LiveSeg: Unsupervised Multimodal Temporal Segmentation of Long Livestream Videos
This addresses the challenge for learners and educators in quickly navigating hours-long livestream videos, though it is incremental as it builds on existing segmentation methods.
The paper tackles the problem of automatically segmenting long livestream tutorial videos by topic to create outlines, proposing an unsupervised multimodal method called LiveSeg that achieved a 16.8% F1-score improvement over the state-of-the-art.
Livestream videos have become a significant part of online learning, where design, digital marketing, creative painting, and other skills are taught by experienced experts in the sessions, making them valuable materials. However, Livestream tutorial videos are usually hours long, recorded, and uploaded to the Internet directly after the live sessions, making it hard for other people to catch up quickly. An outline will be a beneficial solution, which requires the video to be temporally segmented according to topics. In this work, we introduced a large Livestream video dataset named MultiLive, and formulated the temporal segmentation of the long Livestream videos (TSLLV) task. We propose LiveSeg, an unsupervised Livestream video temporal Segmentation solution, which takes advantage of multimodal features from different domains. Our method achieved a $16.8\%$ F1-score performance improvement compared with the state-of-the-art method.