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    IMAGE SEGMENTATION USING TEXT EMBEDDING
    11.
    发明申请
    IMAGE SEGMENTATION USING TEXT EMBEDDING 有权

    公开(公告)号：US20220156992A1

    公开(公告)日：2022-05-19

    申请号：US16952008

    申请日：2020-11-18

    Applicant: Adobe Inc.

    Inventor： Midhun Harikumar ,  Pranav Aggarwal ,  Baldo Faieta ,  Ajinkya Kale ,  Zhe Lin

    IPC: G06T11/60 ,  G06T7/11 ,  G06T7/162

    Abstract: A non-transitory computer-readable medium includes program code that is stored thereon. The program code is executable by one or more processing devices for performing operations including generating, by a model that includes trainable components, a learned image representation of a target image. The operations further include generating, by a text embedding model, a text embedding of a text query. The text embedding and the learned image representation of the target image are in a same embedding space. Additionally, the operations include generating a class activation map of the target image by, at least, convolving the learned image representation of the target image with the text embedding of the text query. Moreover, the operations include generating an object-segmented image using the class activation map of the target image.

    Generating novel images using sketch image representations
    12.
    发明授权
    Generating novel images using sketch image representations 有权

    公开(公告)号：US12299939B2

    公开(公告)日：2025-05-13

    申请号：US17808261

    申请日：2022-06-22

    Applicant: Adobe Inc.

    Inventor： Midhun Harikumar ,  Pranav Aggarwal ,  Ajinkya Gorakhnath Kale

    IPC: G06T7/10 ,  G06T9/00

    Abstract: Techniques for generating a novel image using tokenized image representations are disclosed. In some embodiments, a method of generating the novel image includes generating, via a first machine learning model, a first sequence of coded representations of a first image having one or more features; generating, via a second machine learning model, a second sequence of coded representations of a sketch image having one or more edge features associated with the one or more features; predicting, via a third machine learning model, one or more subsequent coded representations based on the first sequence of coded representations and the second sequence of coded representations; and based on the subsequent coded representations, generating, via the third machine learning model, a first portion of a reconstructed image having one or more image attributes of the first image, and a second portion of the reconstructed image associated with the one or more edge features.

    STYLE-BASED IMAGE GENERATION
    13.
    发明申请
    STYLE-BASED IMAGE GENERATION 有权

    公开(公告)号：US20250117973A1

    公开(公告)日：2025-04-10

    申请号：US18903151

    申请日：2024-10-01

    Applicant: ADOBE INC.

    Inventor： Fengbin Chen ,  Midhun Harikumar ,  Ajinkya Gorakhnath Kale ,  Hareesh Ravi ,  Venkata Naveen Kumar Yadav Marri

    IPC: G06T11/00

    Abstract: A method, apparatus, non-transitory computer readable medium, and system for media processing includes obtaining a text prompt and a style input, where the text prompt describes image content and the style input describes an image style, generating a text embedding based on the text prompt, where the text embedding represents the image content, generating a style embedding based on the style input, where the style embedding represents the image style, and generating a synthetic image based on the text embedding and the style embedding, where the text embedding is provided to the image generation model at a first step and the style embedding is provided to the image generation model at a second step after the first step.

    Image segmentation using text embedding
    17.
    发明授权
    Image segmentation using text embedding 有权

    公开(公告)号：US11615567B2

    公开(公告)日：2023-03-28

    申请号：US16952008

    申请日：2020-11-18

    Applicant: Adobe Inc.

    Inventor： Midhun Harikumar ,  Pranav Aggarwal ,  Baldo Faieta ,  Ajinkya Kale ,  Zhe Lin

    IPC: G06T11/60 ,  G06T7/11 ,  G06T7/162

    Abstract: A non-transitory computer-readable medium includes program code that is stored thereon. The program code is executable by one or more processing devices for performing operations including generating, by a model that includes trainable components, a learned image representation of a target image. The operations further include generating, by a text embedding model, a text embedding of a text query. The text embedding and the learned image representation of the target image are in a same embedding space. Additionally, the operations include generating a class activation map of the target image by, at least, convolving the learned image representation of the target image with the text embedding of the text query. Moreover, the operations include generating an object-segmented image using the class activation map of the target image.

    UTILIZING A DIFFUSION PRIOR NEURAL NETWORK FOR TEXT GUIDED DIGITAL IMAGE EDITING
    18.
    发明公开
    UTILIZING A DIFFUSION PRIOR NEURAL NETWORK FOR TEXT GUIDED DIGITAL IMAGE EDITING 审中-公开

    公开(公告)号：US20240362842A1

    公开(公告)日：2024-10-31

    申请号：US18308017

    申请日：2023-04-27

    Applicant: Adobe Inc.

    Inventor： Hareesh Ravi ,  Sachin Kelkar ,  Midhun Harikumar ,  Ajinkya Gorakhnath Kale

    IPC: G06T11/60 ,  G06T5/00

    CPC classification number: G06T11/60 ,  G06T5/70 ,  G06T2200/24 ,  G06T2207/20084

    Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for utilizing a diffusion prior neural network for text guided digital image editing. For example, in one or more embodiments the disclosed systems utilize a text-image encoder to generate a base image embedding from the base digital image and an edit text embedding from edit text. Moreover, the disclosed systems utilize a diffusion prior neural network to generate a text-image embedding. In particular, the disclosed systems inject the base image embedding at a conceptual editing step of the diffusion prior neural network and condition a set of steps of the diffusion prior neural network after the conceptual editing step utilizing the edit text embedding. Furthermore, the disclosed systems utilize a diffusion neural network to create a modified digital image from the text-edited image embedding and the base image embedding.

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