公开(公告)号：US20210184357A1

公开(公告)日：2021-06-17

申请号：US17259780

申请日：2018-07-13

Applicant: HUAWEI TECHNOLOGIES CO., LTD.

Inventor： Hang XU ,  Steven GAO ,  Hai ZHOU ,  Hanyang WANG

IPC: H01Q9/20 ,  H01Q1/48

Abstract: A sum and difference mode antenna includes a first radiator, a second radiator, a first excitation source, and a second excitation source. The first radiator includes a first segment, a second segment, and a gap formed between the first segment and the second segment, and the first excitation source is configured to feed the first radiator, so that currents in the first segment and the second segment both flow in a first direction. The second radiator includes a third segment, a fourth segment, and a fifth segment. The third segment and the fourth segment are symmetrically distributed on two sides of a connection end of the fifth segment, and the second excitation source is electrically connected to a feed end of the fifth segment to feed the second radiator.

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：US20230089380A1

公开(公告)日：2023-03-23

申请号：US17993430

申请日：2022-11-23

Applicant: HUAWEI TECHNOLOGIES CO., LTD.

Inventor： Chenhan JIANG ,  Hang XU ,  Zhenguo LI ,  Xiaodan LIANG

IPC: G06N3/04

Abstract: A neural network construction method and apparatus in the field of artificial intelligence, to accurately and efficiently construct a target neural network. The constructed target neural network has high output accuracy, may be further applied to different application scenarios, and has a strong generalization capability. The method includes: obtaining a start point network, where the start point network includes a plurality of serial subnets; performing at least one time of transformation on the start point network based on a preset first search space to obtain a serial network, where the first search space includes a range of parameters used for transforming the start point network; and if the serial network meets a preset condition, training the serial network by using a preset dataset to obtain a trained serial network; and if the trained serial network meets a termination condition, obtaining a target neural network based on the trained serial network.

公开(公告)号：US20220108546A1

公开(公告)日：2022-04-07

申请号：US17553236

申请日：2021-12-16

Applicant: HUAWEI TECHNOLOGIES CO., LTD.

Inventor： Hang XU ,  Zhenguo LI

IPC: G06V10/82 ,  G06V10/764 ,  G06V10/77 ,  G06N3/08 ,  G06N5/02

Abstract: This application provides an object detection method and apparatus. This application relates to the field of artificial intelligence, and specifically, to the field of computer vision. The method includes: obtaining a to-be-detected image; performing convolution processing on the to-be-detected image to obtain an initial image feature of a to-be-detected object in the to-be-detected image; determining an enhanced image feature of the to-be-detected object based on knowledge graph information; and determining a candidate frame and a classification of the to-be-detected object based on the initial image feature and the enhanced image feature of the to-be-detected object. The enhanced image feature indicates semantic information of a different object category corresponding to another object associated with the to-be-detected object. Therefore, in this application, an effect of the object detection method can be improved.

公开(公告)号：US20240152770A1

公开(公告)日：2024-05-09

申请号：US18411616

申请日：2024-01-12

Applicant: HUAWEI TECHNOLOGIES CO., LTD.

Inventor： Hang XU ,  Xiaozhe REN ,  Yichun YIN ,  Li QIAN ,  Zhenguo LI ,  Xin JIANG ,  Jiahui GAO

IPC: G06N3/0985 ,  G06N3/04

CPC classification number: G06N3/0985 ,  G06N3/04

Abstract: This application relates to the artificial intelligence field, and discloses a neural network search method and a related apparatus. The neural network search method includes: constructing attention heads in transformer layers by sampling a plurality of candidate operators during model search, to construct a plurality of candidate neural networks, and comparing performance of the plurality of candidate neural networks to select a target neural network with higher performance. In this application, a transformer model is constructed with reference to model search, so that a new attention structure with better performance than an original self-attention mechanism can be generated, and effect in a wide range of downstream tasks is significantly improved.

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：US20230144209A1

公开(公告)日：2023-05-11

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

CPC classification number: G06V20/588 ,  G06T5/20 ,  G06T7/11 ,  G06V10/771 ,  G06V10/803 ,  G06V10/82 ,  G06T2207/30256

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：US20220292357A1

公开(公告)日：2022-09-15

申请号：US17826873

申请日：2022-05-27

Applicant: HUAWEI TECHNOLOGIES CO., LTD.

Inventor： Hang XU ,  Zewei CHEN ,  Zhenguo LI

IPC: G06N3/08

Abstract: A neural network search method, apparatus, and device are provided, and relate to the field of artificial intelligence technologies, and specifically, to the field of automatic machine learning technologies. The method includes: A computing device obtains a dataset and N neural networks (S602), where N is a positive integer; and performs K evolutions on the N neural networks to obtain neural network obtained through the Kth evolution, where K is a positive integer (S604). In a process of each evolution, a network structure of a neural network obtained in previous evolution is mutated; and a candidate neural network is selected, based on a partially ordered hypothesis, from networks obtained through mutation.