Increasing efficiency of inferencing digital videos utilizing machine-learning models

    公开(公告)号:US12067499B2

    公开(公告)日:2024-08-20

    申请号:US17087116

    申请日:2020-11-02

    Applicant: Adobe Inc.

    CPC classification number: G06N5/04 G06N20/00 G06T1/20 G06T3/40 G06V20/49 H04N19/13

    Abstract: This disclosure describes one or more implementations of a video inference system that utilizes machine-learning models to efficiently and flexibly process digital videos utilizing various improved video inference architectures. For example, the video inference system provides a framework for improving digital video processing by increasing the efficiency of both central processing units (CPUs) and graphics processing units (GPUs). In one example, the video inference system utilizes a first video inference architecture to reduce the number of computing resources needed to inference digital videos by analyzing multiple digital videos utilizing sets of CPU/GPU containers along with parallel pipeline processing. In a further example, the video inference system utilizes a second video inference architecture that facilitates multiple CPUs to preprocess multiple digital videos in parallel as well as a GPU to continuously, sequentially, and efficiently inference each of the digital videos.

    EFFICIENTLY INFERENCING DIGITAL VIDEOS UTILIZING MACHINE-LEARNING MODELS

    公开(公告)号:US20240362506A1

    公开(公告)日:2024-10-31

    申请号:US18771409

    申请日:2024-07-12

    Applicant: Adobe Inc.

    CPC classification number: G06N5/04 G06N20/00 G06T1/20 G06T3/40 G06V20/49 H04N19/13

    Abstract: This disclosure describes one or more implementations of a video inference system that utilizes machine-learning models to efficiently and flexibly process digital videos utilizing various improved video inference architectures. For example, the video inference system provides a framework for improving digital video processing by increasing the efficiency of both central processing units (CPUs) and graphics processing units (GPUs). In one example, the video inference system utilizes a first video inference architecture to reduce the number of computing resources needed to inference digital videos by analyzing multiple digital videos utilizing sets of CPU/GPU containers along with parallel pipeline processing. In a further example, the video inference system utilizes a second video inference architecture that facilitates multiple CPUs to preprocess multiple digital videos in parallel as well as a GPU to continuously, sequentially, and efficiently inference each of the digital videos.

    INCREASING EFFICIENCY OF INFERENCING DIGITAL VIDEOS UTILIZING MACHINE-LEARNING MODELS

    公开(公告)号:US20220138596A1

    公开(公告)日:2022-05-05

    申请号:US17087116

    申请日:2020-11-02

    Applicant: Adobe Inc.

    Abstract: This disclosure describes one or more implementations of a video inference system that utilizes machine-learning models to efficiently and flexibly process digital videos utilizing various improved video inference architectures. For example, the video inference system provides a framework for improving digital video processing by increasing the efficiency of both central processing units (CPUs) and graphics processing units (GPUs). In one example, the video inference system utilizes a first video inference architecture to reduce the number of computing resources needed to inference digital videos by analyzing multiple digital videos utilizing sets of CPU/GPU containers along with parallel pipeline processing. In a further example, the video inference system utilizes a second video inference architecture that facilitates multiple CPUs to preprocess multiple digital videos in parallel as well as a GPU to continuously, sequentially, and efficiently inference each of the digital videos.

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