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 10878297 B1

PDF
Full Text
Expired
Date of Patent:
Dec. 29, 2020

Filed:

Aug. 29, 2018
Applicant:

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

Inventors:

Oded Dubovsky, Haifa, IL;

Leonid Karlinsky, Mazkeret Batya, IL;

Joseph Shtok, Binyamina, IL;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06N 3/02 (2006.01); G06N 20/00 (2019.01); G06K 9/62 (2006.01); G06F 16/583 (2019.01); G06K 9/66 (2006.01); G06N 3/08 (2006.01);
U.S. Cl.
CPC ...
G06K 9/66 (2013.01); G06F 16/5838 (2019.01); G06K 9/627 (2013.01); G06K 9/628 (2013.01); G06K 9/6256 (2013.01); G06N 3/08 (2013.01);
Abstract

Embodiments may provide visual recognition techniques that provide improved recognition accuracy and reduced use of computing resources in cases where only a small set of examples is used to train an unlimited number of recognized categories. For example, in an embodiment, a computer-implemented method of visual recognition may comprise generating a plurality of personal embedding models, each personal embedding model including categories relating to a person, and object, or a subject, wherein at least some of the personal embedding models include at least some different categories, training the plurality of personal embedding models using image training data having a limited number of examples of each category, wherein the examples of each category are used to train more than one category in more than one of the personal embedding models, recognizing images from image data using the plurality of personal embedding models, and outputting information relating to the recognized images.


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