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:
Nov. 07, 2023

Filed:

Dec. 21, 2021
Applicant:

Dalian University of Technology, Liaoning, CN;

Inventors:

Xin Yang, Liaoning, CN;

Xiaopeng Wei, Liaoning, CN;

Yu Qiao, Liaoning, CN;

Qiang Zhang, Liaoning, CN;

Baocai Yin, Liaoning, CN;

Haiyin Piao, Liaoning, CN;

Zhenjun Du, Liaoning, CN;

Assignee:
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06V 10/00 (2022.01); G06V 20/40 (2022.01); G06V 10/46 (2022.01); G06V 10/82 (2022.01); G06T 3/40 (2006.01); G06T 7/215 (2017.01); G06T 9/00 (2006.01); G06V 10/72 (2022.01); G06V 10/764 (2022.01); G06V 10/778 (2022.01); G06V 10/774 (2022.01); G06V 10/776 (2022.01); G06T 7/10 (2017.01); G06F 18/21 (2023.01); G06F 18/214 (2023.01);
U.S. Cl.
CPC ...
G06V 20/49 (2022.01); G06F 18/217 (2023.01); G06F 18/2155 (2023.01); G06T 3/4007 (2013.01); G06T 3/4046 (2013.01); G06T 7/10 (2017.01); G06T 7/215 (2017.01); G06T 9/002 (2013.01); G06V 10/46 (2022.01); G06V 10/72 (2022.01); G06V 10/764 (2022.01); G06V 10/776 (2022.01); G06V 10/778 (2022.01); G06V 10/7753 (2022.01); G06V 10/82 (2022.01); G06V 20/41 (2022.01); G06T 2207/10016 (2013.01); G06T 2207/10024 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01);
Abstract

The present invention belongs to the technical field of computer vision, and provides a video semantic segmentation method based on active learning, comprising an image semantic segmentation module, a data selection module based on the active learning and a label propagation module. The image semantic segmentation module is responsible for segmenting image results and extracting high-level features required by the data selection module; the data selection module selects a data subset with rich information at an image level, and selects pixel blocks to be labeled at a pixel level; and the label propagation module realizes migration from image to video tasks and completes the segmentation result of a video quickly to obtain weakly-supervised data. The present invention can rapidly generate weakly-supervised data sets, reduce the cost of manufacture of the data and optimize the performance of a semantic segmentation network.


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