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    IDENTIFYING, DESCRIBING, AND SHARING SALIENT EVENTS IN IMAGES AND VIDEOS
    9.
    发明申请
    IDENTIFYING, DESCRIBING, AND SHARING SALIENT EVENTS IN IMAGES AND VIDEOS 审中-公开
    在图像和视频中识别，描述和共享活动

    公开(公告)号：US20140328570A1

    公开(公告)日：2014-11-06

    申请号：US14332071

    申请日：2014-07-15

    Applicant: SRI International

    Inventor： Hui Cheng ,  Harpreet Singh Sawhney ,  Jingen Liu ,  Ishani Chakraborty ,  Omar Javed ,  Steven S. Weiner

    IPC: G11B27/10 ,  G11B27/031

    CPC classification number: G06K9/00718 ,  G06F16/43 ,  G06F16/78 ,  G11B27/031 ,  G11B27/10 ,  H04N21/233 ,  H04N21/23418 ,  H04N21/4394 ,  H04N21/44008 ,  H04N21/8549

    Abstract: A computing system for identifying, describing, and sharing salient events depicted in images and videos executes feature detection algorithms on multimedia input (e.g., video and/or images). The computing system applies semantic reasoning techniques to the output of the feature detection algorithms. The computing system identifies salient event segments of the multimedia input as a result of the semantic reasoning. The computing system can incorporate the salient event segments into a visual presentation, such as a video clip. Alternatively or in addition, the computing system can generate a natural language description of the content of the multimedia input.

    Abstract translation: 用于识别，描述和共享图像和视频中描绘的突出事件的计算系统执行多媒体输入（例如，视频和/或图像）上的特征检测算法。 计算系统将语义推理技术应用于特征检测算法的输出。 作为语义推理的结果，计算系统识别多媒体输入的突出事件段。 计算系统可以将显着事件段合并到诸如视频剪辑的视觉呈现中。 或者或另外，计算系统可以生成多媒体输入的内容的自然语言描述。

    Zero-shot event detection using semantic embedding
    10.
    发明授权
    Zero-shot event detection using semantic embedding 有权

    公开(公告)号：US10963504B2

    公开(公告)日：2021-03-30

    申请号：US16077449

    申请日：2017-02-13

    Applicant: SRI International

    Inventor： Hui Cheng ,  Jingen Liu ,  Harpreet Sawhney ,  Mohamed Elhoseiny

    IPC: G06F16/783 ,  G06F16/435 ,  G06F40/30 ,  G10L15/26 ,  G06K9/34

    Abstract: Zero-shot content detection includes building/training a semantic space by embedding word-based document descriptions of a plurality of documents into a multi-dimensional space using a semantic embedding technique; detecting a plurality of features in the multimodal content by applying feature detection algorithms to the multimodal content; determining respective word-based concept descriptions for concepts identified in the multimodal content using the detected features; embedding the respective word-based concept descriptions into the semantic space; and in response to a content detection action, (i) embedding/mapping words representative of the content detection action into the semantic space, (ii) automatically determining, without the use of training examples, concepts in the semantic space relevant to the content detection action based on the embedded words, and (iii) identifying portions of the multimodal content responsive to the content detection action based on the concepts in the semantic space determined to be relevant to the content detection action.

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