公开(公告)号：US20240355110A1

公开(公告)日：2024-10-24

申请号：US18752567

申请日：2024-06-24

Applicant: TENCENT TECHNOLOGY (SHENZHEN) COMPANY LIMITED

Inventor： Yawen HUANG ,  Ziyun CAI ,  Dandan ZHANG ,  Yuexiang LI ,  Hong WANG ,  Yefeng ZHENG

IPC: G06V10/82 ,  G06T11/60 ,  G06V10/44 ,  G06V10/764

CPC classification number: G06V10/82 ,  G06T11/60 ,  G06V10/44 ,  G06V10/764

Abstract: A method for training an image classification model performed by an electronic device and includes: obtaining a plurality of sample source-domain images, a plurality of sample target-domain images, modal tagging results of the sample source-domain images, and category tagging results of the sample source-domain images; determining first category prediction results of the sample source-domain images by using a neural network model; determining first category prediction results of the sample target-domain images by using the neural network model; for a category tagging result, determining a first loss of the category tagging result based on source-domain image feature pairs corresponding to the category tagging result; and training the neural network model based on first losses of category tagging results, the first category prediction results of the sample source-domain images, and the first category prediction results of the sample target-domain images, to obtain an image classification model.