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    OBJECT DETECTION FROM IMAGE CONTENT
    1.
    发明申请
    OBJECT DETECTION FROM IMAGE CONTENT 审中-公开

    公开(公告)号：US20190258895A1

    公开(公告)日：2019-08-22

    申请号：US15900606

    申请日：2018-02-20

    申请人： Microsoft Technology Licensing, LLC

    发明人： Arun Sacheti ,  Xi Chen ,  Houdong Hu ,  Li Huang ,  Jiapei Huang ,  Meenaz Merchant

    IPC分类号： G06K9/62 ,  G06F17/30

    摘要： Non-limiting examples of the present disclosure relate to object detection processing of image content that categorically classifies specific objects within image content. Exemplary object detection processing may be utilized to enhance visual search processing including content retrieval and curation, among other technical advantages. An exemplary object detection model is implemented to categorically classify an object. In doing, so an exemplary object detection model may classify objects based on: analysis of specific objects within image content, positioning of the objects within the image content and intent associated with the image content, among other examples. The object detection model generates exemplary categorical classification(s) for specific data objects, which may be propagated to enhance processing efficiency and accuracy during visual search processing. Exemplary categorical classifications may comprise hierarchical classifications of a detected object that can be used to retrieve, curate and surface content that is most contextually relevant to a detected object.

    VISUAL INTENT TRIGGERING FOR VISUAL SEARCH
    2.
    发明申请
    VISUAL INTENT TRIGGERING FOR VISUAL SEARCH 审中-公开

    公开(公告)号：US20200019628A1

    公开(公告)日：2020-01-16

    申请号：US16036224

    申请日：2018-07-16

    申请人： Microsoft Technology Licensing, LLC

    发明人： Xi Chen ,  Houdong Hu ,  Li Huang ,  Jiapei Huang ,  Arun Sacheti ,  Linjun Yang ,  Rui Xia ,  Kuang-Huei Lee ,  Meenaz Merchant ,  Sean Chang Culatana

    IPC分类号： G06F17/30 ,  G06K9/62 ,  G06N5/04 ,  G06N99/00 ,  G06F17/11

    摘要： Representative embodiments disclose mechanisms to perform visual intent classification or visual intent detection or both on an image. Visual intent classification utilizes a trained machine learning model that classifies subjects in the image according to a classification taxonomy. The visual intent classification can be used as a pre-triggering mechanism to initiate further action in order to substantially save processing time. Example further actions include user scenarios, query formulation, user experience enhancement, and so forth. Visual intent detection utilizes a trained machine learning model to identify subjects in an image, place a bounding box around the image, and classify the subject according to the taxonomy. The trained machine learning model utilizes multiple feature detectors, multi-layer predictions, multilabel classifiers, and bounding box regression.

    MACHINE LEARNING HYPERPARAMETER TUNING TOOL
    3.
    发明申请
    MACHINE LEARNING HYPERPARAMETER TUNING TOOL 审中-公开

    公开(公告)号：US20190236487A1

    公开(公告)日：2019-08-01

    申请号：US15883686

    申请日：2018-01-30

    申请人： Microsoft Technology Licensing, LLC

    发明人： Jiapei Huang ,  Houdong Hu ,  Li Huang ,  Xi Chen ,  Linjun Yang

    IPC分类号： G06N99/00

    CPC分类号： G06N20/00 ,  G06F3/04842

    摘要： A technique for hyperparameter tuning can be performed via a hyperparameter tuning tool. In the technique, computer-readable values for each of one or more machine learning hyperparameters can be received. Multiple computer-readable hyperparameter value sets can be defined using different combinations of the values. In response to a request to start, an overall hyperparameter tuning operation can be performed via the tool, with the overall operation including a tuning job for each of the hyperparameter sets. A computer-readable comparison of the results of the parameter tuning operations can be generated for the hyperparameter sets, with the comparison indicating effectiveness of the hyperparameter sets, as compared to each other, in the tuning jobs.