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:
Mar. 21, 2023

Filed:

Feb. 26, 2021
Applicant:

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

Inventors:

Haozhi Huang, Shenzhen, CN;

Hao Wang, Shenzhen, CN;

Wenhan Luo, Shenzhen, CN;

Lin Ma, Shenzhen, CN;

Peng Yang, Shenzhen, CN;

Wenhao Jiang, Shenzhen, CN;

Xiaolong Zhu, Shenzhen, CN;

Wei Liu, Shenzhen, CN;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06V 10/00 (2022.01); G06K 9/62 (2022.01); G06N 3/04 (2023.01); G06N 3/08 (2023.01); G06K 9/00 (2022.01); G06V 10/30 (2022.01); G06V 10/44 (2022.01); G06V 10/75 (2022.01); G06V 10/98 (2022.01); G06V 20/40 (2022.01); G06T 5/00 (2006.01);
U.S. Cl.
CPC ...
G06K 9/6257 (2013.01); G06K 9/00 (2013.01); G06K 9/62 (2013.01); G06K 9/6232 (2013.01); G06N 3/04 (2013.01); G06N 3/08 (2013.01); G06V 10/30 (2022.01); G06V 10/454 (2022.01); G06V 10/751 (2022.01); G06V 10/993 (2022.01); G06V 20/40 (2022.01); G06V 20/46 (2022.01); G06T 5/00 (2013.01);
Abstract

A method, apparatus, and storage medium for training a neural network model used for image processing are described. The method includes: obtaining a plurality of video frames; inputting the plurality of video frames through a neural network model so that the neural network model outputs intermediate images; obtaining optical flow information between an early video frame and a later video frame; modifying an intermediate image corresponding to the early video frame according to the optical flow information to obtain an expected-intermediate image; determining a time loss between an intermediate image corresponding to the later video frame and the expected-intermediate image; determining a feature loss between the intermediate images and a target feature image; and training the neural network model according to the time loss and the feature loss, and returning to obtaining a plurality of video frames continue training until the neural network model satisfies a training finishing condition.


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