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    Multi-modal differential search with real-time focus adaptation
    1.
    发明授权
    Multi-modal differential search with real-time focus adaptation 有权

    公开(公告)号：US11604822B2

    公开(公告)日：2023-03-14

    申请号：US16426369

    申请日：2019-05-30

    Applicant: Adobe Inc.

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

    IPC: G06F7/00 ,  G06F16/583 ,  G06N3/084 ,  G06N20/00 ,  G06F16/538 ,  G06F16/532 ,  G06F16/33 ,  G06F3/04855

    Abstract: Multi-modal differential search with real-time focus adaptation 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.

    Visually guided machine-learning language model
    4.
    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.

    Visually Guided Machine-learning Language Model
    6.
    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.

    Unsupervised style and color cues for transformer-based image generation
    7.
    发明授权
    Unsupervised style and color cues for transformer-based image generation 有权

    公开(公告)号：US12277630B2

    公开(公告)日：2025-04-15

    申请号：US17662560

    申请日：2022-05-09

    Applicant: ADOBE INC.

    Inventor： Pranav Vineet Aggarwal ,  Midhun Harikumar ,  Ajinkya Gorakhnath Kale

    IPC: G06T11/40 ,  G06N3/045 ,  G06N3/088 ,  G06T7/13

    Abstract: Systems and methods for image processing are configured. Embodiments of the present disclosure identify target style attributes and target structure attributes for a composite image; generate a matrix of composite feature tokens based on the target style attributes and the target structure attributes, wherein subsequent feature tokens of the matrix of composite feature tokens are sequentially generated based on previous feature tokens of the matrix of composite feature tokens according to a linear ordering of the matrix of composite feature tokens; and generate the composite image based on the matrix of composite feature tokens, wherein the composite image includes the target style attributes and the target structure attributes.

    UPSIDE-DOWN REINFORCEMENT LEARNING FOR IMAGE GENERATION MODELS
    8.
    发明申请
    UPSIDE-DOWN REINFORCEMENT LEARNING FOR IMAGE GENERATION MODELS 有权

    公开(公告)号：US20250117967A1

    公开(公告)日：2025-04-10

    申请号：US18443590

    申请日：2024-02-16

    Applicant: ADOBE INC.

    Inventor： Ritiz Tambi ,  Purvak Lapsiya ,  Ajinkya Gorakhnath Kale ,  Pranav Vineet Aggarwal ,  Nikolaos Vlassis

    IPC: G06T11/00 ,  G06F40/166 ,  G06V10/764

    Abstract: A method, apparatus, non-transitory computer readable media, and system for image generation include obtaining an input text prompt and an indication of a level of a target characteristic, where the target characteristic comprises a characteristic used to train an image generation model. Some embodiments generate an augmented text prompt comprising the input text and an objective text corresponding to the level of the target characteristic. Some embodiments generate, using the image generation model, an image based on the augmented text prompt, where the image depicts content of the input text prompt and has the level of the target characteristic.

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