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    Simulated handwriting image generator
    11.
    发明授权
    Simulated handwriting image generator 有权

    公开(公告)号：US11250252B2

    公开(公告)日：2022-02-15

    申请号：US16701586

    申请日：2019-12-03

    Applicant: ADOBE INC.

    Inventor： Christopher Alan Tensmeyer ,  Rajiv Jain ,  Curtis Michael Wigington ,  Brian Lynn Price ,  Brian Lafayette Davis

    IPC: G06K9/00 ,  G06F3/0488 ,  G06N3/04 ,  G06K9/22 ,  G06N3/08

    Abstract: Techniques are provided for generating a digital image of simulated handwriting using an encoder-decoder neural network trained on images of natural handwriting samples. The simulated handwriting image can be generated based on a style of a handwriting sample and a variable length coded text input. The style represents visually distinctive characteristics of the handwriting sample, such as the shape, size, slope, and spacing of the letters, characters, or other markings in the handwriting sample. The resulting simulated handwriting image can include the text input rendered in the style of the handwriting sample. The distinctive visual appearance of the letters or words in the simulated handwriting image mimics the visual appearance of the letters or words in the handwriting sample image, whether the letters or words in the simulated handwriting image are the same as in the handwriting sample image or different from those in the handwriting sample image.

    Domain Adaptation for Machine Learning Models
    12.
    发明申请
    Domain Adaptation for Machine Learning Models 有权

    公开(公告)号：US20210334664A1

    公开(公告)日：2021-10-28

    申请号：US16865605

    申请日：2020-05-04

    Applicant: Adobe Inc.

    Inventor： Kai Li ,  Christopher Alan Tensmeyer ,  Curtis Michael Wigington ,  Handong Zhao ,  Nikolaos Barmpalios ,  Tong Sun ,  Varun Manjunatha ,  Vlad Ion Morariu

    IPC: G06N3/08 ,  G06N20/10 ,  G06K9/62 ,  G06K9/00 ,  G06F17/18

    Abstract: Adapting a machine learning model to process data that differs from training data used to configure the model for a specified objective is described. A domain adaptation system trains the model to process new domain data that differs from a training data domain by using the model to generate a feature representation for the new domain data, which describes different content types included in the new domain data. The domain adaptation system then generates a probability distribution for each discrete region of the new domain data, which describes a likelihood of the region including different content described by the feature representation. The probability distribution is compared to ground truth information for the new domain data to determine a loss function, which is used to refine model parameters. After determining that model outputs achieve a threshold similarity to the ground truth information, the model is output as a domain-agnostic model.

    GENERATING VIRTUAL OBJECTS FROM EMBEDDED CODE
    13.
    发明公开
    GENERATING VIRTUAL OBJECTS FROM EMBEDDED CODE 审中-公开

    公开(公告)号：US20240273775A1

    公开(公告)日：2024-08-15

    申请号：US18109517

    申请日：2023-02-14

    Applicant: Adobe Inc.

    Inventor： Alexa Fay Siu ,  Tong Sun ,  Mustafa Doga Dogan ,  Jennifer Anne Healey ,  Curtis Michael Wigington

    IPC: G06T11/00 ,  G06K7/14

    CPC classification number: G06T11/00 ,  G06K7/1417

    Abstract: In implementation of techniques for generating virtual objects from embedded code, a computing device implements an embedded code system to detect an embedded code included in a physical object depicted in a frame of a digital video displayed in a user interface. The physical object includes visual features, and the embedded code is not visible relative to the visual features. The embedded code system determines a virtual object property based on the embedded code. A virtual object is generated for display relative to the visual features of the physical object in the user interface based on the virtual object property.

    ENHANCED DOCUMENT VISUAL QUESTION ANSWERING SYSTEM VIA HIERARCHICAL ATTENTION
    16.
    发明公开
    ENHANCED DOCUMENT VISUAL QUESTION ANSWERING SYSTEM VIA HIERARCHICAL ATTENTION 审中-公开

    公开(公告)号：US20230153531A1

    公开(公告)日：2023-05-18

    申请号：US17528972

    申请日：2021-11-17

    Applicant: ADOBE INC.

    Inventor： Shijie Geng ,  Christopher Tensmeyer ,  Curtis Michael Wigington ,  Jiuxiang Gu

    IPC: G06F40/284 ,  G06N3/04 ,  G06F16/2452

    CPC classification number: G06F40/284 ,  G06F16/24526 ,  G06N3/04

    Abstract: Systems and methods for performing Document Visual Question Answering tasks are described. A document and query are received. The document encodes document tokens and the query encodes query tokens. The document is segmented into nested document sections, lines, and tokens. A nested structure of tokens is generated based on the segmented document. A feature vector for each token is generated. A graph structure is generated based on the nested structure of tokens. Each graph node corresponds to the query, a document section, a line, or a token. The node connections correspond to the nested structure. Each node is associated with the feature vector for the corresponding object. A graph attention network is employed to generate another embedding for each node. These embeddings are employed to identify a portion of the document that includes a response to the query. An indication of the identified portion of the document is be provided.

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