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    Wire segmentation for images using machine learning
    1.
    发明授权
    Wire segmentation for images using machine learning 有权

    公开(公告)号：US12271804B2

    公开(公告)日：2025-04-08

    申请号：US17870496

    申请日：2022-07-21

    Applicant: Adobe Inc.

    Inventor： Mang Tik Chiu ,  Connelly Barnes ,  Zijun Wei ,  Zhe Lin ,  Yuqian Zhou ,  Xuaner Zhang ,  Sohrab Amirghodsi ,  Florian Kainz ,  Elya Shechtman

    IPC: G06N3/045 ,  G06T3/40 ,  G06T5/30 ,  G06T5/77 ,  G06T7/11 ,  G06T7/143 ,  G06T7/187 ,  G06T7/62

    Abstract: Embodiments are disclosed for performing wire segmentation of images using machine learning. In particular, in one or more embodiments, the disclosed systems and methods comprise receiving an input image, generating, by a first trained neural network model, a global probability map representation of the input image indicating a probability value of each pixel including a representation of wires, and identifying regions of the input image indicated as including the representation of wires. The disclosed systems and methods further comprise, for each region from the identified regions, concatenating the region and information from the global probability map to create a concatenated input, and generating, by a second trained neural network model, a local probability map representation of the region based on the concatenated input, indicating pixels of the region including representations of wires. The disclosed systems and methods further comprise aggregating local probability maps for each region.

    PANOPTICALLY GUIDED INPAINTING UTILIZING A PANOPTIC INPAINTING NEURAL NETWORK
    6.
    发明公开
    PANOPTICALLY GUIDED INPAINTING UTILIZING A PANOPTIC INPAINTING NEURAL NETWORK 审中-公开

    公开(公告)号：US20240127410A1

    公开(公告)日：2024-04-18

    申请号：US17937695

    申请日：2022-10-03

    Applicant: Adobe Inc.

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

    IPC: G06T5/00 ,  G06T7/11

    CPC classification number: G06T5/005 ,  G06T7/11 ,  G06T2207/20084

    Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for panoptically guiding digital image inpainting utilizing a panoptic inpainting neural network. In some embodiments, the disclosed systems utilize a panoptic inpainting neural network to generate an inpainted digital image according to panoptic segmentation map that defines pixel regions corresponding to different panoptic labels. In some cases, the disclosed systems train a neural network utilizing a semantic discriminator that facilitates generation of digital images that are realistic while also conforming to a semantic segmentation. The disclosed systems generate and provide a panoptic inpainting interface to facilitate user interaction for inpainting digital images. In certain embodiments, the disclosed systems iteratively update an inpainted digital image based on changes to a panoptic segmentation map.

    OBJECT CLASS INPAINTING IN DIGITAL IMAGES UTILIZING CLASS-SPECIFIC INPAINTING NEURAL NETWORKS
    9.
    发明公开
    OBJECT CLASS INPAINTING IN DIGITAL IMAGES UTILIZING CLASS-SPECIFIC INPAINTING NEURAL NETWORKS 审中-公开

    公开(公告)号：US20230368339A1

    公开(公告)日：2023-11-16

    申请号：US17663317

    申请日：2022-05-13

    Applicant: Adobe Inc.

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

    IPC: G06T5/00 ,  G06T7/11 ,  G06N3/04

    CPC classification number: G06T5/005 ,  G06T7/11 ,  G06N3/04 ,  G06T2207/20081 ,  G06T2207/20084

    Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media that generate inpainted digital images utilizing class-specific cascaded modulation inpainting neural network. For example, the disclosed systems utilize a class-specific cascaded modulation inpainting neural network that includes cascaded modulation decoder layers to generate replacement pixels portraying a particular target object class. To illustrate, in response to user selection of a replacement region and target object class, the disclosed systems utilize a class-specific cascaded modulation inpainting neural network corresponding to the target object class to generate an inpainted digital image that portrays an instance of the target object class within the replacement region. Moreover, in one or more embodiments the disclosed systems train class-specific cascaded modulation inpainting neural networks corresponding to a variety of target object classes, such as a sky object class, a water object class, a ground object class, or a human object class.

    Labeling techniques for a modified panoptic labeling neural network
    10.
    发明授权
    Labeling techniques for a modified panoptic labeling neural network 有权

    公开(公告)号：US11507777B2

    公开(公告)日：2022-11-22

    申请号：US15930539

    申请日：2020-05-13

    Applicant: Adobe Inc.

    Inventor： Sohrab Amirghodsi ,  Zhe Lin ,  Yilin Wang ,  Tianshu Yu ,  Connelly Barnes ,  Elya Shechtman

    IPC: G06K9/62 ,  G06V10/75 ,  G06F17/18 ,  G06N3/08 ,  G06N20/00

    Abstract: A panoptic labeling system includes a modified panoptic labeling neural network (“modified PLNN”) that is trained to generate labels for pixels in an input image. The panoptic labeling system generates modified training images by combining training images with mask instances from annotated images. The modified PLNN determines a set of labels representing categories of objects depicted in the modified training images. The modified PLNN also determines a subset of the labels representing categories of objects depicted in the input image. For each mask pixel in a modified training image, the modified PLNN calculates a probability indicating whether the mask pixel has the same label as an object pixel. The modified PLNN generates a mask label for each mask pixel, based on the probability. The panoptic labeling system provides the mask label to, for example, a digital graphics editing system that uses the labels to complete an infill operation.

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