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:
Apr. 30, 2024

Filed:

Jul. 21, 2021
Applicant:

Amazon Technologies, Inc., Seattle, WA (US);

Inventors:

Ria Chakraborty, Howrah, IN;

Madhur Popli, Ludhiana, IN;

Rachit Lamba, Delhi, IN;

Santosh Kumar Sahu, Bangalore, IN;

Rishi Kishore Verma, Bangalore, IN;

Assignee:

Amazon Technologies, Inc., Seattle, WA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06V 10/00 (2022.01); G06F 18/21 (2023.01); G06F 18/214 (2023.01); G06F 18/22 (2023.01); G06F 18/2413 (2023.01); G06F 18/40 (2023.01); G06N 3/04 (2023.01); G06N 3/08 (2023.01); G06T 7/73 (2017.01); G06V 10/75 (2022.01); G06F 3/0482 (2013.01);
U.S. Cl.
CPC ...
G06F 18/2148 (2023.01); G06F 18/2155 (2023.01); G06F 18/2163 (2023.01); G06F 18/2178 (2023.01); G06F 18/22 (2023.01); G06F 18/2413 (2023.01); G06F 18/40 (2023.01); G06N 3/04 (2013.01); G06N 3/08 (2013.01); G06T 7/73 (2017.01); G06V 10/751 (2022.01); G06F 3/0482 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06V 2201/09 (2022.01);
Abstract

Techniques are generally described for machine learning exampled-based annotation of image data. In some examples, a first machine learning model may receive a query image comprising a first depiction of an object-of-interest. In some examples, the first machine learning model may receive a target image representing a scene in which a second depiction of the object-of-interest is visually represented. In various examples, the first machine learning model may generate annotated output image data that identifies a location of the second depiction of the object-of-interest within the target image. In some examples, an object detection model may be trained based at least in part on the annotated output image data.


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