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公开(公告)号：US10733452B2
公开(公告)日：2020-08-04
申请号：US16201819
申请日：2018-11-27
Applicant: Adobe Inc.
Inventor： Brunno Fidel Maciel Attorre , William Marino , Xiaozhen Xue , Nicolas Huynh Thien
Abstract: Disclosed herein are techniques for determining brand safety of a video including image frames and audio content. In some embodiments, frame-level features, scene-level features, and video-level features are extracted by a set of frame-level models, a set of scene-level models, and a set of video-level models, respectively. Outputs from lower level models are used as inputs for higher level models. A brand safety score indicating whether it is safe to associate a brand with the video is determined based on the outputs from the set of video-level models. In some embodiments, commercial content associated with the brand is insert into the video that is determined to be safe for the brand.
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Patent Agency Ranking
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.
Inventor： Akhilesh Kumar , Xiaozhen Xue , Daniel Miranda , Nicolas Huynh Thien , Kshitiz Garg
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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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.
Inventor： Akhilesh Kumar , Xiaozhen Xue , Daniel Miranda , Nicolas Huynh Thien , Kshitiz Garg
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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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.
Inventor： Akhilesh Kumar , Xiaozhen Xue , Daniel Miranda , Nicolas Huynh Thien , Kshitiz Garg
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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公开(公告)号：US20200005046A1
公开(公告)日：2020-01-02
申请号：US16201819
申请日：2018-11-27
Applicant: Adobe Inc.
Inventor： Brunno Fidel Maciel Attorre , William Marino , Xiaozhen Xue , Nicolas Huynh Thien
Abstract: Disclosed herein are techniques for determining brand safety of a video including image frames and audio content. In some embodiments, frame-level features, scene-level features, and video-level features are extracted by a set of frame-level models, a set of scene-level models, and a set of video-level models, respectively. Outputs from lower level models are used as inputs for higher level models. A brand safety score indicating whether it is safe to associate a brand with the video is determined based on the outputs from the set of video-level models. In some embodiments, commercial content associated with the brand is insert into the video that is determined to be safe for the brand.
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公开(公告)号：US10522186B2
公开(公告)日：2019-12-31
申请号：US16049690
申请日：2018-07-30
Applicant: Adobe Inc.
Inventor： Brunno Fidel Maciel Attorre , Xiaozhen Xue , Shabbir Marzban , Nicolas Huynh Thien , William L. Marino
IPC: H04N5/93 , G11B27/036 , G11B27/30 , G11B27/34 , G06K9/00 , G10L17/00 , H04N21/81 , H04N21/234 , G10L25/30 , H04N21/233 , G10L15/18 , G10L25/78 , G10L25/24 , G10L25/21 , G10L25/09 , G10L21/007
Abstract: Disclosed herein are techniques for digital content integration. A computer-implemented method includes receiving a target digital content item that includes a plurality of frames, identifying a set of candidate host frames for inserting source digital content items from the plurality of frames based on one or more attributes of the target digital content item, determining a candidate score for each respective candidate host frame of the candidate host frames, and generating host time defining data including identifications and the candidate scores of the candidate host frames, where the candidate score indicates a degree of transition of the target digital content item at the candidate host frame. One or more candidate host frames are then selected based on the candidate scores for inserting one or more source digital content items into the target digital content item.
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