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. 09, 2019

Filed:

Jun. 15, 2017
Applicant:

Facebook, Inc., Menlo Park, CA (US);

Inventors:

Pedro Henrique Oliveira Pinheiro, Saint Sulpice, CH;

Ronan Stéfan Collobert, Mountain View, CA (US);

Piotr Dollar, San Mateo, CA (US);

Assignee:

Facebook, Inc., Menlo Park, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06K 9/00 (2006.01); G06K 9/62 (2006.01); G06N 99/00 (2019.01); G06N 5/04 (2006.01); G06N 3/04 (2006.01); G06K 9/46 (2006.01);
U.S. Cl.
CPC ...
G06K 9/6212 (2013.01); G06K 9/00201 (2013.01); G06K 9/4628 (2013.01); G06K 9/6271 (2013.01); G06N 3/04 (2013.01); G06N 5/04 (2013.01); G06N 99/005 (2013.01);
Abstract

In one embodiment, a plurality of patches of an image are processed using a first deep-learning model to detect a plurality of features associated with the first patch of the image. Each patch includes one or more pixels of the image. Using a second deep-learning model, a respective object proposal is generated for each of the plurality of patches of the image. The second deep-learning model takes as input the plurality of detected features associated with the respective patch of the image, and each object proposal includes a prediction as to a location of an object in the patch. Using a third deep-learning model, a respective score is computed for each object proposal generated using the second deep-learning model. The third deep-learning model takes as input the plurality of detected features associated with the respective patch of the image, and the object score may include a likelihood that the patch contains an entire object.


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