公开(公告)号：US20200302265A1

公开(公告)日：2020-09-24

申请号：US16359346

申请日：2019-03-20

Applicant: HUAWEI TECHNOLOGIES CO., LTD.

Inventor： Yunhe WANG ,  Chunjing XU ,  Kai HAN

IPC: G06N3/04 ,  G06F17/16 ,  G06F17/15

Abstract: This application discloses a convolutional neural network-based image processing method and image processing apparatus in the artificial intelligence field. The method may include: receiving an input image; preprocessing the input image to obtain preprocessed image information; and performing convolution on the image information using a convolutional neural network, and outputting a convolution result. In embodiments of this application, the image processing apparatus may store primary convolution kernels of convolution layers, and before performing convolution using the convolution layers, generate secondary convolution kernels using the primary convolution kernels of the convolution layers.

公开(公告)号：US20230401826A1

公开(公告)日：2023-12-14

申请号：US18456312

申请日：2023-08-25

Applicant: HUAWEI TECHNOLOGIES CO., LTD.

Inventor： Jianyuan GUO ,  Kai HAN ,  Yunhe WANG ,  Chunjing XU

IPC: G06V10/77 ,  G06V10/82 ,  G06V10/80

CPC classification number: G06V10/7715 ,  G06V10/82 ,  G06V10/806

Abstract: This disclosure discloses a perception network. The perception network may be applied to the artificial intelligence field, and includes a feature extraction network. A first block in the feature extraction network is configured to perform convolution processing on input data, to obtain M target feature maps; at least one second block in the feature extraction network is configured to perform convolution processing on M1 target feature maps in the M target feature maps, to obtain M1 first feature maps; a target operation in the feature extraction network is used to process M2 target feature maps in the M target feature maps, to obtain M2 second feature maps; and a concatenation operation in the feature extraction network is used to concatenate the M1 first feature maps and the M2 second feature maps, to obtain a concatenated feature map.

公开(公告)号：US20220157041A1

公开(公告)日：2022-05-19

申请号：US17587689

申请日：2022-01-28

Applicant: HUAWEI TECHNOLOGIES CO., LTD.

Inventor： Kai HAN ,  Yunhe WANG ,  Han SHU ,  Chunjing XU

IPC: G06V10/44 ,  G06V10/764 ,  G06V10/82

Abstract: This application relates to an image recognition technology in the field of computer vision in the field of artificial intelligence, and provides an image classification method and apparatus. The method includes: obtaining an input feature map of a to-be-processed image; performing convolution processing on the input feature map based on M convolution kernels of a neural network, to obtain a candidate output feature map of M channels, where M is a positive integer; performing matrix transformation on the M channels of the candidate output feature map based on N matrices, to obtain an output feature map of N channels, where a quantity of channels of each of the N matrices is less than M, N is greater than M, and N is a positive integer; and classify the to-be-processed image based on the output feature map, to obtain a classification result of the to-be-processed image.

公开(公告)号：US20250104397A1

公开(公告)日：2025-03-27

申请号：US18904682

申请日：2024-10-02

Applicant: HUAWEI TECHNOLOGIES CO., LTD.

Inventor： Hanting CHEN ,  Yunhe WANG ,  Chunjing XU

IPC: G06V10/764 ,  G06F17/16 ,  G06N3/02 ,  G06V10/77 ,  G06V10/82

Abstract: This application relates to an image recognition technology in the field of computer vision of artificial intelligence, and provides an image classification method and apparatus. An example method includes obtaining an input feature map of a to-be-processed image, and then performing feature extraction processing on the input feature map based on a feature extraction kernel of a neural network to obtain an output feature map, where each of a plurality of output sub-feature maps is determined based on the corresponding input sub-feature map and the feature extraction kernel, at least one of the output sub-feature maps is determined based on a target matrix obtained after an absolute value is taken, and a difference between the target matrix and the input sub-feature map corresponding to the target matrix is the feature extraction kernel. The to-be-processed image is classified based on the output feature map to obtain a classification result of the to-be-processed image.

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：US20230306719A1

公开(公告)日：2023-09-28

申请号：US18203337

申请日：2023-05-30

Applicant: HUAWEI TECHNOLOGIES CO., LTD.

Inventor： Tianyu GUO ,  Hanting CHEN ,  Yunhe WANG ,  Chunjing XU

IPC: G06V10/771 ,  G06T5/50 ,  G06T7/11 ,  G06V10/44

CPC classification number: G06V10/771 ,  G06T5/50 ,  G06T7/11 ,  G06V10/44 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/20221

Abstract: Embodiments of this application disclose a model structure, a method for training a model, an image enhancement method, and a device, and may be applied to the computer vision field in the artificial intelligence field. The model structure includes: a selection module, a plurality of first neural network layers, a segmentation module, a transformer module, a recombination module, and a plurality of second neural network layers. The model overcomes a limitation that the transformer module can only be used to process a natural language task, and may be applied to a low-level vision task. The model includes the plurality of first/second neural network layers, and different first/second neural network layers correspond to different image enhancement tasks. Therefore, after being trained, the model can be used to process different image enhancement tasks.

公开(公告)号：US20230419646A1

公开(公告)日：2023-12-28

申请号：US18237995

申请日：2023-08-25

Applicant: HUAWEI TECHNOLOGIES CO., LTD.

Inventor： Kai HAN ,  Yunhe WANG ,  An XIAO ,  Jianyuan GUO ,  Chunjing XU ,  Li QIAN

IPC: G06V10/80 ,  G06V10/40 ,  G06V10/82

CPC classification number: G06V10/806 ,  G06V10/40 ,  G06V10/82

Abstract: Embodiments of this disclosure relate to the field of artificial intelligence, and disclose a feature extraction method and apparatus. The method includes: obtaining a to-be-processed object, and obtaining a segmented object based on the to-be-processed object, where the segmented object includes some elements in the to-be-processed object, a first vector indicates the segmented object, and a second vector indicates some elements in the segmented object; performing feature extraction on the first vector to obtain a first feature, and performing feature extraction on the second vector to obtain a second feature; fusing at least two second features based on a first target weight, to obtain a first fused feature; and performing fusion processing on the first feature and the first fused feature to obtain a second fused feature, where the second fused feature is used to obtain a feature of the to-be-processed object.

公开(公告)号：US20230401838A1

公开(公告)日：2023-12-14

申请号：US18455918

申请日：2023-08-25

Applicant: HUAWEI TECHNOLOGIES CO., LTD.

Inventor： Xinghao CHEN ,  Wenshuo LI ,  Yunhe WANG ,  Chunjing XU

IPC: G06V10/82 ,  G06V10/77 ,  G06V10/80

CPC classification number: G06V10/82 ,  G06V10/7715 ,  G06V10/803

Abstract: An image processing method is disclosed in embodiments of this disclosure and is applied to the field of artificial intelligence. The method includes: obtaining an input feature map of an image to be processed, where the input feature map includes a first input sub-feature map and a second input sub-feature map, and resolution of the first input sub-feature map is higher than resolution of the second input sub-feature map; performing feature fusion processing on the input feature map by using a target network, to obtain an output feature map, where a feature of the first input sub-feature map is fused to a feature of the second input sub-feature map from a low level to a high level in the target network; and performing, based on the output feature map, object detection on the image to be processed, to obtain an object detection result.

公开(公告)号：US20230153615A1

公开(公告)日：2023-05-18

申请号：US18147297

申请日：2022-12-28

Applicant: HUAWEI TECHNOLOGIES CO., LTD.

Inventor： Yixing XU ,  Xinghao CHEN ,  Yunhe WANG ,  Chunjing XU

IPC: G06N3/08 ,  G06N3/045

CPC classification number: G06N3/08 ,  G06N3/045

Abstract: The technology of this application relates to a neural network distillation method, applied to the field of artificial intelligence, and includes processing to-be-processed data by using a first neural network and a second neural network to obtain a first target output and a second target output, where the first target output is obtained by performing kernel function-based transformation on an output of the first neural network layer, and the second target output is obtained by performing kernel function-based transformation on an output of the second neural network layer. The method further includes performing knowledge distillation on the first neural network based on a target loss constructed by using the first target output and the second target output.

公开(公告)号：US20210312261A1

公开(公告)日：2021-10-07

申请号：US17220158

申请日：2021-04-01

Applicant: HUAWEI TECHNOLOGIES CO., LTD.

Inventor： Yixing XU ,  Kai HAN ,  Yunhe WANG ,  Chunjing XU ,  Qi TIAN

IPC: G06N3/04 ,  G06N3/08

Abstract: The present application discloses a neural network search method in the field of artificial intelligence, and the neural network search method includes: obtaining a feature tensor of each of a plurality of neural networks, where the feature tensor of each neural network is used to represent a computing capability of the neural network; inputting the feature tensor of each of the plurality of neural networks into an accuracy prediction model for calculation, to obtain accuracy of each neural network, where the accuracy prediction model is obtained through training based on a ranking-based loss function; and determining a neural network corresponding to the maximum accuracy as a target neural network. Embodiments of the present invention help improve accuracy of a network structure found through search.