公开(公告)号：US20230215148A1

公开(公告)日：2023-07-06

申请号：US18183590

申请日：2023-03-14

Applicant: Beijing Baidu Netcom Science Technology Co., Ltd.

Inventor： Shuilong DONG ,  Sensen HE ,  Shengyu WEI ,  Cheng CUI ,  Yuning DU ,  Tingquan GAO ,  Shao ZENG ,  Ying ZHOU ,  Xueying LYU ,  Yi LIU ,  Qiao ZHAO ,  Qiwen LIU ,  Ran BI ,  Xiaoguang HU ,  Dianhai YU ,  Yanjun MA

IPC: G06V10/774 ,  G06V10/40 ,  G06V10/74 ,  G06V10/764 ,  G06V10/776 ,  G06V10/778

CPC classification number: G06V10/774 ,  G06V10/40 ,  G06V10/761 ,  G06V10/764 ,  G06V10/776 ,  G06V10/7784

Abstract: The present disclosure provides a method for training a feature extraction model, a method for classifying an image and related apparatuses, and relates to the field of artificial intelligence technology such as deep learning and image recognition. The scheme comprises: extracting an image feature of each sample image in a sample image set using a basic feature extraction module of an initial feature extraction model, to obtain an initial feature vector set; performing normalization processing on each initial feature vector in the initial feature vector set using a normalization processing module of the initial feature extraction model, to obtain each normalized feature vector; and guiding training for the initial feature extraction model through a preset high discriminative loss function, to obtain a target feature extraction model as a training result.

公开(公告)号：US20220247626A1

公开(公告)日：2022-08-04

申请号：US17718149

申请日：2022-04-11

Applicant: Beijing Baidu Netcom Science Technology Co., Ltd.

Inventor： Cheng CUI ,  Tingquan GAO ,  Shengyu WEI ,  Yuning DU ,  Ruoyu GUO ,  Bin LU ,  Ying ZHOU ,  Xueying LYU ,  Qiwen LIU ,  Xiaoguang HU ,  Dianhai YU ,  Yanjun MA

IPC: H04L41/0806 ,  H04L41/084 ,  H04L41/0894 ,  G06K9/62

Abstract: The present disclosure provides a method for generating a backbone network, an apparatus for generating a backbone network, a device, and a storage medium. The method includes: acquiring a set of a training image, a set of an inference image, and a set of an initial backbone network; training and inferring, for each initial backbone network in the set of the initial backbone network, the initial backbone network by using the set of the training image and the set of the inference image, to obtain an inference time and an inference accuracy of a trained backbone network in an inference process; determining a basic backbone network based on the inference time and the inference accuracy of the trained backbone network in the inference process; and obtaining a target backbone network based on the basic backbone network and a preset target network.