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:
Dec. 26, 2023

Filed:

Oct. 20, 2022
Applicant:

Adobe Inc., San Jose, CA (US);

Inventors:

Sohrab Amirghodsi, Seattle, WA (US);

Zhe Lin, Fremont, CA (US);

Yilin Wang, Sunnyvale, CA (US);

Tianshu Yu, Tempe, AZ (US);

Connelly Barnes, Seattle, WA (US);

Elya Shechtman, Seattle, WA (US);

Assignee:

ADOBE INC., San Jose, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06V 10/75 (2022.01); G06F 17/18 (2006.01); G06N 3/08 (2023.01); G06N 20/00 (2019.01); G06V 10/82 (2022.01); G06F 18/214 (2023.01); G06F 18/22 (2023.01); G06F 18/211 (2023.01); G06F 18/213 (2023.01); G06V 10/74 (2022.01); G06V 10/771 (2022.01); G06V 10/774 (2022.01); G06V 20/70 (2022.01);
U.S. Cl.
CPC ...
G06V 10/757 (2022.01); G06F 17/18 (2013.01); G06F 18/211 (2023.01); G06F 18/213 (2023.01); G06F 18/214 (2023.01); G06F 18/22 (2023.01); G06N 3/08 (2013.01); G06N 20/00 (2019.01); G06V 10/761 (2022.01); G06V 10/771 (2022.01); G06V 10/774 (2022.01); G06V 10/82 (2022.01); G06V 20/70 (2022.01);
Abstract

A panoptic labeling system includes a modified panoptic labeling neural network ('modified PLNN') that is trained to generate labels for pixels in an input image. The panoptic labeling system generates modified training images by combining training images with mask instances from annotated images. The modified PLNN determines a set of labels representing categories of objects depicted in the modified training images. The modified PLNN also determines a subset of the labels representing categories of objects depicted in the input image. For each mask pixel in a modified training image, the modified PLNN calculates a probability indicating whether the mask pixel has the same label as an object pixel. The modified PLNN generates a mask label for each mask pixel, based on the probability. The panoptic labeling system provides the mask label to, for example, a digital graphics editing system that uses the labels to complete an infill operation.


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