公开(公告)号：US12039440B2

公开(公告)日：2024-07-16

申请号：US17582880

申请日：2022-01-24

Applicant: Huawei Technologies Co., Ltd. ,  Peking University

Inventor： Weiran Huang ,  Aoxue Li ,  Zhenguo Li ,  Tiange Luo ,  Liwei Wang

IPC: G06V10/774 ,  G06N3/045 ,  G06N3/08 ,  G06V10/82

CPC classification number: G06N3/08 ,  G06N3/045 ,  G06V10/774 ,  G06V10/82

Abstract: An image classification method and apparatus, and an image classification model training method and apparatus are provided, which are related to an image recognition technology in the artificial intelligence field and more specifically, to the computer vision field. The method includes: obtaining a to-be-processed image; and classifying the to-be-processed image based on a preset global class feature, to obtain a classification result of the to-be-processed image. The preset global class feature includes a plurality of class features obtained through training based on a plurality of training images in a training set. The plurality of class features in the preset global class feature are used to indicate visual features of all classes in the training set.

公开(公告)号：US11005737B2

公开(公告)日：2021-05-11

申请号：US16245717

申请日：2019-01-11

Applicant: HUAWEI TECHNOLOGIES CO., LTD.

Inventor： Zhenguo Li ,  Ge Luo ,  Ke Yi

IPC: G06F15/173 ,  H04L12/26

Abstract: A data processing method, includes receiving a data flow; generating a triplet set according to the data flow, where each triplet in the set includes three items, the first item is a first element in the data flow, the second item includes a first time point at which the first element appears in the data flow and a first quantity of times that corresponds to the first time point, and the third item includes a second time point at which the first element appears in the data flow and a second quantity of times that corresponds to the second time point; and performing data processing on the data flow according to the triplet set. In the embodiments of the present application, the triplet set may be generated based on the data flow.

公开(公告)号：US20170250705A1

公开(公告)日：2017-08-31

申请号：US15597963

申请日：2017-05-17

Applicant: HUAWEI TECHNOLOGIES CO.,LTD.

Inventor： Zhenguo Li ,  Ge Luo ,  Ke Yi ,  Wei Fan ,  Cheng He

IPC: H03M7/30

CPC classification number: H03M7/30 ,  H03M7/3064

Abstract: A method for compressing flow data, including: constructing multiple line segments according to flow data and a predefined maximum error that are acquired; obtaining a target piecewise linear function according to the multiple line segments, where the target piecewise linear function includes multiple linear functions, and an intersection set of value ranges of independent variables of every two linear functions among the multiple linear functions includes a maximum of one value; and outputting a reference data point according to the target piecewise linear function, where the reference data point includes a point of continuity and a point of discontinuity of the target piecewise linear function. In this way, a maximum error, a target piecewise linear function is further determined according to the multiple line segments, and a point of continuity and a point of discontinuity of the target piecewise linear function are used to represent compressed flow data.

公开(公告)号：US20240185568A1

公开(公告)日：2024-06-06

申请号：US18400930

申请日：2023-12-29

Applicant: Huawei Technologies Co., Ltd.

Inventor： Aoxue Li ,  Zhenguo Li

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

CPC classification number: G06V10/764 ,  G06V10/7715 ,  G06V10/806

Abstract: An image classification method includes obtaining a first feature of a reference image and a second feature of a to-be-classified image; generating a third feature based on the first feature and the second feature; generating a first classification result based on the first feature, where the first classification result is used to determine a category of the reference image; generating a second classification result based on the third feature; and generating a third classification result based on the first classification result and the second classification result, where the third classification result is used to determine a category of the to-be-classified image.

公开(公告)号：US20230401756A1

公开(公告)日：2023-12-14

申请号：US18455844

申请日：2023-08-25

Applicant: Huawei Technologies Co., Ltd.

Inventor： Shifeng Zhang ,  Chen Zhang ,  Ning Kang ,  Zhenguo Li

IPC: G06T9/00

CPC classification number: G06T9/002

Abstract: A data encoding method includes obtaining to-be-encoded data; processing the to-be-encoded data by using a volume preserving flow model to obtain a hidden variable output, where the volume preserving flow model includes a target volume preserving flow layer, an operation corresponding to the target volume preserving flow layer is an invertible operation that meets a volume preserving flow constraint, the target volume preserving flow layer is used to perform a multiplication operation on a preset coefficient and first data input to the target volume preserving flow layer, and the preset coefficient is not 1; and encoding the hidden variable output to obtain encoded data.

公开(公告)号：US20230082597A1

公开(公告)日：2023-03-16

申请号：US17990125

申请日：2022-11-18

Applicant: Huawei Technologies Co., Ltd.

Inventor： Yunfeng Lin ,  Guilin Li ,  Xing Zhang ,  Weinan Zhang ,  Zhenguo Li

IPC: G06N3/04 ,  G06N3/08

Abstract: A neural network construction method and system in the field of artificial intelligence, to construct a target neural network by replacing a part of basic units in an initial backbone network with placeholder modules, so that different target neural networks can be constructed based on different scenarios. The method may include obtaining an initial backbone network and a candidate set, replacing at least one basic unit in the initial backbone network with at least one placeholder module to obtain a to-be-determined network, performing sampling based on the candidate set to obtain information about at least one sampling structure, and obtaining a network model based on the to-be-determined network and the information about the at least one sampling structure. The information about the at least one sampling structure may be used for determining a structure of the at least one placeholder module.

公开(公告)号：US20230026322A1

公开(公告)日：2023-01-26

申请号：US17948392

申请日：2022-09-20

Applicant: Huawei Technologies Co., Ltd.

Inventor： Guilin Li ,  Bin Liu ,  Ruiming Tang ,  Xiuqiang He ,  Zhenguo Li

IPC: G06N3/08 ,  G06N3/04

Abstract: A data processing method related to the field of artificial intelligence includes adding an architecture parameter to each feature interaction item in a first model, to obtain a second model, where the first model is a factorization machine (FM)-based model, and the architecture parameter represents importance of a corresponding feature interaction item; performing optimization on architecture parameters in the second model to obtain the optimized architecture parameters; and obtaining, based on the optimized architecture parameters and the first model or the second model, a third model through feature interaction item deletion.

公开(公告)号：US20220375213A1

公开(公告)日：2022-11-24

申请号：US17880318

申请日：2022-08-03

Applicant: Huawei Technologies Co., Ltd.

Inventor： Hang Xu ,  Zhili Liu ,  Fengwei Zhou ,  Jiawei Li ,  Xiaodan Liang ,  Zhenguo Li ,  Li Qian

IPC: G06V10/82 ,  G06V10/77 ,  G06N3/063 ,  G06V10/28 ,  G06V10/774

Abstract: A processing apparatus includes a collection module and a training module, the training module includes a backbone network and a region proposal network (RPN) layer, the backbone network is connected to the RPN layer, and the RPN layer includes a class activation map (CAM) unit. The collection module is configured to obtain an image, where the image includes an image with an instance-level label and an image with an image-level label. The backbone network is used to output a feature map of the image based on the image obtained by the collection module.

公开(公告)号：US20210197855A1

公开(公告)日：2021-07-01

申请号：US17198937

申请日：2021-03-11

Applicant: Huawei Technologies Co., Ltd.

Inventor： Xing Zhang ,  Lin Lan ,  Zhenguo Li ,  Li Qian

IPC: B60W60/00 ,  G06N3/08

Abstract: A self-driving method and a related apparatus, the method including determining, by a self-driving apparatus, a task feature vector of a self-driving task according to M groups of historical paths of the self-driving task, where the task feature vector is a vector representing features of the self-driving task, and where M is an integer greater than 0, determining, by the self-driving apparatus, according to the task feature vector and a status vector, a target driving operation that needs to be performed, where the status vector indicates a driving status of the self-driving apparatus, and performing, by the self-driving apparatus, the target driving operation.

公开(公告)号：US09768801B1

公开(公告)日：2017-09-19

申请号：US15597963

申请日：2017-05-17

Applicant: HUAWEI TECHNOLOGIES CO., LTD.

Inventor： Zhenguo Li ,  Ge Luo ,  Ke Yi ,  Wei Fan ,  Cheng He

IPC: H03M7/30

CPC classification number: H03M7/30 ,  H03M7/3064

Abstract: A method for compressing flow data, including: constructing multiple line segments according to flow data and a predefined maximum error that are acquired; obtaining a target piecewise linear function according to the multiple line segments, where the target piecewise linear function includes multiple linear functions, and an intersection set of value ranges of independent variables of every two linear functions among the multiple linear functions includes a maximum of one value; and outputting a reference data point according to the target piecewise linear function, where the reference data point includes a point of continuity and a point of discontinuity of the target piecewise linear function. In this way, a maximum error, a target piecewise linear function is further determined according to the multiple line segments, and a point of continuity and a point of discontinuity of the target piecewise linear function are used to represent compressed flow data.