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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.

Patent No.:

US 12210835 B1

PDFFull Text
Date of Patent:
Jan. 28, 2025

Filed:

Sep. 16, 2022

Multi-granularity alignment for visual question answering

Applicant:

Samsung Electronics Co., Ltd., Suwon-si, KR;

Inventors:

Peixi Xiong, Evanston, IL (US);

Yilin Shen, Santa Clara, CA (US);

Hongxia Jin, San Jose, CA (US);

Assignee:

Samsung Electronics Co., Ltd., Suwon-si, JP;

Attorney:
Primary Examiner:

Richard Z Zhu

Int. Cl.
CPC ...
G06F 40/30 (2020.01); G06F 40/284 (2020.01); G06F 40/289 (2020.01); G06V 10/40 (2022.01); G06V 10/70 (2022.01); G06V 10/80 (2022.01); G06F 16/33 (2019.01); G06V 10/426 (2022.01); G06V 10/762 (2022.01); G06V 10/764 (2022.01); G06V 10/82 (2022.01);
U.S. Cl.
CPC ...
G06F 40/30 (2020.01); G06F 40/284 (2020.01); G06F 40/289 (2020.01); G06V 10/40 (2022.01); G06V 10/811 (2022.01); G06F 16/3344 (2019.01); G06V 10/426 (2022.01); G06V 10/7635 (2022.01); G06V 10/764 (2022.01); G06V 10/82 (2022.01);
Abstract

In one embodiment, a method includes accessing an image and a natural-language question regarding the image and extracting, from the image, a first set of image features at a first level of granularity and a second set of image features at a second level of granularity. The method further includes extracting, from the question, a first set of text features at the first level of granularity and a second set of text features at the second level of granularity; generating a first output representing an alignment between the first set of image features and the first set of text features; generating a second output representing an alignment between the second set of image features and the second set of text features; and determining an answer to the question based on the first output and the second output.

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