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    TRANSFERRING STYLES TO DIGITAL IMAGES IN AN OBJECT-AWARE MANNER
    1.
    发明申请
    TRANSFERRING STYLES TO DIGITAL IMAGES IN AN OBJECT-AWARE MANNER 有权

    公开(公告)号：US20250069297A1

    公开(公告)日：2025-02-27

    申请号：US18948839

    申请日：2024-11-15

    Applicant: Adobe Inc.

    Inventor： Zhifei Zhang ,  Zhe Lin ,  Scott Cohen ,  Darshan Prasad ,  Zhihong Ding

    IPC: G06T11/40 ,  G06T5/77 ,  G06T7/12

    Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for transferring global style features between digital images utilizing one or more machine learning models or neural networks. In particular, in one or more embodiments, the disclosed systems receive a request to transfer a global style from a source digital image to a target digital image, identify at least one target object within the target digital image, and transfer the global style from the source digital image to the target digital image while maintaining an object style of the at least one target object.

    SELECTION OF AREAS OF DIGITAL IMAGES
    2.
    发明申请
    SELECTION OF AREAS OF DIGITAL IMAGES 有权

    公开(公告)号：US20250061626A1

    公开(公告)日：2025-02-20

    申请号：US18674518

    申请日：2024-05-24

    Applicant: Adobe Inc.

    Inventor： Xiaoyang Liu ,  Zhe Lin ,  Yuqian Zhou ,  Sohrab Amirghodsi ,  Sarah Jane Stuckey ,  Sakshi Gupta ,  Guotong Feng ,  Elya Schechtman ,  Connelly Stuart Barnes ,  Betty Leong

    IPC: G06T11/60 ,  G06T5/77 ,  G06T11/20 ,  G06V10/764

    Abstract: Techniques for performing a digital operation on a digital image are described along with methods and systems employing such techniques. According to the techniques, an input (e.g., an input stroke) is received by, for example, a processing system. Based upon the input, an area of the digital image upon which a digital operation (e.g., for removal of a distractor within the area) is to be performed is determined. In an implementation, one or more metrics of an input stroke are analyzed, typically in real time, to at least partially determine the area upon which the digital operation is to be performed. In an additional or alternative implementation, the input includes a first point, a second point and a connector, and the area is at least partially determined by a location of the first point relative to a location of the second point and/or by locations of the first point and/or second point relative to one or more edges of the digital image.

    GENERATING COLOR-EDITED DIGITAL IMAGES UTILIZING A CONTENT AWARE DIFFUSION NEURAL NETWORK
    3.
    发明申请
    GENERATING COLOR-EDITED DIGITAL IMAGES UTILIZING A CONTENT AWARE DIFFUSION NEURAL NETWORK 有权

    公开(公告)号：US20250046055A1

    公开(公告)日：2025-02-06

    申请号：US18363980

    申请日：2023-08-02

    Applicant: Adobe Inc.

    Inventor： Zhifei Zhang ,  Zhe Lin ,  Yixuan Ren ,  Yifei Fan ,  Jing Shi

    IPC: G06V10/56 ,  G06T5/00 ,  G06V10/60 ,  G06V10/82

    Abstract: This disclosure describes one or more implementations of systems, non-transitory computer-readable media, and methods that trains (and utilizes) an image color editing diffusion neural network to generate a color edited digital image(s) for a digital image. In particular, in one or more implementations, the disclosed systems identify a digital image depicting content in a first color style. Moreover, the disclosed systems generate, from the digital image utilizing an image color editing diffusion neural network, a color-edited digital image depicting the content in a second color style different from the first color style. Further, the disclosed systems provide, for display within a graphical user interface, the color-edited digital image.

    EFFICIENT VISION-LANGUAGE RETRIEVAL USING STRUCTURAL PRUNING
    4.
    发明申请
    EFFICIENT VISION-LANGUAGE RETRIEVAL USING STRUCTURAL PRUNING 有权

    公开(公告)号：US20250013866A1

    公开(公告)日：2025-01-09

    申请号：US18347877

    申请日：2023-07-06

    Applicant: ADOBE INC.

    Inventor： Handong Zhao ,  Yue Bai ,  Zhe Lin ,  Ajinkya Gorakhnath Kale ,  Jiuxiang Gu ,  Tong Yu ,  Sungchul Kim

    IPC: G06N3/082 ,  G06N3/04

    Abstract: Systems and methods for reducing inference time of vision-language models, as well as for multimodal search, are described herein. Embodiments are configured to obtain an embedding neural network. The embedding neural network is pretrained to embed inputs from a plurality of modalities into a multimodal embedding space. Embodiments are further configured to perform a first progressive pruning stage, where the first progressive pruning stage includes a first pruning of the embedding neural network and a first fine-tuning of the embedding neural network. Embodiments then perform a second progressive pruning stage based on an output of the first progressive pruning stage, where the second progressive pruning stage includes a second pruning of the embedding neural network and a second fine-tuning of the embedding neural network.

    Harmonizing composite images utilizing a transformer neural network
    5.
    发明授权
    Harmonizing composite images utilizing a transformer neural network 有权

    公开(公告)号：US12165284B2

    公开(公告)日：2024-12-10

    申请号：US17655663

    申请日：2022-03-21

    Applicant: Adobe Inc.

    Inventor： He Zhang ,  Jianming Zhang ,  Jose Ignacio Echevarria Vallespi ,  Kalyan Sunkavalli ,  Meredith Payne Stotzner ,  Yinglan Ma ,  Zhe Lin ,  Elya Shechtman ,  Frederick Mandia

    IPC: G06V10/00 ,  G06T5/50 ,  G06T7/194 ,  G06T7/90 ,  G06T11/00

    Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods that implement a dual-branched neural network architecture to harmonize composite images. For example, in one or more implementations, the transformer-based harmonization system uses a convolutional branch and a transformer branch to generate a harmonized composite image based on an input composite image and a corresponding segmentation mask. More particularly, the convolutional branch comprises a series of convolutional neural network layers followed by a style normalization layer to extract localized information from the input composite image. Further, the transformer branch comprises a series of transformer neural network layers to extract global information based on different resolutions of the input composite image. Utilizing a decoder, the transformer-based harmonization system combines the local information and the global information from the corresponding convolutional branch and transformer branch to generate a harmonized composite image.

    Media enhancement using discriminative and generative models with feedback
    6.
    发明授权
    Media enhancement using discriminative and generative models with feedback 有权

    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).

    INPAINTING DIGITAL IMAGES USING A HYBRID WIRE REMOVAL PIPELINE
    9.
    发明公开
    INPAINTING DIGITAL IMAGES USING A HYBRID WIRE REMOVAL PIPELINE 审中-公开

    公开(公告)号：US20240303787A1

    公开(公告)日：2024-09-12

    申请号：US18179855

    申请日：2023-03-07

    Applicant: Adobe Inc.

    Inventor： Yuqian Zhou ,  Connelly Barnes ,  Zijun Wei ,  Zhe Lin ,  Elya Shechtman ,  Sohrab Amirghodsi ,  Xiaoyang Liu

    IPC: G06T5/00 ,  G06T7/11 ,  G06V20/10

    CPC classification number: G06T5/77 ,  G06T7/11 ,  G06V20/176 ,  G06T2207/20021 ,  G06T2207/30184

    Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for inpainting a digital image using a hybrid wire removal pipeline. For example, the disclosed systems use a hybrid wire removal pipeline that integrates multiple machine learning models, such as a wire segmentation model, a hole separation model, a mask dilation model, a patch-based inpainting model, and a deep inpainting model. Using the hybrid wire removal pipeline, in some embodiments, the disclosed systems generate a wire segmentation from a digital image depicting one or more wires. The disclosed systems also utilize the hybrid wire removal pipeline to extract or identify portions of the wire segmentation that indicate specific wires or portions of wires. In certain embodiments, the disclosed systems further inpaint pixels of the digital image corresponding to the wires indicated by the wire segmentation mask using the patch-based inpainting model and/or the deep inpainting model.

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