasks while being much more data efficient than
previous works. Finally, we evaluate our model's transferability by setting a
new state-of-the-art in five different benchmarks.
