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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：US20220270310A1
公开(公告)日：2022-08-25
申请号：US17182492
申请日：2021-02-23
Applicant: Adobe Inc.
Inventor： Akhilesh Kumar , Baldo Faieta , Piotr Walczyszyn , Ratheesh Kalarot , Archie Bagnall , Shabnam Ghadar , Wei-An Lin , Cameron Smith , Christian Cantrell , Patrick Hebron , Wilson Chan , Jingwan Lu , Holger Winnemoeller , Sven Olsen
Abstract: The present disclosure describes systems, methods, and non-transitory computer readable media for detecting user interactions to edit a digital image from a client device and modify the digital image for the client device by using a web-based intermediary that modifies a latent vector of the digital image and an image modification neural network to generate a modified digital image from the modified latent vector. In response to user interaction to modify a digital image, for instance, the disclosed systems modify a latent vector extracted from the digital image to reflect the requested modification. The disclosed systems further use a latent vector stream renderer (as an intermediary device) to generate an image delta that indicates a difference between the digital image and the modified digital image. The disclosed systems then provide the image delta as part of a digital stream to a client device to quickly render the modified digital image.
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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：US11928762B2
公开(公告)日：2024-03-12
申请号：US17466699
申请日：2021-09-03
Applicant: ADOBE INC.
Inventor： Akhilesh Kumar , Ratheesh Kalarot , Baldo Faieta , Shabnam Ghadar
IPC: G06T11/60 , G06N3/045 , H04L65/401
CPC classification number: G06T11/60 , G06N3/045 , G06T3/4046 , H04L65/4015
Abstract: Embodiments of the present invention provide systems, methods, and computer storage media for editing images using a web-based intermediary between a user interface on a client device and an image editing neural network(s) (e.g., a generative adversarial network) on a server(s). The present image editing system supports multiple users in the same software container, advanced concurrency of projection and transformation of the same image, clubbing transformation requests from several users hosted in the same software container, and smooth display updates during a progressive projection.
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公开(公告)号：US12136189B2
公开(公告)日：2024-11-05
申请号：US17172744
申请日：2021-02-10
Applicant: ADOBE INC.
Inventor： Akhilesh Kumar , Zhe Lin , Baldo Faieta
IPC: G06T7/00 , G06F18/214 , G06F18/2411 , G06N3/04 , G06T5/20
Abstract: The present disclosure describes systems and methods for image enhancement. Embodiments of the present disclosure provide an image enhancement system with a feedback mechanism that provides quantifiable image enhancement information. An image enhancement system may include a discriminator network that determines the quality of the media object. In cases where the discriminator network determines that the media object has a low image quality score (e.g., an image quality score below a quality threshold), the image enhancement system may perform enhancement on the media object using an enhancement network (e.g., using an enhancement network that includes a generative neural network or a generative adversarial network (GAN) model). The discriminator network may then generate an enhancement score for the enhanced media object that may be provided to the user as a feedback mechanism (e.g., where the enhancement score generated by the discriminator network quantifies the enhancement performed by the enhancement network).
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Patent Agency Ranking
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：US11762622B1
公开(公告)日：2023-09-19
申请号：US17663635
申请日：2022-05-16
Applicant: Adobe Inc.
Inventor： Sven Olsen , Shabnam Ghadar , Baldo Faieta , Akhilesh Kumar
CPC classification number: G06F3/1462 , G06F3/1407 , G06F3/1415 , G06T11/60
Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for remotely generating modified digital images utilizing an interactive image editing architecture. For example, the disclosed systems receive an image editing request for remotely editing a digital image utilizing an interactive image editing architecture. In some cases, the disclosed systems maintain, via a canvas worker container, a digital stream that reflects versions of the digital image. The disclosed systems determine, from the digital stream utilizing the canvas worker container, an image differential metric indicating a difference between a first version of the digital image and a second version of the digital image associated with the image editing request. Further, the disclosed systems provide the image differential metric to a client device for rendering the second version of the digital image to reflect a modification corresponding to the user interaction.
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公开(公告)号：US11727614B2
公开(公告)日：2023-08-15
申请号：US17182492
申请日：2021-02-23
Applicant: Adobe Inc.
Inventor： Akhilesh Kumar , Baldo Faieta , Piotr Walczyszyn , Ratheesh Kalarot , Archie Bagnall , Shabnam Ghadar , Wei-An Lin , Cameron Smith , Christian Cantrell , Patrick Hebron , Wilson Chan , Jingwan Lu , Holger Winnemoeller , Sven Olsen
CPC classification number: G06T11/60 , G06N3/04 , G06T11/203
Abstract: The present disclosure describes systems, methods, and non-transitory computer readable media for detecting user interactions to edit a digital image from a client device and modify the digital image for the client device by using a web-based intermediary that modifies a latent vector of the digital image and an image modification neural network to generate a modified digital image from the modified latent vector. In response to user interaction to modify a digital image, for instance, the disclosed systems modify a latent vector extracted from the digital image to reflect the requested modification. The disclosed systems further use a latent vector stream renderer (as an intermediary device) to generate an image delta that indicates a difference between the digital image and the modified digital image. The disclosed systems then provide the image delta as part of a digital stream to a client device to quickly render the modified digital image.
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公开(公告)号：US11494886B2
公开(公告)日：2022-11-08
申请号：US16888473
申请日：2020-05-29
Applicant: ADOBE INC.
Inventor： Akhilesh Kumar , Zhe Lin , William Lawrence Marino
Abstract: Embodiments of the present invention provide systems, methods, and computer storage media for detecting and classifying an exposure defect in an image using neural networks trained via a limited amount of labeled training images. An image may be applied to a first neural network to determine whether the images includes an exposure defect. Detected defective image may be applied to a second neural network to determine an exposure defect classification for the image. The exposure defect classification can includes severe underexposure, medium underexposure, mild underexposure, mild overexposure, medium overexposure, severe overexposure, and/or the like. The image may be presented to a user along with the exposure defect classification.
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公开(公告)号：US20210374931A1
公开(公告)日：2021-12-02
申请号：US16888473
申请日：2020-05-29
Applicant: ADOBE INC.
Inventor： Akhilesh Kumar , Zhe Lin , William Lawrence Marino
Abstract: Embodiments of the present invention provide systems, methods, and computer storage media for detecting and classifying an exposure defect in an image using neural networks trained via a limited amount of labeled training images. An image may be applied to a first neural network to determine whether the images includes an exposure defect. Detected defective image may be applied to a second neural network to determine an exposure defect classification for the image. The exposure defect classification can includes severe underexposure, medium underexposure, mild underexposure, mild overexposure, medium overexposure, severe overexposure, and/or the like. The image may be presented to a user along with the exposure defect classification.
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