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:
Jun. 23, 2026

Filed:

May. 12, 2023
Applicant:

Robert Bosch Gmbh, Stuttgart, DE;

Inventors:

Wenbin He, Santa Clara, CA (US);

Suphanut Jamonnak, Santa Clara, CA (US);

Liang Gou, San Jose, CA (US);

Liu Ren, Saratoga, CA (US);

Assignee:

Robert Bosch GmbH, Stuttgart, DE;

Attorneys:
Primary Examiner:
Int. Cl.
CPC ...
G06K 9/00 (2022.01); G06V 10/74 (2022.01); G06V 10/764 (2022.01); G06V 10/771 (2022.01); G06V 10/774 (2022.01);
U.S. Cl.
CPC ...
G06V 10/764 (2022.01); G06V 10/761 (2022.01); G06V 10/771 (2022.01); G06V 10/774 (2022.01);
Abstract

A computer-implemented system and method relates to language-guided self-supervised semantic segmentation. A modified image is generated by performing data augmentation on a source image. A machine learning model generates first pixel embeddings based on the modified image. First segment embeddings are generated using the first pixel embeddings. A pretrained vision-language model generates second pixel embeddings based on the source image. Second segment embeddings are generated by applying segment contour data from the first pixel embeddings to the second pixel embeddings after the data augmentation is performed on the second pixel embeddings. Embedding consistent loss data is generated by comparing the first segment embeddings in relation to the second segment embeddings. Combined loss data is generated that includes the embedding consistent loss data. Parameters of the machine learning model are updated based on the combined loss data.


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