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:
Jul. 15, 2025

Filed:

May. 05, 2022
Applicant:

Google Llc, Mountain View, CA (US);

Inventors:

Ilya Tolstikhin, Adliswil, CH;

Neil Matthew Tinmouth Houlsby, Zurich, CH;

Alexander Kolesnikov, Zurich, CH;

Lucas Klaus Beyer, Zurich, CH;

Alexey Dosovitskiy, Berlin, DE;

Mario Lucic, Adliswil, CH;

Xiaohua Zhai, Zurich, CH;

Thomas Unterthiner, Berlin, DE;

Daniel M. Keysers, Stallikon, CH;

Jakob D. Uszkoreit, Berlin, DE;

Yin Ching Jessica Yung, Vienna, AT;

Andreas Peter Steiner, Zurich, CH;

Assignee:

Google LLC, Mountain View, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06V 10/82 (2022.01); G06N 3/04 (2023.01); G06V 10/764 (2022.01);
U.S. Cl.
CPC ...
G06V 10/82 (2022.01); G06N 3/04 (2013.01); G06V 10/764 (2022.01);
Abstract

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for processing images using mixer neural networks. One of the methods includes obtaining one or more images comprising a plurality of pixels; determining, for each image of the one or more images, a plurality of image patches of the image, wherein each image patch comprises a different subset of the pixels of the image; processing, for each image of the one or more images, the corresponding plurality of image patches to generate an input sequence comprising a respective input element at each of a plurality of input positions, wherein a plurality of the input elements correspond to respective different image patches; and processing the input sequences using a neural network to generate a network output that characterizes the one or more images, wherein the neural network comprises one or more mixer neural network layers.


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