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    DIGITAL IMAGE INPAINTING UTILIZING A CASCADED MODULATION INPAINTING NEURAL NETWORK
    52.
    发明公开
    DIGITAL IMAGE INPAINTING UTILIZING A CASCADED MODULATION INPAINTING NEURAL NETWORK 审中-公开

    公开(公告)号：US20230360180A1

    公开(公告)日：2023-11-09

    申请号：US17661985

    申请日：2022-05-04

    Applicant: Adobe Inc.

    Inventor： Haitian Zheng ,  Zhe Lin ,  Jingwan Lu ,  Scott Cohen ,  Elya Shechtman ,  Connelly Barnes ,  Jianming Zhang ,  Ning Xu ,  Sohrab Amirghodsi

    IPC: G06V10/40 ,  G06T3/40 ,  G06T5/00

    CPC classification number: G06T5/005 ,  G06T3/4046 ,  G06V10/40 ,  G06T2207/20084

    Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media that generate inpainted digital images utilizing a cascaded modulation inpainting neural network. For example, the disclosed systems utilize a cascaded modulation inpainting neural network that includes cascaded modulation decoder layers. For example, in one or more decoder layers, the disclosed systems start with global code modulation that captures the global-range image structures followed by an additional modulation that refines the global predictions. Accordingly, in one or more implementations, the image inpainting system provides a mechanism to correct distorted local details. Furthermore, in one or more implementations, the image inpainting system leverages fast Fourier convolutions block within different resolution layers of the encoder architecture to expand the receptive field of the encoder and to allow the network encoder to better capture global structure.

    RECOMMENDING OBJECTS FOR IMAGE COMPOSITION USING A GEOMETRY-AND-LIGHTING AWARE NEURAL NETWORK
    54.
    发明公开
    RECOMMENDING OBJECTS FOR IMAGE COMPOSITION USING A GEOMETRY-AND-LIGHTING AWARE NEURAL NETWORK 审中-公开

    公开(公告)号：US20230325991A1

    公开(公告)日：2023-10-12

    申请号：US17658770

    申请日：2022-04-11

    Applicant: Adobe Inc.

    Inventor： Zhe Lin ,  Sijie Zhu ,  Jason Wen Yong Kuen ,  Scott Cohen ,  Zhifei Zhang

    IPC: G06T5/50 ,  G06T7/194 ,  G06T5/00 ,  G06T3/60

    CPC classification number: G06T5/50 ,  G06T7/194 ,  G06T5/002 ,  G06T3/60 ,  G06T2207/20084 ,  G06T2207/20221

    Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media that utilizes artificial intelligence to learn to recommend foreground object images for use in generating composite images based on geometry and/or lighting features. For instance, in one or more embodiments, the disclosed systems transform a foreground object image corresponding to a background image using at least one of a geometry transformation or a lighting transformation. The disclosed systems further generating predicted embeddings for the background image, the foreground object image, and the transformed foreground object image within a geometry-lighting-sensitive embedding space utilizing a geometry-lighting-aware neural network. Using a loss determined from the predicted embeddings, the disclosed systems update parameters of the geometry-lighting-aware neural network. The disclosed systems further provide a variety of efficient user interfaces for generating composite digital images.

    GENERATING OBJECT MASKS OF OBJECT PARTS UTLIZING DEEP LEARNING
    56.
    发明申请
    GENERATING OBJECT MASKS OF OBJECT PARTS UTLIZING DEEP LEARNING 有权

    公开(公告)号：US20230136913A1

    公开(公告)日：2023-05-04

    申请号：US18147278

    申请日：2022-12-28

    Applicant: Adobe Inc.

    Inventor： Yinan Zhao ,  Brian Price ,  Scott Cohen

    IPC: G06T7/11 ,  G06T7/168 ,  G06T9/00 ,  G06T5/00 ,  G06T7/181

    Abstract: The present disclosure relates to a class-agnostic object segmentation system that automatically detects, segments, and selects objects within digital images irrespective of object semantic classifications. For example, the object segmentation system utilizes a class-agnostic object segmentation neural network to segment each pixel in a digital image into an object mask. Further, in response to detecting a selection request of a target object, the object segmentation system utilizes a corresponding object mask to automatically select the target object within the digital image. In some implementations, the object segmentation system utilizes a class-agnostic object segmentation neural network to detect and automatically select a partial object in the digital image in response to a target object selection request.

    EXTRACTING ATTRIBUTES FROM ARBITRARY DIGITAL IMAGES UTILIZING A MULTI-ATTRIBUTE CONTRASTIVE CLASSIFICATION NEURAL NETWORK
    59.
    发明申请
    EXTRACTING ATTRIBUTES FROM ARBITRARY DIGITAL IMAGES UTILIZING A MULTI-ATTRIBUTE CONTRASTIVE CLASSIFICATION NEURAL NETWORK 有权

    公开(公告)号：US20220383037A1

    公开(公告)日：2022-12-01

    申请号：US17332734

    申请日：2021-05-27

    Applicant: Adobe Inc.

    Inventor： Khoi Pham ,  Kushal Kafle ,  Zhe Lin ,  Zhihong Ding ,  Scott Cohen ,  Quan Tran

    IPC: G06K9/62 ,  G06N3/08

    Abstract: This disclosure describes one or more implementations of systems, non-transitory computer-readable media, and methods that extract multiple attributes from an object portrayed in a digital image utilizing a multi-attribute contrastive classification neural network. For example, the disclosed systems utilize a multi-attribute contrastive classification neural network that includes an embedding neural network, a localizer neural network, a multi-attention neural network, and a classifier neural network. In some cases, the disclosed systems train the multi-attribute contrastive classification neural network utilizing a multi-attribute, supervised-contrastive loss. In some embodiments, the disclosed systems generate negative attribute training labels for labeled digital images utilizing positive attribute labels that correspond to the labeled digital images.

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