Multiple pairwise ranking networks for personalized video summarization

Yassir Saquil, Da Chen, Yuan He, Chuan Li, Yongliang Yang

Research output: Contribution to conferencePaperpeer-review

16 Citations (SciVal)
80 Downloads (Pure)


In this paper, we investigate video summarization in the supervised setting. Since video summarization is subjective to the preference of the end-user, the design of a unique model is limited. In this work, we propose a model that provides personalized video summaries by conditioning the summarization process with predefined categorical user labels referred to as preferences. The underlying method is based on multiple pairwise rankers (called Multi-ranker), where the rankers are trained jointly to provide local summaries as well as a global summarization of a given video. In order to demonstrate the relevance and applications of our method in contrast with a classical global summarizer, we conduct experiments on multiple benchmark datasets, notably through a user study and comparisons with the state-of-art methods in the global video summarization task
Original languageEnglish
Publication statusPublished - 2021
EventIEEE International Conference on Computer Vision (2021) - Montreal, Canada
Duration: 10 Oct 202117 Oct 2021


ConferenceIEEE International Conference on Computer Vision (2021)
Abbreviated titleICCV
Internet address


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