Long-range Multimodal Pretraining for Movie Understanding

• URL: http://arxiv.org/abs/2308.09775v1
• Date: Fri, 18 Aug 2023 18:52:59 GMT
• Title: Long-range Multimodal Pretraining for Movie Understanding
• Authors: Dawit Mureja Argaw, Joon-Young Lee, Markus Woodson, In So Kweon, Fabian Caba Heilbron
• Abstract summary: 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. Our model achieves state-of-the-art on several LVU tasks while being much more data efficient than previous works.
• Score: 79.63187251571391
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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