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    Systems and methods for generating comic books from video and images
    2.
    发明授权
    Systems and methods for generating comic books from video and images 有权

    公开(公告)号：US11532111B1

    公开(公告)日：2022-12-20

    申请号：US17344690

    申请日：2021-06-10

    Applicant: Amazon Technologies, Inc.

    Inventor： Dongqing Zhang ,  Muhammad Raffay Hamid ,  Xiaohan Nie ,  Shixing Chen

    IPC: G06F17/00 ,  G06T11/60 ,  G11B27/031 ,  G10L15/26 ,  G06F40/134 ,  G06V20/40 ,  G06V40/16

    Abstract: Techniques for a comic book feature are described herein. A visual data stream of a video may be parsed into a plurality of frames. Scene boundaries may be determined to generate a scene using the plurality of frames where a scene includes a subset of frames. A key frame may be determined for the scene using the subset of frames. An audio portion of an audio data stream of the video may be identified that maps to the subset of frames based on time information. The key frame may be converted to a comic image based on an algorithm. First dimensions and placement for a data object may be determined for the comic image. The data object may include the audio portion for the comic image. A comic panel may be generated for the comic image that incorporates the data object using the determined first dimensions and the placement.

    Contrastive learning of scene representation guided by video similarities
    3.
    发明授权
    Contrastive learning of scene representation guided by video similarities 有权

    公开(公告)号：US12067779B1

    公开(公告)日：2024-08-20

    申请号：US17668014

    申请日：2022-02-09

    Applicant: Amazon Technologies, Inc.

    Inventor： Shixing Chen ,  Xiang Hao ,  Xiaohan Nie ,  Muhammad Raffay Hamid

    IPC: G06V20/40 ,  G06V10/774

    CPC classification number: G06V20/48 ,  G06V10/774 ,  G06V20/46

    Abstract: A plurality of similar video pairs may be determined based on one or more similarity information types. Each video pair of the plurality of similar video pairs may include a first respective video and a second respective video. For each video pair, one or more similar scene pairs may be determined. Each of the one or more similar scene pairs may include a respective first scene from the first respective video and a second respective scene from the second respective video. An encoder may be trained using a contrastive learning model that contrasts a plurality of similar scene pairs with a plurality of random scenes. The plurality of similar scene pairs may include the one or more scene pairs for each video pair. One or more scene features of one or more other scenes of one or more other videos may be determined using the encoder.

    Ensemble of machine learning models for automatic scene change detection
    5.
    发明授权
    Ensemble of machine learning models for automatic scene change detection 有权

    公开(公告)号：US11776273B1

    公开(公告)日：2023-10-03

    申请号：US17107514

    申请日：2020-11-30

    Applicant: Amazon Technologies, Inc.

    Inventor： Shixing Chen ,  Muhammad Raffay Hamid ,  Vimal Bhat ,  Shiva Krishnamurthy

    IPC: G06V20/40 ,  G06N5/04 ,  G06N20/20 ,  G10L25/78 ,  G06F18/213

    CPC classification number: G06V20/49 ,  G06F18/213 ,  G06N5/04 ,  G06N20/20 ,  G10L25/78

    Abstract: Techniques for automatic scene change detection are described. As one example, a computer-implemented method includes receiving a request to train an ensemble of machine learning models on a training dataset of videos having labels that indicate scene changes to detect a scene change in a video, partitioning each video file of the training dataset of videos into a plurality of shots, training the ensemble of machine learning models into a trained ensemble of machine learning models based at least in part on the plurality of shots of the training dataset of videos and the labels that indicate scene changes, receiving an inference request for an input video, partitioning the input video into a plurality of shots, generating, by the trained ensemble of machine learning models, an inference of one or more scene changes in the input video based at least in part on the plurality of shots of the input video, and transmitting the inference to a client application or to a storage location.

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