LiveSeg: Unsupervised Multimodal Temporal Segmentation of Long Livestream Videos
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Abstract (arXiv)
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.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 39 of its 93 references that are in the map. First posted on arXiv 12 Oct 2022.
Builds on
39 of 93 references.
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Citing papers per year: 2023: 3; 2024: 1; 2025: 1; 2026: 1.
6 papers.
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