The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Sep. 15, 2026

Filed:

Jun. 24, 2024
Applicant:

Tencent Technology (Shenzhen) Company Limited, Shenzhen, CN;

Inventors:

Yawen Huang, Shenzhen, CN;

Ziyun Cai, Shenzhen, CN;

Dandan Zhang, Shenzhen, CN;

Yuexiang Li, Shenzhen, CN;

Hong Wang, Shenzhen, CN;

Yefeng Zheng, Shenzhen, CN;

Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G06V 10/82 (2022.01); G06T 11/60 (2026.01); G06V 10/44 (2022.01); G06V 10/764 (2022.01);
U.S. Cl.
CPC ...
G06V 10/82 (2022.01); G06T 11/60 (2013.01); G06V 10/44 (2022.01); G06V 10/764 (2022.01);
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.


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