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    Multi-source panoptic feature pyramid network
    1.
    发明授权
    Multi-source panoptic feature pyramid network 有权

    公开(公告)号：US11941884B2

    公开(公告)日：2024-03-26

    申请号：US17454740

    申请日：2021-11-12

    Applicant: ADOBE INC.

    Inventor： Jason Wen Yong Kuen ,  Bo Sun ,  Zhe Lin ,  Simon Su Chen

    IPC: G06K9/00 ,  G06F18/21 ,  G06N3/08 ,  G06T9/00 ,  G06V10/75 ,  G06V20/40

    CPC classification number: G06V20/41 ,  G06F18/2163 ,  G06N3/08 ,  G06T3/4046 ,  G06T9/002 ,  G06V10/751

    Abstract: Systems and methods for image processing are described. Embodiments of the present disclosure receive an image having a plurality of object instances; encode the image to obtain image features; decode the image features to obtain object features; generate object detection information based on the object features using an object detection branch, wherein the object detection branch is trained based on a first training set using a detection loss; generate semantic segmentation information based on the object features using a semantic segmentation branch, wherein the semantic segmentation branch is trained based on a second training set different from the first training set using a semantic segmentation loss; and combine the object detection information and the semantic segmentation information to obtain panoptic segmentation information that indicates which pixels of the image correspond to each of the plurality of object instances.

    Detecting digital objects and generating object masks on device
    4.
    发明授权
    Detecting digital objects and generating object masks on device 有权

    公开(公告)号：US12272127B2

    公开(公告)日：2025-04-08

    申请号：US17589114

    申请日：2022-01-31

    Applicant: Adobe Inc.

    Inventor： Jason Wen Yong Kuen ,  Su Chen ,  Scott Cohen ,  Zhe Lin ,  Zijun Wei ,  Jianming Zhang

    IPC: G06V10/82 ,  G06N3/08 ,  G06T7/00

    Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that generates object masks for digital objects portrayed in digital images utilizing a detection-masking neural network pipeline. In particular, in one or more embodiments, the disclosed systems utilize detection heads of a neural network to detect digital objects portrayed within a digital image. In some cases, each detection head is associated with one or more digital object classes that are not associated with the other detection heads. Further, in some cases, the detection heads implement multi-scale synchronized batch normalization to normalize feature maps across various feature levels. The disclosed systems further utilize a masking head of the neural network to generate one or more object masks for the detected digital objects. In some cases, the disclosed systems utilize post-processing techniques to filter out low-quality masks.

    SEMANTIC IMAGE SYNTHESIS
    5.
    发明申请
    SEMANTIC IMAGE SYNTHESIS 有权

    公开(公告)号：US20250086849A1

    公开(公告)日：2025-03-13

    申请号：US18463333

    申请日：2023-09-08

    Applicant: ADOBE INC.

    Inventor： Yu Zeng ,  Zhe Lin ,  Jianming Zhang ,  Qing Liu ,  Jason Wen Yong Kuen ,  John Philip Collomosse

    IPC: G06T11/00 ,  G06F40/295 ,  G06F40/30 ,  G06V10/774 ,  G06V10/776 ,  G06V20/70

    Abstract: Embodiments of the present disclosure include obtaining a text prompt describing an element, layout information indicating a target region for the element, and a precision level corresponding to the element. Some embodiments generate a text feature pyramid based on the text prompt, the layout information, and the precision level, wherein the text feature pyramid comprises a plurality of text feature maps at a plurality of scales, respectively. Then, an image is generated based on the text feature pyramid. In some cases, the image includes an object corresponding to the element of the text prompt at the target region. Additionally, a shape of the object corresponds to a shape of the target region based on the precision level.

    Object Detection In Images
    8.
    发明申请
    Object Detection In Images 有权

    公开(公告)号：US20220157054A1

    公开(公告)日：2022-05-19

    申请号：US17588516

    申请日：2022-01-31

    Applicant: Adobe Inc.

    Inventor： Zhe Lin ,  Xiaohui Shen ,  Mingyang Ling ,  Jianming Zhang ,  Jason Wen Yong Kuen

    IPC: G06V20/20 ,  G06K9/62 ,  G06N3/04 ,  G06V20/64

    Abstract: In implementations of object detection in images, object detectors are trained using heterogeneous training datasets. A first training dataset is used to train an image tagging network to determine an attention map of an input image for a target concept. A second training dataset is used to train a conditional detection network that accepts as conditional inputs the attention map and a word embedding of the target concept. Despite the conditional detection network being trained with a training dataset having a small number of seen classes (e.g., classes in a training dataset), it generalizes to novel, unseen classes by concept conditioning, since the target concept propagates through the conditional detection network via the conditional inputs, thus influencing classification and region proposal. Hence, classes of objects that can be detected are expanded, without the need to scale training databases to include additional classes.

    MULTI-SOURCE PANOPTIC FEATURE PYRAMID NETWORK
    9.
    发明公开
    MULTI-SOURCE PANOPTIC FEATURE PYRAMID NETWORK 审中-公开

    公开(公告)号：US20230154185A1

    公开(公告)日：2023-05-18

    申请号：US17454740

    申请日：2021-11-12

    Applicant: ADOBE INC.

    Inventor： Jason Wen Yong Kuen ,  Bo Sun ,  Zhe Lin ,  Simon Su Chen

    IPC: G06K9/00 ,  G06K9/62 ,  G06T3/40 ,  G06T9/00 ,  G06N3/08

    CPC classification number: G06K9/00624 ,  G06K9/6202 ,  G06K9/6261 ,  G06N3/08 ,  G06T3/4046 ,  G06T9/002

    Abstract: Systems and methods for image processing are described. Embodiments of the present disclosure receive an image having a plurality of object instances; encode the image to obtain image features; decode the image features to obtain object features; generate object detection information based on the object features using an object detection branch, wherein the object detection branch is trained based on a first training set using a detection loss; generate semantic segmentation information based on the object features using a semantic segmentation branch, wherein the semantic segmentation branch is trained based on a second training set different from the first training set using a semantic segmentation loss; and combine the object detection information and the semantic segmentation information to obtain panoptic segmentation information that indicates which pixels of the image correspond to each of the plurality of object instances.

    DETECTING DIGITAL OBJECTS AND GENERATING OBJECT MASKS ON DEVICE
    10.
    发明申请
    DETECTING DIGITAL OBJECTS AND GENERATING OBJECT MASKS ON DEVICE 有权

    公开(公告)号：US20230128792A1

    公开(公告)日：2023-04-27

    申请号：US17589114

    申请日：2022-01-31

    Applicant: Adobe Inc.

    Inventor： Jason Wen Yong Kuen ,  Su Chen ,  Scott Cohen ,  Zhe Lin ,  Zijun Wei ,  Jianming Zhang

    IPC: G06V10/82 ,  G06N3/08 ,  G06T7/00

    Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that generates object masks for digital objects portrayed in digital images utilizing a detection-masking neural network pipeline. In particular, in one or more embodiments, the disclosed systems utilize detection heads of a neural network to detect digital objects portrayed within a digital image. In some cases, each detection head is associated with one or more digital object classes that are not associated with the other detection heads. Further, in some cases, the detection heads implement multi-scale synchronized batch normalization to normalize feature maps across various feature levels. The disclosed systems further utilize a masking head of the neural network to generate one or more object masks for the detected digital objects. In some cases, the disclosed systems utilize post-processing techniques to filter out low-quality masks.

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