论文标题
摘要网络：自动视频摘要的多阶段深度学习模型
SummaryNet: A Multi-Stage Deep Learning Model for Automatic Video Summarisation
论文作者
论文摘要
可以将视频摘要作为提取视频重要部分的任务，以便为视频中发生的内容创建内容丰富的摘要。在本文中，我们将SummaryNet作为自动视频摘要的监督学习框架。 Summarynet采用两个流卷卷网络来学习空间（外观）和时间（运动）表示。它利用编码器模型从学习的视频表示中提取最显着的功能。最后，它使用具有双向长期短期记忆细胞的Sigmoid回归网络来预测框架为摘要框架的概率。基准数据集的实验结果表明，所提出的方法比最先进的视频摘要方法获得了可比或明显更好的结果。
Video summarisation can be posed as the task of extracting important parts of a video in order to create an informative summary of what occurred in the video. In this paper we introduce SummaryNet as a supervised learning framework for automated video summarisation. SummaryNet employs a two-stream convolutional network to learn spatial (appearance) and temporal (motion) representations. It utilizes an encoder-decoder model to extract the most salient features from the learned video representations. Lastly, it uses a sigmoid regression network with bidirectional long short-term memory cells to predict the probability of a frame being a summary frame. Experimental results on benchmark datasets show that the proposed method achieves comparable or significantly better results than the state-of-the-art video summarisation methods.
