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:
Jun. 27, 2023

Filed:

Aug. 05, 2019
Applicant:

Salesforce.com, Inc., San Francisco, CA (US);

Inventors:

Mingfei Gao, San Jose, CA (US);

Richard Socher, Menlo Park, CA (US);

Caiming Xiong, Menlo Park, CA (US);

Assignee:

Salesforce.com, Inc., San Francisco, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06F 16/735 (2019.01); G06F 16/73 (2019.01); G06V 10/82 (2022.01); G06F 16/74 (2019.01); G06V 20/40 (2022.01); G06F 17/10 (2006.01); G06N 3/08 (2023.01); G06F 40/47 (2020.01); G06F 18/21 (2023.01); G06V 10/44 (2022.01);
U.S. Cl.
CPC ...
G06F 16/735 (2019.01); G06F 16/73 (2019.01); G06F 17/10 (2013.01); G06F 18/2185 (2023.01); G06F 40/47 (2020.01); G06N 3/08 (2013.01); G06V 10/82 (2022.01); G06V 20/41 (2022.01); G06V 20/49 (2022.01); G06V 10/454 (2022.01); G06V 20/44 (2022.01); G06V 20/46 (2022.01);
Abstract

Systems and methods are provided for weakly supervised natural language localization (WSNLL), for example, as implemented in a neural network or model. The WSNLL network is trained with long, untrimmed videos, i.e., videos that have not been temporally segmented or annotated. The WSNLL network or model defines or generates a video-sentence pair, which corresponds to a pairing of an untrimmed video with an input text sentence. According to some embodiments, the WSNLL network or model is implemented with a two-branch architecture, where one branch performs segment sentence alignment and the other one conducts segment selection. These methods and systems are specifically used to predict how a video proposal matches a text query using respective visual and text features.


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