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:
Oct. 03, 2023

Filed:

Oct. 14, 2021
Applicant:

Ancestry.com Operations Inc., Lehi, UT (US);

Inventors:

Jiayun Li, Los Angeles, CA (US);

Mohammad K. Ebrahimpour, Fremont, CA (US);

Azadeh Moghtaderi, San Francisco, CA (US);

Yen-Yun Yu, Murray, UT (US);

Assignee:
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06N 3/08 (2023.01); G06N 3/084 (2023.01); G06F 18/214 (2023.01); G06F 18/21 (2023.01); G06N 3/044 (2023.01); G06N 3/045 (2023.01); G06V 10/764 (2022.01); G06V 10/778 (2022.01); G06V 10/82 (2022.01); G06V 10/44 (2022.01); G06V 20/70 (2022.01); G06V 20/20 (2022.01);
U.S. Cl.
CPC ...
G06N 3/084 (2013.01); G06F 18/214 (2023.01); G06F 18/217 (2023.01); G06N 3/044 (2023.01); G06N 3/045 (2023.01); G06N 3/08 (2013.01); G06V 10/454 (2022.01); G06V 10/764 (2022.01); G06V 10/7788 (2022.01); G06V 10/82 (2022.01); G06V 20/20 (2022.01); G06V 20/70 (2022.01);
Abstract

Techniques for training a machine-learning (ML) model for captioning images are disclosed. A plurality of feature vectors and a plurality of visual attention maps are generated by a visual model of the ML model based on an input image. Each of the plurality of feature vectors correspond to different regions of the input image. A plurality of caption attention maps are generated by an attention model of the ML model based on the plurality of feature vectors. An attention penalty is calculated based on a comparison between the caption attention maps and the visual attention maps. A loss function is calculated based on the attention penalty. One or both of the visual model and the attention model are trained using the loss function.


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