Video ReCap: Recursive Captioning of Hour-Long Videos
Authors: name: Md Mohaiminul Islam, name: Ngan Ho, name: Xitong Yang, name: Tushar Nagarajan, name: Lorenzo Torresani, name: Gedas Bertasius

Most video captioning models are designed to process short video clips of few
seconds and output text describing low-level visual concepts (e.g., objects,
scenes, atomic actions). However, most real-world videos last for minutes or
hours and have a complex hierarchical structure spanning different temporal
granularities. We propose Video ReCap, a recursive video captioning model that
can process video inputs of dramatically different lengths (from 1 second to 2
hours) and output video captions at multiple hierarchy levels. The recursive
video-language architecture exploits the synergy between different video
hierarchies and can process hour-long videos efficiently. We utilize a
curriculum learning training scheme to learn the hierarchical structure of
videos, starting from clip-level captions describing atomic actions, then
focusing on segment-level descriptions, and concluding with generating
summaries for hour-long videos. Furthermore, we introduce Ego4D-HCap dataset by
augmenting Ego4D with 8,267 manually collected long-range video summaries. Our
recursive model can flexibly generate captions at different hierarchy levels
while also being useful for other complex video understanding tasks, such as
VideoQA on EgoSchema. Data, code, and models are available at:
https://sites.google.com/view/vidrecap