Self-supervised hierarchical event representation learning

    公开(公告)号:US11948358B2

    公开(公告)日:2024-04-02

    申请号:US17455126

    申请日:2021-11-16

    Applicant: ADOBE INC.

    CPC classification number: G06V20/41 G06N3/088 G06V20/47 G06V20/44

    Abstract: Systems and methods for video processing are described. Embodiments of the present disclosure generate a plurality of image feature vectors corresponding to a plurality of frames of a video; generate a plurality of low-level event representation vectors based on the plurality of image feature vectors, wherein a number of the low-level event representation vectors is less than a number of the image feature vectors; generate a plurality of high-level event representation vectors based on the plurality of low-level event representation vectors, wherein a number of the high-level event representation vectors is less than the number of the low-level event representation vectors; and identify a plurality of high-level events occurring in the video based on the plurality of high-level event representation vectors.

    SELF-SUPERVISED HIERARCHICAL EVENT REPRESENTATION LEARNING

    公开(公告)号:US20230154186A1

    公开(公告)日:2023-05-18

    申请号:US17455126

    申请日:2021-11-16

    Applicant: ADOBE INC.

    CPC classification number: G06K9/00718 G06K9/00751 G06N3/088 G06K2009/00738

    Abstract: Systems and methods for video processing are described. Embodiments of the present disclosure generate a plurality of image feature vectors corresponding to a plurality of frames of a video; generate a plurality of low-level event representation vectors based on the plurality of image feature vectors, wherein a number of the low-level event representation vectors is less than a number of the image feature vectors; generate a plurality of high-level event representation vectors based on the plurality of low-level event representation vectors, wherein a number of the high-level event representation vectors is less than the number of the low-level event representation vectors; and identify a plurality of high-level events occurring in the video based on the plurality of high-level event representation vectors.

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