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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 12475704 B1

PDFFull Text
Date of Patent:
Nov. 18, 2025

Filed:

Aug. 26, 2022

Method, system, and computer-readable medium for obtaining aggregated multi-scale first and second dimensional receptive field information

Applicant:

Guangdong Oppo Mobile Telecommunications Corp., Ltd., Guangdong, CN;

Inventors:

Zibo Meng, Palo Alto, CA (US);

Ming Chen, Palo Alto, CA (US);

Chiuman Ho, Palo Alto, CA (US);

Assignee:

GUANGDONG OPPO MOBILE TELECOMMUNICATIONS CORP., LTD., Guangdong, CN;

Attorney:

Young Basile Hanlon & MacFarlane, P.C.

Primary Examiner:

Quoc A Tran

Int. Cl.
CPC ...
G06V 20/40 (2022.01); G06N 3/045 (2023.01); G06N 3/08 (2023.01); G06N 3/084 (2023.01); G06V 10/40 (2022.01); G06V 10/44 (2022.01); G06V 10/50 (2022.01); G06V 10/764 (2022.01); G06V 10/80 (2022.01); G06V 10/82 (2022.01); G06V 30/24 (2022.01); H04B 17/309 (2015.01);
U.S. Cl.
CPC ...
G06V 20/41 (2022.01); G06V 10/82 (2022.01); G06V 30/2504 (2022.01); G06N 3/045 (2023.01); G06N 3/08 (2013.01); G06N 3/084 (2013.01); G06T 2207/20084 (2013.01); G06V 10/40 (2022.01); G06V 10/454 (2022.01); G06V 10/50 (2022.01); G06V 10/764 (2022.01); G06V 10/806 (2022.01); H04B 17/309 (2015.01);
Abstract

A method includes: aggregating information from a corresponding combination of all of multi-scale first dimensional receptive fields and each of multi-scale second dimensional receptive fields, so that information from multi-scale first and second dimensional receptive fields corresponding to the multi-scale second dimensional receptive fields is obtained; wherein the multi-scale first dimensional receptive fields being first dimensional is being one of spatial and temporal, and the multi-scale second dimensional receptive fields being second dimensional is being the other of spatial and temporal; wherein a corresponding first convolutional neural network operation set provides each of the multi-scale second dimensional receptive fields and each operation of the first convolutional neural network operation set has a corresponding first dimensional local to local constraint; and aggregating the information from the multi-scale first and second dimensional receptive fields to obtain aggregated multi-scale first and second dimensional receptive field information.

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