IMAGE LANDMARK DETECTION
    1.
    发明申请

    公开(公告)号:US20210192198A1

    公开(公告)日:2021-06-24

    申请号:US17138177

    申请日:2020-12-30

    Applicant: Snap Inc.

    Abstract: A landmark detection system can more accurately detect landmarks in images using a detection scheme that penalizes for dispersion parameters, such as variance or scale. The landmark detection system can be trained using both labeled and unlabeled training data in a semi-supervised approach. The landmark detection system can further implement tracking of an object across multiple images using landmark data.

    Image landmark detection
    3.
    发明授权

    公开(公告)号:US10909357B1

    公开(公告)日:2021-02-02

    申请号:US16277710

    申请日:2019-02-15

    Applicant: Snap Inc.

    Abstract: A landmark detection system can more accurately detect landmarks in images using a detection scheme that penalizes for dispersion parameters, such as variance or scale. The landmark detection system can be trained using both labeled and unlabeled training data in a semi-supervised approach. The landmark detection system can further implement tracking of an object across multiple images using landmark data.

    IMAGE LANDMARK DETECTION
    5.
    发明申请

    公开(公告)号:US20220292866A1

    公开(公告)日:2022-09-15

    申请号:US17829644

    申请日:2022-06-01

    Applicant: Snap Inc.

    Abstract: A landmark detection system can more accurately detect landmarks in images using a detection scheme that penalizes for dispersion parameters, such as variance or scale. The landmark detection system can be trained using both labeled and unlabeled training data in a semi-supervised approach. The landmark detection system can further implement tracking of an object across multiple images using landmark data.

    Image landmark detection
    6.
    发明授权

    公开(公告)号:US11354922B2

    公开(公告)日:2022-06-07

    申请号:US17138177

    申请日:2020-12-30

    Applicant: Snap Inc.

    Abstract: A landmark detection system can more accurately detect landmarks in images using a detection scheme that penalizes for dispersion parameters, such as variance or scale. The landmark detection system can be trained using both labeled and unlabeled training data in a semi-supervised approach. The landmark detection system can further implement tracking of an object across multiple images using landmark data.

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