VidChapters-7M: Video Chapters at Scale
Antoine Yang Arsha Nagrani Ivan Laptev Josef Sivic Cordelia Schmid
Segmenting long videos into chapters enables users to quickly navigate to the
information of their interest. This important topic has been understudied due
to the lack of publicly released datasets. To address this issue, we present
VidChapters-7M, a dataset of 817K user-chaptered videos including 7M chapters
in total. VidChapters-7M is automatically created from videos online in a
scalable manner by scraping user-annotated chapters and hence without any
additional manual annotation. We introduce the following three tasks based on
this data. First, the video chapter generation task consists of temporally
segmenting the video and generating a chapter title for each segment. To
further dissect the problem, we also define two variants of this task: video
chapter generation given ground-truth boundaries, which requires generating a
chapter title given an annotated video segment, and video chapter grounding,
which requires temporally localizing a chapter given its annotated title. We
benchmark both simple baselines and state-of-the-art video-language models for
these three tasks. We also show that pretraining on VidChapters-7M transfers
well to dense video captioning tasks in both zero-shot and finetuning settings,
largely improving the state of the art on the YouCook2 and ViTT benchmarks.
Finally, our experiments reveal that downstream performance scales well with
the size of the pretraining dataset. Our dataset, code, and models are publicly
available at https://antoyang.github.io/vidchapters.html.