Video Summarization With Attention-Based Encoder-Decoder Networks

This paper addresses the problem of supervised video summarization by formulating it as a sequence-to-sequence learning problem, where the input is a sequence of original video frames, and the output is a keyshot sequence. Our key idea is to learn a deep summarization network with attention mechanis...

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Veröffentlicht in:	IEEE transactions on circuits and systems for video technology Jg. 30; H. 6; S. 1709 - 1717
Hauptverfasser:	Ji, Zhong, Xiong, Kailin, Pang, Yanwei, Li, Xuelong
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.06.2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Additives attention mechanism Coders Datasets Decoding encoder-decoder Frames (data processing) Indexes Internet LSTM Networks Recurrent neural networks Semantics Video data Video summarization Visualization
ISSN:	1051-8215, 1558-2205
Online-Zugang:	Volltext
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