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. 30, 2025

Filed:

Feb. 28, 2022
Applicant:

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

Inventors:

Jiaxuan Zhuo, Shenzhen, CN;

Hong Shang, Shenzhen, CN;

Zhongqian Sun, Shenzhen, CN;

Han Zheng, Shenzhen, CN;

Xinghui Fu, Shenzhen, CN;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06V 10/25 (2022.01); G06F 18/214 (2023.01); G06N 3/088 (2023.01); G06T 7/00 (2017.01); G06T 7/73 (2017.01); G06V 10/40 (2022.01); G06V 10/764 (2022.01); G06V 10/774 (2022.01); G06V 10/776 (2022.01); G06V 10/778 (2022.01); G06V 10/82 (2022.01); G16H 30/40 (2018.01); G16H 50/20 (2018.01); G16H 50/70 (2018.01); A61B 1/00 (2006.01); A61B 1/31 (2006.01);
U.S. Cl.
CPC ...
G06V 10/774 (2022.01); G06T 7/0012 (2013.01); G06T 7/73 (2017.01); G06V 10/40 (2022.01); G06V 10/764 (2022.01); G06V 10/776 (2022.01); G06V 10/7784 (2022.01); G16H 30/40 (2018.01); A61B 1/000094 (2022.02); A61B 1/000096 (2022.02); A61B 1/31 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/30032 (2013.01); G06V 2201/032 (2022.01);
Abstract

An object detection model training method includes: inputting an unannotated first sample image into an initial detection model of a current round, and outputting a first prediction result for a target object, transforming the first sample image and a first prediction position region within the first prediction result to obtain a second sample image and a prediction transformation result in the second sample image; inputting the second sample image into the initial detection model, and outputting a second prediction result for the target object; obtaining a loss value of unsupervised learning according to a difference between the second prediction result and the prediction transformation result; and adjusting model parameters of the initial detection model according to the loss value and returning to the operation of inputting a first sample image into an initial detection model of a current round to perform iterative training, to obtain an object detection model.


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