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    METHOD AND APPARATUS WITH ATTENTION-BASED OBJECT ANALYSIS
    13.
    发明公开
    METHOD AND APPARATUS WITH ATTENTION-BASED OBJECT ANALYSIS 审中-公开

    公开(公告)号：US20240153130A1

    公开(公告)日：2024-05-09

    申请号：US18342892

    申请日：2023-06-28

    Applicant: Samsung Electronics Co., Ltd.

    Inventor： Hyeongseok SON ,  Seungin PARK ,  Byung In YOO ,  Dongwook LEE ,  Solae LEE ,  Sangil JUNG

    IPC: G06T7/73 ,  G06T7/50 ,  G06V10/25 ,  G06V10/764 ,  G06V10/771

    CPC classification number: G06T7/73 ,  G06T7/50 ,  G06V10/25 ,  G06V10/764 ,  G06V10/771

    Abstract: An apparatus includes one or more processors configured to generate a plurality of feature maps having respective different resolutions based on an input image; and update, for each of the plurality of transformer layers, respective position estimation information comprising first position information of a respective bounding box corresponding to one object query and second position information of respective key points corresponding to the one object query, wherein each of the plurality of transformer layers includes a self-attention model configured to generate respective intermediate data by performing self-attention on respective content information on a feature of the input image; and a cross-attention model configured to generate respective output data by performing cross-attention on respective one or more feature maps among the plurality of feature maps and the respective generated intermediate data.

    METHOD AND APPARATUS FOR PROCESSING CONVOLUTION OPERATION ON LAYER IN NEURAL NETWORK
    14.
    发明申请
    METHOD AND APPARATUS FOR PROCESSING CONVOLUTION OPERATION ON LAYER IN NEURAL NETWORK 有权

    公开(公告)号：US20210279568A1

    公开(公告)日：2021-09-09

    申请号：US17015122

    申请日：2020-09-09

    Applicant: Samsung Electronics Co., Ltd.

    Inventor： Jaehyoung YOO ,  Jinwoo SON ,  Changyong SON ,  Seohyung LEE ,  Sangil JUNG ,  Changin CHOI

    IPC: G06N3/08 ,  G06N3/04 ,  G06F9/38

    Abstract: Disclosed are methods and apparatuses for processing a convolution operation on a layer in a neural network. The method includes extracting a first target feature vector from a target feature map, extracting a first weight vector matched with the first target feature vector from a first-type weight element, based on matching relationships for depth-wise convolution operations between target feature vectors of the target feature map and weight vectors of the first-type weight element, generating a first intermediate feature vector by performing multiplication between the first target feature vector and the first weight vector, generating a first hidden feature vector by accumulating the first intermediate feature vector and a second intermediate feature vector generated based on a second target feature vector, and generating a first output feature vector of an output feature map based on a point-wise convolution operation between the first hidden feature vector and a second-type weight element.

    METHOD AND APPARATUS WITH MAP CONSTRUCTION
    17.
    发明申请
    METHOD AND APPARATUS WITH MAP CONSTRUCTION 有权

    公开(公告)号：US20250157206A1

    公开(公告)日：2025-05-15

    申请号：US18946809

    申请日：2024-11-13

    Applicant: Samsung Electronics Co., Ltd.

    Inventor： Yi ZHOU ,  Hui ZHANG ,  Byung In YOO ,  Seungin PARK ,  Sangil JUNG ,  Xiaoshuai HAO

    IPC: G06V10/80 ,  G06V10/764 ,  G06V10/77 ,  G06V10/774

    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.

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