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DSNet: A Flexible Detect-to-Summarize Network for Video Summarization.

Wencheng Zhu, Jiwen Lu, Jiahao Li

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |December 1, 2020
    PubMed
    Summary

    We introduce a novel Detect-to-Summarize network (DSNet) for supervised video summarization. This framework enhances temporal consistency by using interest detection, outperforming existing methods.

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    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Supervised video summarization methods often lack temporal consistency.
    • Existing approaches typically frame summarization as a regression problem.

    Purpose of the Study:

    • To propose a novel Detect-to-Summarize network (DSNet) for supervised video summarization.
    • To introduce an interest detection framework that enforces temporal consistency and integrity.

    Main Methods:

    • Developed an anchor-based DSNet generating temporal interest proposals with multi-scale intervals for content localization and importance prediction.
    • Developed an anchor-free DSNet directly predicting frame importance scores and segment locations, bypassing predefined proposals.
    • Incorporated temporal consistency constraints through an interest detection formulation.

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    Main Results:

    • Both anchor-based and anchor-free DSNet approaches demonstrated significant effectiveness.
    • Experimental results on SumMe and TVSum datasets validated the proposed methods.
    • The interest detection framework offers flexibility for integration with existing methods.

    Conclusions:

    • The DSNet framework successfully addresses temporal consistency and integrity in video summarization.
    • The proposed interest detection approach represents a novel advancement in supervised video summarization.
    • Both anchor-based and anchor-free variants are effective and validated on benchmark datasets.