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    Text-to-Visual Machine Learning Embedding Techniques
    21.
    发明申请
    Text-to-Visual Machine Learning Embedding Techniques 审中-公开

    公开(公告)号：US20200380298A1

    公开(公告)日：2020-12-03

    申请号：US16426264

    申请日：2019-05-30

    Applicant: Adobe Inc.

    Inventor： Pranav Vineet Aggarwal ,  Zhe Lin ,  Baldo Antonio Faieta ,  Saeid Motiian

    IPC: G06K9/62 ,  G06K9/72 ,  G06F16/535 ,  G06N20/00

    Abstract: Text-to-visual machine learning embedding techniques are described that overcome the challenges of conventional techniques in a variety of ways. These techniques include use of query-based training data which may expand availability and types of training data usable to train a model. Generation of negative digital image samples is also described that may increase accuracy in training the model using machine learning. A loss function is also described that also supports increased accuracy and computational efficiency by losses separately, e.g., between positive or negative sample embeddings a text embedding.

    Visually guided machine-learning language model
    24.
    发明授权
    Visually guided machine-learning language model 有权

    公开(公告)号：US11605019B2

    公开(公告)日：2023-03-14

    申请号：US16426298

    申请日：2019-05-30

    Applicant: Adobe Inc.

    Inventor： Pranav Vineet Aggarwal ,  Zhe Lin ,  Baldo Antonio Faieta ,  Saeid Motiian

    IPC: G06K9/00 ,  G06N20/00 ,  G06K9/62 ,  G06N3/08 ,  G06F16/538 ,  G10L15/22 ,  G06F40/30

    Abstract: Visually guided machine-learning language model and embedding techniques are described that overcome the challenges of conventional techniques in a variety of ways. In one example, a model is trained to support a visually guided machine-learning embedding space that supports visual intuition as to “what” is represented by text. The visually guided language embedding space supported by the model, once trained, may then be used to support visual intuition as part of a variety of functionality. In one such example, the visually guided language embedding space as implemented by the model may be leveraged as part of a multi-modal differential search to support search of digital images and other digital content with real-time focus adaptation which overcomes the challenges of conventional techniques.

    TEXTUAL DESIGN AGENT
    25.
    发明申请
    TEXTUAL DESIGN AGENT 有权

    公开(公告)号：US20230070390A1

    公开(公告)日：2023-03-09

    申请号：US17466679

    申请日：2021-09-03

    Applicant: ADOBE INC.

    Inventor： Zhaowen Weng ,  Saeid Motiian ,  Baldo Faieta ,  Zegi Gu ,  Peter Evan O'Donovan ,  Alex Filipkowski ,  Jose Ignacio Echevarria Vallespi

    IPC: G06F40/109 ,  G06N3/04 ,  G06F40/166

    Abstract: Embodiments of the technology described herein, are an intelligent system that aims to expedite a text design process by providing text design predictions interactively. The system works with a typical text design scenario comprising a background image and one or more text strings as input. In the design scenario, the text string is to be placed on top of the background. The textual design agent may include a location recommendation model that recommends a location on the background image to place the text. The textual design agent may also include a font recommendation model, a size recommendation model, and a color recommendation model. The output of these four models may be combined to generate draft designs that are evaluated as a whole (combination of color, font, and size) for the best designs. The top designs may be output to the user.

    Visually Guided Machine-learning Language Model
    30.
    发明申请
    Visually Guided Machine-learning Language Model 审中-公开

    公开(公告)号：US20200380403A1

    公开(公告)日：2020-12-03

    申请号：US16426298

    申请日：2019-05-30

    Applicant: Adobe Inc.

    Inventor： Pranav Vineet Aggarwal ,  Zhe Lin ,  Baldo Antonio Faieta ,  Saeid Motiian

    IPC: G06N20/00 ,  G06K9/62 ,  G06F16/538 ,  G06N3/08

    Abstract: Visually guided machine-learning language model and embedding techniques are described that overcome the challenges of conventional techniques in a variety of ways. In one example, a model is trained to support a visually guided machine-learning embedding space that supports visual intuition as to “what” is represented by text. The visually guided language embedding space supported by the model, once trained, may then be used to support visual intuition as part of a variety of functionality. In one such example, the visually guided language embedding space as implemented by the model may be leveraged as part of a multi-modal differential search to support search of digital images and other digital content with real-time focus adaptation which overcomes the challenges of conventional techniques.

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