公开(公告)号：US20240005453A1

公开(公告)日：2024-01-04

申请号：US18368897

申请日：2023-09-15

Applicant: SAMSUNG ELECTRONICS CO., LTD.

Inventor： Albert SAA-GARRIGA ,  Mehmet YUCEL ,  Tommaso MAESTRI ,  Konstantinos PEPPAS

IPC: G06T3/60 ,  G06V10/774 ,  G06V10/82 ,  G06V10/776 ,  G06V10/94 ,  G06N3/098 ,  G06N3/045

CPC classification number: G06T3/60 ,  G06V10/774 ,  G06V10/82 ,  G06V10/776 ,  G06V10/95 ,  G06N3/098 ,  G06N3/045 ,  G06V2201/10

Abstract: Broadly speaking, the present techniques generally relates to methods, systems and apparatuses for performing image orientation correction, i.e. correcting or adjusting images that are tilted. In particular, the present application relates to a computer-implemented method for generating a training dataset for training a machine learning, ML, model using federated learning to perform image orientation correction, and methods for training the ML model using the generated training dataset. Advantageously, the method to generate a training dataset enables a diverse training dataset to be generated while maintaining user data privacy, where the diversity refers to the range of image tilt angles represented by the dataset. The present application also provides methods for training the ML model using the generated training dataset.

公开(公告)号：US20230114028A1

公开(公告)日：2023-04-13

申请号：US17960571

申请日：2022-10-05

Applicant: SAMSUNG ELECTRONICS CO., LTD.

Inventor： Mehmet YUCEL ,  Albert SAÀ-GARRIGA ,  Valia DIMARIDOU

IPC: G06T7/50 ,  G06V10/74 ,  G06V10/776

Abstract: Broadly speaking, this disclosure generally relates to methods, systems and apparatuses for performing monocular depth estimation, i.e. depth estimation using a single camera. In particular, this disclosure relates to a method for generating a training dataset for training a machine learning, ML, model using federated learning to perform depth estimation. Advantageously, the method to generate a training dataset enables a diverse training dataset to be generated while maintaining user data privacy. This disclosure also provides methods for training the ML model using the generated training dataset. Advantageously, the methods determine whether a community ML model that is trained by client devices needs to be retrained, and/or whether a global ML model, which is used to generate the community ML model, needs to be retrained.