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    EXTRACTING ATTRIBUTES FROM ARBITRARY DIGITAL IMAGES UTILIZING A MULTI-ATTRIBUTE CONTRASTIVE CLASSIFICATION NEURAL NETWORK
    1.
    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.

    EXTRACTING ATTRIBUTES FROM ARBITRARY DIGITAL IMAGES UTILIZING A MULTI-ATTRIBUTE CONTRASTIVE CLASSIFICATION NEURAL NETWORK
    2.
    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：US20250022252A1

    公开(公告)日：2025-01-16

    申请号：US18899571

    申请日：2024-09-27

    Applicant: Adobe Inc.

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

    IPC: G06V10/75 ,  G06F18/214 ,  G06F18/25 ,  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.

    AUTOMATICALLY SELECTING QUERY OBJECTS IN DIGITAL IMAGES
    6.
    发明申请
    AUTOMATICALLY SELECTING QUERY OBJECTS IN DIGITAL IMAGES 有权

    公开(公告)号：US20210319255A1

    公开(公告)日：2021-10-14

    申请号：US17331161

    申请日：2021-05-26

    Applicant: Adobe Inc.

    Inventor： Khoi Pham ,  Scott Cohen ,  Zhe Lin ,  Zhihong Ding ,  Walter Wei Tuh Chang

    IPC: G06K9/62

    Abstract: The present disclosure relates to an object selection system that automatically detects and selects objects in a digital image utilizing a large-scale object detector. For instance, in response to receiving a request to automatically select a query object with an unknown object class in a digital image, the object selection system can utilize a large-scale object detector to detect potential objects in the image, filter out one or more potential objects, and label the remaining potential objects in the image to detect the query object. In some implementations, the large-scale object detector utilizes a region proposal model, a concept mask model, and an auto tagging model to automatically detect objects in the digital image.

    Utilizing a large-scale object detector to automatically select objects in digital images
    7.
    发明授权
    Utilizing a large-scale object detector to automatically select objects in digital images 有权

    公开(公告)号：US11055566B1

    公开(公告)日：2021-07-06

    申请号：US16817418

    申请日：2020-03-12

    Applicant: Adobe Inc.

    Inventor： Khoi Pham ,  Scott Cohen ,  Zhe Lin ,  Zhihong Ding ,  Walter Wei Tuh Chang

    IPC: G06K9/00 ,  G06K9/62

    Abstract: The present disclosure relates to an object selection system that automatically detects and selects objects in a digital image utilizing a large-scale object detector. For instance, in response to receiving a request to automatically select a query object with an unknown object class in a digital image, the object selection system can utilize a large-scale object detector to detect potential objects in the image, filter out one or more potential objects, and label the remaining potential objects in the image to detect the query object. In some implementations, the large-scale object detector utilizes a region proposal model, a concept mask model, and an auto tagging model to automatically detect objects in the digital image.

    Extracting attributes from arbitrary digital images utilizing a multi-attribute contrastive classification neural network
    8.
    发明授权
    Extracting attributes from arbitrary digital images utilizing a multi-attribute contrastive classification neural network 有权

    公开(公告)号：US12136250B2

    公开(公告)日：2024-11-05

    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: G06V10/75 ,  G06F18/214 ,  G06F18/25 ,  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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