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W3090254005 abstract "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." @default.
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W3090254005 date "2021-03-01" @default.
W3090254005 modified "2023-10-16" @default.
W3090254005 title "Exploring global diverse attention via pairwise temporal relation for video summarization" @default.
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W3090254005 doi "https://doi.org/10.1016/j.patcog.2020.107677" @default.
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