Chapter-Llama: Efficient Chaptering in Hour-Long Videos with LLMs
We address the task of video chaptering, i.e., partitioning a long video
timeline into semantic units and generating corresponding chapter titles. While
relatively underexplored, automatic chaptering has the potential to enable
efficient navigation and content retrieval in long-form videos. In this paper,
we achieve strong chaptering performance on hour-long videos by efficiently
addressing the problem in the text domain with our 'Chapter-Llama' framework.
Specifically, we leverage a pretrained large language model (LLM) with large
context window, and feed as input (i) speech transcripts and (ii) captions
describing video frames, along with their respective timestamps. Given the
inefficiency of exhaustively captioning all frames, we propose a lightweight
speech-guided frame selection strategy based on speech transcript content, and
experimentally demonstrate remarkable advantages. We train the LLM to output
timestamps for the chapter boundaries, as well as free-form chapter titles.
This simple yet powerful approach scales to processing one-hour long videos in
a single forward pass. Our results demonstrate substantial improvements (e.g.,
45.3 vs 26.7 F1 score) over the state of the art on the recent VidChapters-7M
benchmark. To promote further research, we release our code and models at our
project page.
67eccbca1006da75eca94d24
Lucas Ventura
admin_assigned
2025-04-06T08:07:08.372Z
62d1583d1e36881a57f041ab
/avatars/33315f8431196e1f6c57162fa37f4eef.svg
Lucas
user
lucas-ventura
67eccbca1006da75eca94d25
Antoine Yang
admin_assigned
2025-04-02T12:28:15.216Z
6512da122f79557a90b1717f
https://cdn-avatars.huggingface.co/v1/production/uploads/6512da122f79557a90b1717f/-rL2Iu_HHPEi1zWOL4cZi.jpeg
Antoine Yang
user
antoineyang
67eccbca1006da75eca94d26
Cordelia Schmid
67eccbca1006da75eca94d27
Gül Varol
Chapter-Llama: Efficient Chaptering in Hour-Long Videos with LLMs
We address the task of video chaptering, i.e., partitioning a long video
timeline into semantic units and generating corresponding chapter titles. While
relatively underexplored, automatic chaptering has the potential to enable
efficient navigation and content retrieval in long-form videos. In this paper,
we achieve strong chaptering performance on hour-long videos by efficiently
addressing the problem in the text domain with our 'Chapter-Llama' framework.
Specifically, we leverage a pretrained large language model (LLM) with large
context window, and feed as input (i) speech transcripts and (ii) captions
describing video frames, along with their respective timestamps. Given the
inefficiency of exhaustively captioning all frames, we propose a lightweight
speech-guided frame selection strategy based on speech transcript content, and
experimentally demonstrate remarkable advantages. We train the LLM to output
timestamps for the chapter boundaries, as well as free-form chapter titles.
This simple yet powerful approach scales to processing one-hour long videos in
a single forward pass. Our results demonstrate substantial improvements (e.g.,
45.3 vs 26.7 F1 score) over the state of the art on the recent VidChapters-7M
benchmark. To promote further research, we release our code and models at our
project page.
A framework using a pretrained large language model for video chaptering enhances performance by efficiently processing speech transcripts and selected video frames to generate chapter titles and timestamps.
pretrained large language model (LLM) large context window speech transcripts speech-guided frame selection VidChapters-7M benchmark