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. 01, 2025

Filed:

Jul. 27, 2022
Applicant:

Meta Platforms, Inc., Menlo Park, CA (US);

Inventors:

Kaiming He, Palo Alto, CA (US);

Piotr Dollar, San Mateo, CA (US);

Ross Girshick, Seattle, WA (US);

Saining Xie, Sunnyvale, CA (US);

Xinlei Chen, Belmont, CA (US);

Yanghao Li, Sunnyvale, CA (US);

Assignee:

Meta Platforms, Inc., Menlo Park, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06V 10/778 (2022.01); G06V 10/26 (2022.01); G06V 10/75 (2022.01); G06V 10/774 (2022.01);
U.S. Cl.
CPC ...
G06V 10/778 (2022.01); G06V 10/26 (2022.01); G06V 10/751 (2022.01); G06V 10/774 (2022.01);
Abstract

In particular embodiments, a computing system may access a plurality of images for pre-training a first machine-learning model that includes an encoder and a decoder. Using each image, the system may pre-train the model by dividing the image into a set a patches, selecting a first subset of the patches to be visible and a second subset of the patches to be masked during the pre-training, processing, using the encoder, the first subset of patches to generate corresponding first latent representations, processing, using the decoder, the first latent representations corresponding to the first subset of patches and mask tokens corresponding to the second subset of patches to generate reconstructed patches corresponding to the second subset of patches, the reconstructed patches and the first subset of patches being used to generate a reconstructed image, and updating the model based on comparisons between the image and the reconstructed image.


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