公开(公告)号：US20240282119A1

公开(公告)日：2024-08-22

申请号：US18649088

申请日：2024-04-29

Applicant: HUAWEI TECHNOLOGIES CO., LTD.

Inventor： Minzhe NIU ,  Hang XU ,  Chunjing XU

IPC: G06V20/58 ,  G06V10/82

CPC classification number: G06V20/58 ,  G06V10/82

Abstract: A point cloud data processing method, a neural network training method, and a related device are provided. The method may be applied to the field of point cloud data processing in the field of artificial intelligence. The method may include: obtaining point cloud data corresponding to a target environment, where the point cloud data is divided into a plurality of target cubes; generating an initial feature of each target cube based on initial information of a target point in each target cube; updating initial features of the plurality of target cubes based on an attention mechanism to obtain updated features of the plurality of target cubes; and performing a feature processing operation on the updated features of the plurality of target cubes to obtain a prediction result corresponding to the point cloud data.

公开(公告)号：US20240070436A1

公开(公告)日：2024-02-29

申请号：US17900592

申请日：2022-08-31

Applicant: Huawei Technologies Co., Ltd.

Inventor： Hang XU ,  Lu HOU ,  Guansong LU ,  Minzhe NIU ,  Zhenguo LI ,  Runhui HUANG ,  Lewei YAO ,  Chunjing XU ,  Xiaodan LIANG

IPC: G06N3/04 ,  G06F40/284

CPC classification number: G06N3/0454 ,  G06F40/284

Abstract: A method is provided for data processing performed by a processing system. The method comprises determining a set of first tokens for first data and a set of second token for second data, each token comprising information associated with a segment of the respective data, determining pair-wise similarities between the set of first tokens and the set of second tokens, each pair comprising a first token in the set of first tokens and a second token in the set of second tokens, determining, for each first token in the set of first tokens, a maximum similarity based on the determined pair-wise similarities between the respective first token and the second tokens in the set of second tokens, and determining a first similarity between the first data and the second data by aggregating the maximum similarities corresponding to the first tokens in the set of first set of tokens.

公开(公告)号：US20230075836A1

公开(公告)日：2023-03-09

申请号：US17986081

申请日：2022-11-14

Applicant: HUAWEI TECHNOLOGIES CO., LTD.

Inventor： Fuhui TANG ,  Xiaopeng ZHANG ,  Minzhe NIU ,  Zichen WANG ,  Jianhua HAN ,  Qi TIAN

IPC: G06V10/774 ,  G06V10/40 ,  G06V10/82 ,  G06V10/764

Abstract: A model training method and a related apparatus are provided, which may be used in computer vision to perform image detection. The method includes: extracting feature information in a target image; further separately extracting features of a target object from the feature information by using a Gaussian mask to obtain a first local feature and a second local feature; determining a feature loss by using the first local feature and the second local feature; performing prediction by using the first network and the second network based on a same region proposal set to obtain a first classification predicted value and a second classification predicted value, and obtaining a classification loss based on the first classification predicted value and the second classification predicted value; and training the second network based on the classification loss and the feature loss to obtain a target network.

公开(公告)号：US20200242450A1

公开(公告)日：2020-07-30

申请号：US16850549

申请日：2020-04-16

Applicant: HUAWEI TECHNOLOGIES CO., LTD.

Inventor： Ruiming TANG ,  Minzhe NIU ,  Yanru QU ,  Weinan ZHANG ,  Yong YU

IPC: G06N3/04 ,  G06N3/063 ,  G06K9/62 ,  G06F17/16

Abstract: Example user behavior prediction methods and apparatus are described. One example method includes obtaining a first contribution value of each piece of characteristic data for a specified behavior after obtaining behavior prediction information including a plurality of pieces of characteristic data. Every N pieces of characteristic data in the plurality of pieces of characteristic data may be processed by using one corresponding characteristic interaction model, to obtain a second contribution value of the every N pieces of characteristic data for the specified behavior. Finally, an execution probability of executing the specified behavior by a user may be determined based on the obtained first contribution value and the obtained second contribution value, to predict a user behavior. In the example method, interaction impact of the plurality of pieces of characteristic data on the specified behavior is considered during behavior prediction.