公开(公告)号：US11314988B2

公开(公告)日：2022-04-26

申请号：US16771063

申请日：2018-03-26

Applicant: HUAWEI TECHNOLOGIES CO., LTD.

Inventor： Qing Zhang ,  Miao Xie ,  Shangling Jui

IPC: G06K9/62 ,  G06N3/04 ,  G06N3/08 ,  G06T7/00

Abstract: This application provides an image aesthetic processing method and an electronic device. A method for generating an image aesthetic scoring model includes: constructing a first neural network based on a preset convolutional structure set; obtaining an image classification neural network, where the image classification neural network is used to classify image scenarios; obtaining a second neural network based on the first neural network and the image classification neural network, where the second neural network is a neural network containing scenario information; and determining an image aesthetic scoring model based on the second neural network, where output information of the image aesthetic scoring model includes image scenario classification information. In this method, scenario information is integrated into a backbone neural network, so that a resulting image aesthetic scoring model is interpretable. In addition, scoring accuracy of the image aesthetic scoring model can be improved by using the preset convolutional structure set.

公开(公告)号：US12079579B2

公开(公告)日：2024-09-03

申请号：US17277455

申请日：2018-09-19

Applicant: Huawei Technologies Co., Ltd.

Inventor： Qing Zhang ,  Wei Yang ,  Yifan Xiao ,  Lianghe Zhang ,  Shangling Jui

IPC: G06F40/30 ,  G06F18/214 ,  G06F40/35 ,  G06N20/20

CPC classification number: G06F40/30 ,  G06F18/214 ,  G06F40/35 ,  G06N20/20

Abstract: An intention identification model learning method includes receiving positive data that corresponds to a first skill, generating, based on the positive data that corresponds to the first skill, negative data that corresponds to the first skill, determining a second skill similar to the first skill, obtaining data that corresponds to each second skill, generating a second base model based on the data that corresponds to the second skill and a first base model stored on the server, and performing learning based on the second base model, the positive data, and the negative data that correspond to the first skill, and generating an intention identification model.

公开(公告)号：US20210350084A1

公开(公告)日：2021-11-11

申请号：US17277455

申请日：2018-09-19

Applicant: Huawei Technologies Co., Ltd.

Inventor： Qing Zhang ,  Wei Yang ,  Yifan Xiao ,  Lianghe Zhang ,  Shangling Jui

IPC: G06F40/35 ,  G06N20/20 ,  G06K9/62

Abstract: An intention identification model learning method includes receiving positive data that corresponds to a first skill, generating, based on the positive data that corresponds to the first skill, negative data that corresponds to the first skill, determining, a second skill similar to the first skill, obtaining data that corresponds to each second skill, generating a second base model based on the data that corresponds to the second skill and a first base model stored on the server, and performing learning based on the second base model, the positive data, and the negative data that correspond to the first skill, and generating an intention identification model.

公开(公告)号：US20210182613A1

公开(公告)日：2021-06-17

申请号：US16771063

申请日：2018-03-26

Applicant: HUAWEI TECHNOLOGIES CO., LTD.

Inventor： Qing Zhang ,  Miao Xie ,  Shangling Jui

IPC: G06K9/62 ,  G06T7/00 ,  G06N3/04 ,  G06N3/08

Abstract: This application provides an image aesthetic processing method and an electronic device. A method for generating an image aesthetic scoring model includes: constructing a first neural network based on a preset convolutional structure set; obtaining an image classification neural network, where the image classification neural network is used to classify image scenarios; obtaining a second neural network based on the first neural network and the image classification neural network, where the second neural network is a neural network containing scenario information; and determining an image aesthetic scoring model based on the second neural network, where output information of the image aesthetic scoring model includes image scenario classification information. In this method, scenario information is integrated into a backbone neural network, so that a resulting image aesthetic scoring model is interpretable. In addition, scoring accuracy of the image aesthetic scoring model can be improved by using the preset convolutional structure set.