公开(公告)号：US20240419947A1

公开(公告)日：2024-12-19

申请号：US18819957

申请日：2024-08-29

Applicant: HUAWEI TECHNOLOGIES CO., LTD.

Inventor： Xinghao CHEN ,  Yikai WANG ,  Xiudong WANG ,  Yunhe WANG

IPC: G06N3/0455

Abstract: Embodiments of this application disclose a data processing method. The method is used in a multimodal fusion scenario, and the method includes obtaining first data and second data, where modalities of the first data and the second data are different. The method also includes obtaining a first feature set of the first data and a second feature set of the second data, and replacing a first target feature in the first feature set with a second target feature in the second feature set, to obtain a third feature set, where the second target feature corresponds to the first target feature. The method further includes obtaining a data feature based on the third feature set and the second feature set, where the data feature is used to implement a computer vision task.

公开(公告)号：US20250156697A1

公开(公告)日：2025-05-15

申请号：US19019769

申请日：2025-01-14

Applicant: HUAWEI TECHNOLOGIES CO., LTD.

Inventor： Xinghao CHEN ,  Zhijun TU ,  Yunhe WANG

IPC: G06N3/0495 ,  G06N3/084

Abstract: This application provides a binary quantization method, a neural network training method, a device, and a storage medium. The binary quantization method includes: determining to-be-quantized data in a neural network; determining a quantization parameter corresponding to the to-be-quantized data, where the quantization parameter includes a scaling factor and an offset; determining, based on the scaling factor and the offset, a binary upper limit and a binary lower limit corresponding to the to-be-quantized data; and performing binary quantization on the to-be-quantized data based on the scaling factor and the offset, to quantize the to-be-quantized data into the binary upper limit or the binary lower limit.

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.