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:
Dec. 26, 2023

Filed:

Oct. 16, 2020
Applicant:

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

Inventors:

Baoyuan Wu, Shenzhen, CN;

Weidong Chen, Shenzhen, CN;

Wei Liu, Shenzhen, CN;

Yanbo Fan, Shenzhen, CN;

Tong Zhang, Shenzhen, CN;

Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G06F 16/58 (2019.01); G06F 16/55 (2019.01); G06N 20/00 (2019.01); G06F 16/535 (2019.01); G06V 30/262 (2022.01); G06V 30/19 (2022.01); G06V 20/30 (2022.01);
U.S. Cl.
CPC ...
G06F 16/58 (2019.01); G06F 16/535 (2019.01); G06F 16/55 (2019.01); G06N 20/00 (2019.01); G06V 20/30 (2022.01); G06V 30/19173 (2022.01); G06V 30/274 (2022.01);
Abstract

A method of establishing an image set for image recognition includes obtaining a single-label image set comprising an image annotated with a single label, and a multi-label image set comprising an image annotated with a plurality of labels; converting content of each label into a corresponding word identifier according to a semantic network, to obtain a word identifier set, a converted single-label image set, and a converted multi-label image set; and constructing a hierarchical semantic structure according to the word identifier set and the semantic network. The method also includes performing label supplementation on the image in the converted single-label image set to obtain a supplemented single-label image set; performing label supplementation on the supplemented single-label image set to obtain a final supplemented image set; and establishing a target multi-label image set to train an image recognition model by using the target multi-label image set.


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