Exploring global diverse attention via pairwise temporal relation for video summarization

• URL: http://arxiv.org/abs/2009.10942v1
• Date: Wed, 23 Sep 2020 06:29:09 GMT
• Title: Exploring global diverse attention via pairwise temporal relation for video summarization
• Authors: Ping Li, Qinghao Ye, Luming Zhang, Li Yuan, Xianghua Xu, Ling Shao
• Abstract summary: We propose an efficient convolutional neural network architecture for video SUMmarization via Global Diverse Attention. The proposed models can be run in parallel with significantly less computational costs.
• Score: 84.28263235895798
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Video summarization is an effective way to facilitate video searching and browsing. Most of existing systems employ encoder-decoder based recurrent neural networks, which fail to explicitly diversify the system-generated summary frames while requiring intensive computations. In this paper, we propose an efficient convolutional neural network architecture for video SUMmarization via Global Diverse Attention called SUM-GDA, which adapts attention mechanism in a global perspective to consider pairwise temporal relations of video frames. Particularly, the GDA module has two advantages: 1) it models the relations within paired frames as well as the relations among all pairs, thus capturing the global attention across all frames of one video; 2) it reflects the importance of each frame to the whole video, leading to diverse attention on these frames. Thus, SUM-GDA is beneficial for generating diverse frames to form satisfactory video summary. Extensive experiments on three data sets, i.e., SumMe, TVSum, and VTW, have demonstrated that SUM-GDA and its extension outperform other competing state-of-the-art methods with remarkable improvements. In addition, the proposed models can be run in parallel with significantly less computational costs, which helps the deployment in highly demanding applications.

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