Li Qingwen, Chen Jianni, Xie Qiqin, Han Xiao
Shanghai University of Finance and Economics, 777 Guoding Rd, Shanghai, 200433, China.
Shanghai University, 99 Shangda Rd, Shanghai, 200444, China.
Neural Netw. 2023 Apr;161:359-370. doi: 10.1016/j.neunet.2023.01.047. Epub 2023 Feb 3.
Video summarization has long been used to ease video browsing and plays a more crucial role with the explosion of online videos. In the context of event-centric videos, we aim to extract the corresponding clips of more important events in the video. To tackle the dilemma between the detection precision and the clip completeness faced by previous methods, we present an efficient Boundary-Aware framework for Summary clip Extraction (BASE) to extract summary clips with more precise boundaries while maintaining their completeness. Specifically, we propose a new distance-based importance signal to reflect the progress information in each video. The signal can not only help us to detect boundaries with higher precision, but also make it possible to preserve the clip completeness. For the feature presentation part, we also explore new information types to facilitate video summarization. Our approach outperforms current state-of-the-art video summarization models in terms of more precise clip boundaries and more complete summary clips. Note that we even yield comparable results to manual annotations.
视频摘要技术长期以来一直用于方便视频浏览，并且随着在线视频的爆炸式增长发挥着更为关键的作用。在以事件为中心的视频背景下，我们旨在提取视频中更重要事件的相应片段。为了解决先前方法所面临的检测精度和片段完整性之间的困境，我们提出了一种高效的用于摘要片段提取的边界感知框架（BASE），以提取具有更精确边界同时保持其完整性的摘要片段。具体而言，我们提出了一种基于新距离的重要性信号来反映每个视频中的进度信息。该信号不仅可以帮助我们以更高的精度检测边界，还能够保留片段的完整性。对于特征呈现部分，我们还探索了新的信息类型以促进视频摘要。我们的方法在更精确的片段边界和更完整的摘要片段方面优于当前最先进的视频摘要模型。请注意，我们甚至能产生与人工标注相当的结果。
