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    MULTI-MODULE AND MULTI-TASK MACHINE LEARNING SYSTEM BASED ON AN ENSEMBLE OF DATASETS
    4.
    发明申请
    MULTI-MODULE AND MULTI-TASK MACHINE LEARNING SYSTEM BASED ON AN ENSEMBLE OF DATASETS 审中-公开

    公开(公告)号：US20200349464A1

    公开(公告)日：2020-11-05

    申请号：US16401548

    申请日：2019-05-02

    Applicant: Adobe Inc.

    Inventor： Zhe Lin ,  Trung Huu Bui ,  Scott Cohen ,  Mingyang Ling ,  Chenyun Wu

    IPC: G06N20/00

    Abstract: Techniques and systems are provided for training a machine learning model using different datasets to perform one or more tasks. The machine learning model can include a first sub-module configured to perform a first task and a second sub-module configured to perform a second task. The first sub-module can be selected for training using a first training dataset based on a format of the first training dataset. The first sub-module can then be trained using the first training dataset to perform the first task. The second sub-module can be selected for training using a second training dataset based on a format of the second training dataset. The second sub-module can then be trained using the second training dataset to perform the second task.

    Learning copy space using regression and segmentation neural networks
    5.
    发明授权
    Learning copy space using regression and segmentation neural networks 有权

    公开(公告)号：US11605168B2

    公开(公告)日：2023-03-14

    申请号：US17215067

    申请日：2021-03-29

    Applicant: Adobe Inc.

    Inventor： Mingyang Ling ,  Alex Filipkowski ,  Zhe Lin ,  Jianming Zhang ,  Samarth Gulati

    IPC: G06K9/62 ,  G06T7/11 ,  G06T7/136 ,  G06T7/143 ,  G06T7/174 ,  G06F18/214 ,  G06N3/045 ,  G06V10/25 ,  G06V10/764 ,  G06V10/82 ,  G06V10/26

    Abstract: Techniques are disclosed for characterizing and defining the location of a copy space in an image. A methodology implementing the techniques according to an embodiment includes applying a regression convolutional neural network (CNN) to an image. The regression CNN is configured to predict properties of the copy space such as size and type (natural or manufactured). The prediction is conditioned on a determination of the presence of the copy space in the image. The method further includes applying a segmentation CNN to the image. The segmentation CNN is configured to generate one or more pixel-level masks to define the location of copy spaces in the image, whether natural or manufactured, or to define the location of a background region of the image. The segmentation CNN may include a first stage comprising convolutional layers and a second stage comprising pairs of boundary refinement layers and bilinear up-sampling layers.

    Classifying colors of objects in digital images
    6.
    发明授权
    Classifying colors of objects in digital images 有权

    公开(公告)号：US11302033B2

    公开(公告)日：2022-04-12

    申请号：US16518795

    申请日：2019-07-22

    Applicant: Adobe Inc.

    Inventor： Zhihong Ding ,  Scott Cohen ,  Zhe Lin ,  Mingyang Ling

    IPC: G06T7/90 ,  G06K9/32 ,  G06K9/62 ,  G06N3/02 ,  G06V10/20

    Abstract: The present disclosure relates to a color classification system that accurately classifies objects in digital images based on color. In particular, in one or more embodiments, the color classification system utilizes a multidimensional color space and one or more color mappings to match objects to colors. Indeed, the color classification system can accurately and efficiently detect the color of an object utilizing one or more color similarity regions generated in the multidimensional color space.

    CLASSIFYING COLORS OF OBJECTS IN DIGITAL IMAGES
    8.
    发明申请
    CLASSIFYING COLORS OF OBJECTS IN DIGITAL IMAGES 有权

    公开(公告)号：US20210027497A1

    公开(公告)日：2021-01-28

    申请号：US16518795

    申请日：2019-07-22

    Applicant: Adobe Inc.

    Inventor： Zhihong Ding ,  Scott Cohen ,  Zhe Lin ,  Mingyang Ling

    IPC: G06T7/90 ,  G06K9/32 ,  G06K9/62 ,  G06N3/02

    Abstract: The present disclosure relates to a color classification system that accurately classifies objects in digital images based on color. In particular, in one or more embodiments, the color classification system utilizes a multidimensional color space and one or more color mappings to match objects to colors. Indeed, the color classification system can accurately and efficiently detect the color of an object utilizing one or more color similarity regions generated in the multidimensional color space.

    Object Detection In Images
    9.
    发明申请
    Object Detection In Images 审中-公开

    公开(公告)号：US20200151448A1

    公开(公告)日：2020-05-14

    申请号：US16189805

    申请日：2018-11-13

    Applicant: Adobe Inc.

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

    IPC: G06K9/00 ,  G06K9/62 ,  G06N3/04

    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.

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