In this work we propose a novel method for supervised, keyshots based video
summarization by applying a conceptually simple and computationally efficient soft, self …

Conventional unsupervised video summarization algorithms are usually developed in a
frame level clustering manner. For example, frame level diversity and representativeness …

Conventional video summarization approaches based on reinforcement learning have the
problem that the reward can only be received after the whole summary is generated. Such …

Video summarization aims at generating a compact yet representative visual summary that
conveys the essence of the original video. The advantage of unsupervised approaches is …

The goal of video summarization is to distill a raw video into a more compact form without
losing much semantic information. However, previous methods mainly consider the diversity …

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 …

Video summarization has attracted extensive attention benefiting from its valuable capability
to facilitate video browsing. While achieving notable improvement, existing methods still fail …

This paper studies the video summarization task by formulating it as a sequential decision-
making process, in which the input is a sequence of video frames and the output is a subset …

In this paper, we propose a dynamic graph modeling approach to learn spatial-temporal
representations for video summarization. Most existing video summarization methods extract …

In this paper, we address the problem of shot-level video summarization, which aims at
selecting a subset of video shots as a summary to represent the original video contents …