公开(公告)号：US12154352B2

公开(公告)日：2024-11-26

申请号：US18064016

申请日：2022-12-09

Applicant: HUAWEI TECHNOLOGIES CO., LTD.

Inventor： Xinyue Cai ,  Hang Xu ,  Wei Zhang ,  Zhen Yang ,  Zhenguo Li

IPC: G06V20/56 ,  G06T5/20 ,  G06T7/11 ,  G06V10/771 ,  G06V10/80 ,  G06V10/82

Abstract: This disclosure discloses lane line detection methods and devices. In an implementation, features extracted by different layers of the neural network are fused to obtain a fused second feature map, so that the second feature map obtained through fusion processing has a plurality of layers of features. The fused second feature map has a related feature of a low-layer receptive field and a related feature of a high-layer receptive field. Afterwards, an output predicted lane line set is divided into groups, where each predicted lane line in each group has an optimal prediction interval.

公开(公告)号：US11748452B2

公开(公告)日：2023-09-05

申请号：US17661448

申请日：2022-04-29

Applicant: Huawei Technologies Co., Ltd.

Inventor： Ruiming Tang ,  Huifeng Guo ,  Zhenguo Li ,  Xiuqiang He

IPC: G06F18/2321 ,  G06F18/2451 ,  G06F18/2133 ,  G06F18/2453

CPC classification number: G06F18/2321 ,  G06F18/2133 ,  G06F18/2451 ,  G06F18/2453

Abstract: The method includes: obtaining a plurality of pieces of feature data; automatically performing two different types of nonlinear combination processing operations on the plurality of pieces of feature data to obtain two groups of processed data, where the two groups of processed data include a group of higher-order data and a group of lower-order data, the higher-order data is related to a nonlinear combination of m pieces of feature data in the plurality of pieces of feature data, and the lower-order data is related to a nonlinear combination of n pieces of feature data in the plurality of pieces of feature data, where m≥3, and m>n≥2; and determining prediction data based on a plurality of pieces of target data, where the plurality of pieces of target data include the two groups of processed data.

公开(公告)号：US11580457B2

公开(公告)日：2023-02-14

申请号：US16863110

申请日：2020-04-30

Applicant: HUAWEI TECHNOLOGIES CO., LTD.

Inventor： Fei Chen ,  Zhenhua Dong ,  Zhenguo Li ,  Xiuqiang He ,  Li Qian ,  Shuaihua Peng

IPC: G06N5/02 ,  G06N20/00

Abstract: Example prediction methods and apparatus are described. One example includes sending a first model parameter and a second model parameter by a server to a plurality of terminals. The first model parameter and the second model parameter are adapted to a prediction model of the terminal. The server receives a first prediction loss sent by at least one of the plurality of terminals. A first prediction loss sent by each of the at least one terminal is calculated by the terminal based on the prediction model that uses the first model parameter and the second model parameter. The server updates the first model parameter based on the first prediction loss sent by the at least one terminal to obtain an updated first model parameter. The server updates the second model parameter based on the first prediction loss sent by the at least one terminal to obtain an updated second model parameter.

公开(公告)号：US20230031522A1

公开(公告)日：2023-02-02

申请号：US17964117

申请日：2022-10-12

Applicant: HUAWEI TECHNOLOGIES CO., LTD.

Inventor： Bin Liu ,  Ruiming Tang ,  Huifeng Guo ,  Niannan Xue ,  Guilin Li ,  Xiuqiang He ,  Zhenguo Li

IPC: G06K9/62 ,  G06N3/04 ,  G06N3/08

Abstract: This application relates to the field of artificial intelligence. A recommendation method based on automatic feature grouping includes: obtaining a plurality of candidate recommended objects and a plurality of association features of each of the plurality of candidate recommended objects; performing multi-order automatic feature grouping on the plurality of association features of each candidate recommended object, to obtain a multi-order feature interaction set of each candidate recommended object; obtaining an interaction feature contribution value of each candidate recommended object through calculation based on the plurality of association features in the multi-order feature interaction set of each candidate recommended object; obtaining a prediction score of each candidate recommended object through calculation based on the interaction feature contribution value of each candidate recommended object; and determining one or more corresponding candidate recommended objects with a high prediction score as a target recommended object.

公开(公告)号：US20220261659A1

公开(公告)日：2022-08-18

申请号：US17738685

申请日：2022-05-06

Applicant: Huawei Technologies Co., Ltd.

Inventor： Hang Xu ,  Zhenguo Li ,  Wei Zhang ,  Xiaodan Liang ,  Chenhan Jiang

IPC: G06N3/10 ,  G06N3/08 ,  G06N3/04

Abstract: This application provides a method and related apparatus for determining a neural network in the field of artificial intelligence. The method includes: obtaining a plurality of initial search spaces; determining M candidate neural networks based on the plurality of initial search spaces, where the candidate neural network includes a plurality of candidate subnetworks, the plurality of candidate subnetworks belong to the plurality of initial search spaces, and any two of the plurality of candidate subnetworks belong to different initial search spaces; evaluating the M candidate neural networks to obtain M evaluation results; and determining N candidate neural networks from the M candidate neural networks based on the M evaluation results, and determining N first target neural networks based on the N candidate neural networks. According to the method and the related apparatus provided in this application, a combined neural network with relatively high performance can be obtained.

公开(公告)号：US11194861B2

公开(公告)日：2021-12-07

申请号：US16405367

申请日：2019-05-07

Applicant: Huawei Technologies Co., Ltd.

Inventor： Zhenguo Li ,  Jiefeng Cheng ,  Zhihong Zhao

IPC: G06F16/00 ,  G06F16/901 ,  G06F16/90

Abstract: The method of the present disclosure includes: after a graph partitioning apparatus extracts an edge, first determining whether an aggregation degree between a currently extracted edge and an allocated edge in a first device satisfies a preset condition; then, when the preset condition is satisfied, determining whether a quantity of allocated edges stored in the first device is less than a first preset threshold; and allocating the currently extracted edge to the first device when the quantity is less than the first preset threshold. In this way, an aggregation degree between allocated edges in each device is relatively high and each device has relatively balanced load. When an edge changes and an edge associated with the particular edge needs to be synchronized, a relatively small quantity of devices need to perform synchronization and update, so that costs of communication between devices are reduced, and distributed graph computing efficiency is improved.