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:

Aug. 03, 2021
Applicant:

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

Inventors:

Fanyi Xiao, San Jose, CA (US);

Joseph P. Tighe, Seattle, WA (US);

Davide Modolo, Seattle, WA (US);

Assignee:

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

Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G06F 18/214 (2023.01); G06F 18/21 (2023.01); G06N 3/08 (2023.01); G06T 5/00 (2006.01); G06T 5/50 (2006.01); G06V 20/40 (2022.01);
U.S. Cl.
CPC ...
G06F 18/2148 (2023.01); G06F 18/2193 (2023.01); G06N 3/08 (2013.01); G06T 5/002 (2013.01); G06T 5/50 (2013.01); G06V 20/41 (2022.01); G06T 2207/10016 (2013.01); G06T 2207/20084 (2013.01);
Abstract

Techniques for training a visual neural network are described. In particular, the training of the visual neural network may be performed using one or more contrastive learning loss functions including one or more of a visual-to-visual contrastive learning function using the visual neural network on positive and negative video clips according to a first loss function, a secondary-to-secondary contrastive learning a secondary neural network on secondary positive and negative information generated from the positive and negative video clips, and a secondary-to-visual contrastive learning according to a third loss function using the visual neural network on positive and negative video clips and using the secondary neural network secondary positive and negative information generated from the positive and negative video clips.


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