Dense video captioning aims to identify the events of interest in an input video, and generate descriptive captions for each event. Previous approaches usually follow a two-stage generative process, which first proposes a segment for each event, then renders a caption for each identified segment. Recent advances in large-scale sequence generation pretraining have seen great success in unifying task formulation for a great variety of tasks, but so far, more complex tasks such as dense video captioning are not able to fully utilize this powerful paradigm. In this work, we show how to model the two subtasks of dense video captioning jointly as one sequence generation task, and simultaneously predict the events and the corresponding descriptions. Experiments on YouCook2 and ViTT show encouraging results and indicate the feasibility of training complex tasks such as end-to-end dense video captioning integrated into large-scale pre-trained models.

本文提出一种联合建模的方法，将稠密视频字幕的两个子任务共同作为一个序列生成任务，同时预测事件和相应的描述，并使用YouCook2和ViTT的实验结果表明了该方法的可行性和在大规模预训练模型中集成端到端密集视频字幕等复杂任务中的适用性。

端到端密集视频字幕生成