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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.

Patent No.:

US 12548293 B1

PDFFull Text
Date of Patent:
Feb. 10, 2026

Filed:

Oct. 10, 2023

Image detection method and apparatus

Applicant:

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

Inventors:

Jinxi Xiang, Guangdong, CN;

Sen Yang, Guangdong, CN;

Jun Zhang, Guangdong, CN;

Dongxian Jiang, Guangdong, CN;

Yingyong Hou, Guangdong, CN;

Xiao Han, Guangdong, CN;

Assignee:

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

Attorney:

Crowell & Moring LLP

Primary Examiner:

Sumati Lefkowitz

Assistant Examiner:

Michael Adam Shariff

Int. Cl.
CPC ...
G06V 10/40 (2022.01); G06V 10/26 (2022.01); G06V 10/762 (2022.01); G06V 10/764 (2022.01); G06V 10/77 (2022.01); G06V 10/776 (2022.01); G06V 10/80 (2022.01);
U.S. Cl.
CPC ...
G06V 10/762 (2022.01); G06V 10/267 (2022.01); G06V 10/40 (2022.01); G06V 10/764 (2022.01); G06V 10/7715 (2022.01); G06V 10/776 (2022.01); G06V 10/806 (2022.01);
Abstract

An image detection method and apparatus are disclosed. The method includes: performing feature extraction processing on the image to obtain a feature representation subset of the image; generating attention weights corresponding to the at least two sub-image features; performing weighting aggregation processing on the at least two sub-image features according to the attention weights to obtain a first feature vector; performing clustering sampling processing on the at least two sub-image features to obtain at least two classification clusters comprising sampled sub-image features; determining a block sparse self-attention for each of the sampled sub-image features according to the at least two classification clusters and a block sparse matrix; determining a second feature vector according to at least two block sparse self-attentions respectively corresponding to the at least two classification clusters; and determining a classification result of the image according to the first feature vector and the second feature vector.

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