Video summarization technologies aim to create a concise and complete synopsis by
selecting the most informative parts of the video content. Several approaches have been …

The exponential growth in the usage of computing technologies in various applications has
led to the creation of huge amount of multimedia information such as, video, audio, and text …

Most of the prior works summarize videos by either exploring different heuristically designed
criteria in an unsupervised way or develo** fully supervised algorithms by leveraging …

Video summarization is a challenging under-constrained problem because the underlying
summary of a single video strongly depends on users' subjective understandings. Data …

In recent years, supervised video summarization has achieved promising progress with
various recurrent neural networks (RNNs) based methods, which treats video summarization …

Large collections of videos are grouped into clusters by a topic keyword, such as" Eiffel
Tower" or" Surfing", with many important visual concepts repeating across them. Such a …

Most traditional video summarization methods are designed to generate effective summaries
for single-view videos, and thus, they cannot fully exploit the complicated intra-and inter …

Most video summarization approaches have focused on extracting a summary from a single
video; we propose an unsupervised framework for summarizing a collection of videos. We …

For many intelligent applications with limited computation, communication, storage and
energy resources, there is an imperative need of vision methods that could select an …

Video summarization is to extract effective information from videos to quickly obtain the most
informative summary. Most of the existing video summarization methods use recurrent …