We propose Video Localized Narratives, a new form of multimodal video
annotations connecting vision and language. In the original Localized
Narratives, annotators speak and move their mouse simultaneously on an image,
thus grounding each word with a mouse trace segment. However, this is
challenging on a video. Our new protocol empowers annotators to tell the story
of a video with Localized Narratives, capturing even complex events involving
multiple actors interacting with each other and with several passive objects.
We annotated 20k videos of the OVIS, UVO, and Oops datasets, totalling 1.7M
words. Based on this data, we also construct new benchmarks for the video
narrative grounding and video question answering tasks, and provide reference
results from strong baseline models. Our annotations are available at
this https URL

本文提出了一种新形式的多模态视频注释方法：基于视频的定位叙事。在此方法中，注释员不仅可以说出视频中发生的事件，而且还可以使用鼠标追踪分段来定位句子的表示部分。同时，作者采用了该方法对三个数据集中的 20,000 个视频进行了注释，并提供了视频故事理解和视频问题回答任务的评测基准以及来自强基准模型的参考结果。