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:
May. 26, 2026

Filed:

Aug. 19, 2022
Applicants:

Versitech Limited, Hong Kong, HK;

Tcl Technology Group Corporation, Huizhou, CN;

Inventors:

Ping Luo, Hong Kong, HK;

Jiannan Wu, Beijing, CN;

Jiajun Shen, Hong Kong, HK;

Lan MA, Hong Kong, HK;

Attorneys:
Primary Examiner:
Int. Cl.
CPC ...
G06V 20/40 (2022.01); G06V 10/44 (2022.01); G06V 10/764 (2022.01);
U.S. Cl.
CPC ...
G06V 20/49 (2022.01); G06V 10/44 (2022.01); G06V 10/764 (2022.01);
Abstract

A system and method for video temporal action proposal generation are provided. It processes video features extracted from the input video through an encoder to obtain video encoding features with global information, extracts corresponding interest segment features from the video encoding features using pre-trained proposal segments, and provides them to the decoder. The decoder generates segment features based on the interest segment features corresponding to each proposal segment and the pre-trained proposal features. These are then provided to the prediction module, generating temporal action proposal results based on the decoder's segment features. The solution in embodiments of the present invention can effectively capture global context information of the video, obtaining video encoding features with stronger representational capabilities. By introducing the several learnable proposal segments to extract the corresponding position-based feature sequences from the video encoding features, the training convergence speed is enhanced, and computational burden is reduced.


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