Learning specific hands-on skills such as cooking, car maintenance, and home repairs increasingly happens via instructional videos. The user experience with such videos is known to be improved by meta-information such as time-stamped annotations for the main steps involved. Generating such annotations automatically is challenging, and we describe here two relevant contributions. First, we construct and release a new dense video captioning dataset, Video Timeline Tags (ViTT), featuring a variety of instructional videos together with time-stamped annotations. Second, we explore several multimodal sequence-to-sequence pretraining strategies that leverage large unsupervised datasets of videos and caption-like texts. We pretrain and subsequently finetune dense video captioning models using both YouCook2 and ViTT. We show that such models generalize well and are robust over a wide variety of instructional videos.

中文翻译：

---

多模式预训练用于密集视频字幕

通过教学视频越来越多地学习特定的动手技能，例如烹饪，汽车维修和房屋维修。已知可以通过元信息（例如，所涉及的主要步骤的时间戳注释）来改善此类视频的用户体验。自动生成此类注释具有挑战性，在此我们描述两个相关的贡献。首先，我们构建并发布一个新的密集视频字幕数据集，即视频时间线标签（ViTT），其中包含各种教学视频以及带时间戳的注释。其次，我们探索几种多模式序列到序列的预训练策略，这些策略利用了视频和字幕类文本的大型无监督数据集。我们使用YouCook2和ViTT预训练并随后微调密集视频字幕模型。