Weakly-supervised video summarization using variational encoder-decoder and web prior

Sijia Cai, Wangmeng Zuo, Larry S. Davis, Lei Zhang

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

5 Citations (Scopus)


Video summarization is a challenging under-constrained problem because the underlying summary of a single video strongly depends on users’ subjective understandings. Data-driven approaches, such as deep neural networks, can deal with the ambiguity inherent in this task to some extent, but it is extremely expensive to acquire the temporal annotations of a large-scale video dataset. To leverage the plentiful web-crawled videos to improve the performance of video summarization, we present a generative modelling framework to learn the latent semantic video representations to bridge the benchmark data and web data. Specifically, our framework couples two important components: a variational autoencoder for learning the latent semantics from web videos, and an encoder-attention-decoder for saliency estimation of raw video and summary generation. A loss term to learn the semantic matching between the generated summaries and web videos is presented, and the overall framework is further formulated into a unified conditional variational encoder-decoder, called variational encoder-summarizer-decoder (VESD). Experiments conducted on the challenging datasets CoSum and TVSum demonstrate the superior performance of the proposed VESD to existing state-of-the-art methods. The source code of this work can be found at https://github.com/cssjcai/vesd.

Original languageEnglish
Title of host publicationComputer Vision – ECCV 2018 - 15th European Conference, 2018, Proceedings
EditorsVittorio Ferrari, Cristian Sminchisescu, Yair Weiss, Martial Hebert
Number of pages18
ISBN (Print)9783030012632
Publication statusPublished - 1 Jan 2018
Event15th European Conference on Computer Vision, ECCV 2018 - Munich, Germany
Duration: 8 Sep 201814 Sep 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11218 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference15th European Conference on Computer Vision, ECCV 2018


  • Variational autoencoder
  • Video summarization

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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