论文标题
Liveseg：长期直播视频的无监督的多模式时间分割
LiveSeg: Unsupervised Multimodal Temporal Segmentation of Long Livestream Videos
论文作者
论文摘要
直播视频已成为在线学习的重要组成部分，在此期间，设计，数字营销，创意绘画和其他技能由会议上经验丰富的专家教授，使其成为宝贵的材料。但是，直播后，直播视频通常是长时间的，记录和上传到互联网，使其他人很难迅速赶上。大纲将是一个有益的解决方案，该解决方案要求视频根据主题进行时间细分。在这项工作中，我们介绍了一个名为Multilive的大型直播视频数据集，并制定了长期直播视频（TSLLV）任务的时间分割。我们提出了Liveseg，这是一种无监督的直播视频时间分割解决方案，它利用了来自不同域中的多模式特征。与最先进的方法相比，我们的方法实现了$ 16.8 \％$ F1分数的性能。
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.
