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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.

Patent No.:

US 11763544 B1

PDFFull Text
Date of Patent:
Sep. 19, 2023

Filed:

Jul. 07, 2020

Denoising autoencoder image captioning

Applicant:

International Business Machines Corporation, Armonk, NY (US);

Inventors:

Shiwan Zhao, Beijing, CN;

Hao Kai Zhang, Harbin, CN;

Yi Ke Wu, Beijing, CN;

Zhong Su, Beijing, CN;

Assignee:

International Business Machines Corporation, Armonk, NY (US);

Attorney:

Randy Tejeda

Primary Examiner:

Michael R Neff

Int. Cl.
CPC ...
G06V 10/30 (2022.01); G06N 20/00 (2019.01); G06F 18/214 (2023.01); G06F 18/22 (2023.01); G06F 18/21 (2023.01); G06F 18/2113 (2023.01); G06V 10/74 (2022.01); G06V 10/764 (2022.01); G06V 10/771 (2022.01); G06V 10/774 (2022.01); G06V 10/776 (2022.01);
U.S. Cl.
CPC ...
G06V 10/30 (2022.01); G06F 18/217 (2023.01); G06F 18/2113 (2023.01); G06F 18/2148 (2023.01); G06F 18/22 (2023.01); G06N 20/00 (2019.01); G06V 10/761 (2022.01); G06V 10/764 (2022.01); G06V 10/771 (2022.01); G06V 10/776 (2022.01); G06V 10/7747 (2022.01);
Abstract

In an approach to augmenting a caption dataset by leveraging a denoising autoencoder to sample and generate additional captions from the ground truth captions, one or more computer processors generate a plurality of new captions utilizing an autoencoder fed with one or more noisy captions, wherein the autoencoder is trained with a dataset comprising a plurality of ground truth captions. The one or more computer processors calculate an importance weight for each new caption in the plurality of generated new captions as compared to a plurality of associated ground truth captions based on a consensus metric. The one or more computer processors train a caption model with the generated plurality of new captions and associated calculated weights.

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