English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Zendy Plus

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Zendy Tools

Zendy Open

This paper addresses the problem of video summarization. Given an inputvideo, the goal is to select a subset of the frames to create a summary videothat optimally captures the important information of the input video. With thelarge amount of videos available online, video summarization provides a usefultool that assists video search, retrieval, browsing, etc. In this paper, weformulate video summarization as a sequence labeling problem. Unlike existingapproaches that use recurrent models, we propose fully convolutional sequencemodels to solve video summarization. We firstly establish a novel connectionbetween semantic segmentation and video summarization, and then adapt popularsemantic segmentation networks for video summarization. Extensive experimentsand analysis on two benchmark datasets demonstrate the effectiveness of ourmodels.

Video Summarization Using Fully Convolutional Sequence Networks