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:
Mar. 05, 2024

Filed:

Mar. 29, 2021
Applicant:

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

Inventors:

Todd Hester, Seattle, WA (US);

Sheng Chen, Seattle, WA (US);

Mark Buckler, Seattle, WA (US);

Ayan Tuhinendu Sinha, Seattle, WA (US);

Hitesh Arora, Pittsburgh, PA (US);

Michael Lawrence LeKander, Seattle, WA (US);

Hamed Pirsiavash, Ellicott City, MD (US);

Assignee:

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

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06F 18/25 (2023.01); B25J 9/16 (2006.01); G06F 18/21 (2023.01); G06N 3/045 (2023.01); G06N 3/08 (2023.01); G06V 20/10 (2022.01);
U.S. Cl.
CPC ...
G06F 18/25 (2023.01); B25J 9/1697 (2013.01); G06F 18/2163 (2023.01); G06N 3/045 (2023.01); G06N 3/08 (2013.01); G06V 20/10 (2022.01);
Abstract

Techniques are generally described for fusing sensor data of different modalities using a transformer. In various examples, first sensor data may be received from a first sensor and second sensor data may be received from a second sensor. A first feature representation of the first sensor data may be generated using a first machine learning model and a second feature representation of the second sensor data may be generated using a second machine learning model. In some examples, a modified first feature representation of the first sensor data may be generated based at least in part on a self-attention mechanism of a transformer encoder. The modified first feature representation may be generated based at least in part on the first feature representation and the second feature representation. A computer vision task may be performed using the modified first feature representation.


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