CONTENT RECOMMENDATION METHOD AND DEVICE
    61.
    发明申请
    CONTENT RECOMMENDATION METHOD AND DEVICE 审中-公开
    内容推荐方法和设备

    公开(公告)号:US20160337696A1

    公开(公告)日:2016-11-17

    申请号:US15111943

    申请日:2014-05-02

    Abstract: A content recommendation method and device for recommending content to a user are disclosed. According to one embodiment, the content recommendation device extracts the features of a user from image data, audio data and the like, and can determine a recognition rate indicating the degree that is recognized as a user model predetermined according to the features of the user. The content recommendation device can determine the recommended content to be provided to the user on the basis of the determined recognition rate.

    Abstract translation: 公开了一种向用户推荐内容的内容推荐方法和装置。 根据一个实施例,内容推荐设备从图像数据,音频数据等中提取用户的特征,并且可以根据用户的特征来确定指示被识别为预定的用户模型的程度的识别率。 内容推荐装置可以基于确定的识别率来确定要提供给用户的推荐内容。

    APPARATUS AND METHOD FOR CREATING POSE CLUSTER
    64.
    发明申请
    APPARATUS AND METHOD FOR CREATING POSE CLUSTER 审中-公开
    用于创建位置集合的装置和方法

    公开(公告)号:US20130181988A1

    公开(公告)日:2013-07-18

    申请号:US13738509

    申请日:2013-01-10

    CPC classification number: G06T17/00 G06K9/00369 G06K9/6272

    Abstract: Provided is a method of creating a body pose cluster, including performing feature extraction from pose data about at least one pose, classifying, as a single cluster, similar poses from a feature vector space using a similarity measure, and configuring the number of poses included in each cluster from the feature vector space to be uniform using an imbalance measure.

    Abstract translation: 提供了一种创建身体姿势群集的方法,包括从关于至少一个姿势的姿势数据执行特征提取,使用相似度度量从特征向量空间分类为单个群集类似的姿势,以及配置包括的姿势数 在每个聚类中从特征向量空间中均匀使用不平衡度量。

    METHOD AND APPARATUS WITH MAP CONSTRUCTION

    公开(公告)号:US20250157206A1

    公开(公告)日:2025-05-15

    申请号:US18946809

    申请日:2024-11-13

    Abstract: A high-definition (HD) map-related map construction method, electronic device, and storage medium are provided. The method includes: extracting a bird's-eye view (BEV) feature map based on the data; determining map information through a hybrid decoder based on the BEV feature map and a hybrid query; and constructing an HD map corresponding to the data based on the map information, wherein the map includes a plurality of map elements each including an area formed by a plurality of coordinate points in the map, the map information comprises coordinate information and class information of the plurality of map elements, and the hybrid query includes a plurality of hybrid features each corresponding to one map element and including a point feature and an element feature. Optionally, the method may be executed using an artificial intelligence (AI) model.

    METHOD AND APPARATUS WITH OBJECT TRACKING
    69.
    发明公开

    公开(公告)号:US20240144527A1

    公开(公告)日:2024-05-02

    申请号:US18311340

    申请日:2023-05-03

    CPC classification number: G06T7/74 G06T7/62 G06T7/66 G06T2207/20081

    Abstract: An object tracking apparatus is provided. The object tracking apparatus includes a processor configured to detect, from a first image frame, an amodal region including a first visible region in which a target object is visible and an occlusion region in which the target object is occluded, determine, based on the detected amodal region of the first image frame, that at least a partial region of a second image frame is a search region of the second image frame, the second image frame being temporally adjacent to the first image frame, and track the target object in the second image frame based on the determined search region.

    METHOD AND APPARATUS WITH OBJECT CLASSIFICATION

    公开(公告)号:US20230095716A1

    公开(公告)日:2023-03-30

    申请号:US17697160

    申请日:2022-03-17

    Abstract: An object classification method and apparatus are disclosed. The object classification method includes receiving an input image, storing first feature data extracted by a first feature extraction layer of a neural network configured to extract features of the input image, receiving second feature data from a second feature extraction layer which is an upper layer of the first feature extraction layer, generating merged feature data by merging the first feature data and the second feature data, and classifying an object in the input image based on the merged feature data.

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