This article studies supervised video summarization by formulating it into a sequence-to-
sequence learning framework, in which the input and output are sequences of original video …

With the rapid growth of video data, video summarization technique plays a key role in
reducing people's efforts to explore the content of videos by generating concise but …

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 …

With the explosive growth of videos captured everyday, how to efficiently extract useful
information from videos has become a more and more important problem. As one of the …

Video summarization shortens a lengthy video into a succinct version, whose challenges
mainly originate from the difficulties of discovering the inherent relations between the …

This paper presents a new method for supervised video summarization. To overcome
drawbacks of existing RNN-based summarization architectures, that relate to the modeling …

Video summarization is an important technique to browse, manage and retrieve a large
amount of videos efficiently. The main objective of video summarization is to minimize the …

In this paper, we address the problem of unsupervised video summarization that
automatically extracts key-shots from an input video. Specifically, we tackle two critical …

Modern video summarization methods are based on deep neural networks that require a
large amount of annotated data for training. However, existing datasets for video …

In this work, we describe a new method for unsupervised video summarization. To overcome
limitations of existing unsupervised video summarization approaches, that relate to the …