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公开(公告)号：US20200175362A1
公开(公告)日：2020-06-04
申请号：US16379704
申请日：2019-04-09
Applicant: Samsung Electronics Co., Ltd.
Inventor： Jie Zhang , Junting Zhang , Shalini Ghosh , Dawei Li , Jingwen Zhu
Abstract: Methods, devices, and computer-readable media for multi-task based lifelong learning. A method for lifelong learning includes identifying a new task for a machine learning model to perform. The machine learning model trained to perform an existing task. The method includes adaptively training a network architecture of the machine learning model to generate an adapted machine learning model based on incorporating inherent correlations between the new task and the existing task. The method further includes using the adapted machine learning model to perform both the existing task and the new task.
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公开(公告)号：US20210407090A1
公开(公告)日：2021-12-30
申请号：US16946504
申请日：2020-06-24
Applicant: Samsung Electronics Co., Ltd.
Inventor： Dawei Li , Wenbo Li , Hongxia Jin
Abstract: A method includes training, using at least one processor, a specialized teacher model to perform visual object instance segmentation in order to segment and classify objects in first training images. The first training images contain foreground objects without backgrounds. The method also includes training, using the at least one processor, a student model to perform visual object instance segmentation in order to segment and classify objects in second training images. The second training images contain the foreground objects and the backgrounds. Training the student model includes using selected outputs of the specialized teacher model. The method further includes deploying the trained student model to perform visual object instance segmentation in an external device.
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Patent Agency Ranking
公开(公告)号：US11775812B2
公开(公告)日：2023-10-03
申请号：US16379704
申请日：2019-04-09
Applicant: Samsung Electronics Co., Ltd.
Inventor： Jie Zhang , Junting Zhang , Shalini Ghosh , Dawei Li , Jingwen Zhu
Abstract: Methods, devices, and computer-readable media for multi-task based lifelong learning. A method for lifelong learning includes identifying a new task for a machine learning model to perform. The machine learning model trained to perform an existing task. The method includes adaptively training a network architecture of the machine learning model to generate an adapted machine learning model based on incorporating inherent correlations between the new task and the existing task. The method further includes using the adapted machine learning model to perform both the existing task and the new task.
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公开(公告)号：US11430124B2
公开(公告)日：2022-08-30
申请号：US16946504
申请日：2020-06-24
Applicant: Samsung Electronics Co., Ltd.
Inventor： Dawei Li , Wenbo Li , Hongxia Jin
Abstract: A method includes training, using at least one processor, a specialized teacher model to perform visual object instance segmentation in order to segment and classify objects in first training images. The first training images contain foreground objects without backgrounds. The method also includes training, using the at least one processor, a student model to perform visual object instance segmentation in order to segment and classify objects in second training images. The second training images contain the foreground objects and the backgrounds. Training the student model includes using selected outputs of the specialized teacher model. The method further includes deploying the trained student model to perform visual object instance segmentation in an external device.
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公开(公告)号：US10776609B2
公开(公告)日：2020-09-15
申请号：US15905609
申请日：2018-02-26
Applicant: Samsung Electronics Co., Ltd.
Inventor： Abhishek Kolagunda , Xiaolong Wang , Serafettin Tasci , Dawei Li
Abstract: One embodiment provides a method for face liveness detection. The method comprises receiving a first image comprising a face of a user, determining one or more two-dimensional (2D) facial landmark points based on the first image, and determining a three-dimensional (3D) pose of the face in the first image based on the one or more determined 2D facial landmark points and one or more corresponding 3D facial landmark points in a 3D face model for the user. The method further comprises determining a homography mapping between the one or more determined 2D facial landmark points and one or more corresponding 3D facial landmark points that are perspectively projected based on the 3D pose, and determining liveness of the face in the first image based on the homography mapping.
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公开(公告)号：US20200175384A1
公开(公告)日：2020-06-04
申请号：US16255737
申请日：2019-01-23
Applicant: Samsung Electronics Co., Ltd.
Inventor： Junting Zhang , Jie Zhang , Shalini Ghosh , Dawei Li , Serafettin Tasci , Larry Heck
Abstract: Methods, devices, and computer-readable media for incremental learning in image classification and/or object detection. A method for incremental learning includes identifying, for a model for object detection or classification, a first set of object classes the model is trained to detect or classify and adapting the model for use with a second set of object classes different from the first set of object classes to generate an adapted model. The method further includes retaining detection or classification performance on the first set of object classes in the adapted model by performing a knowledge distillation process for the model; and using the adapted model to detect or classify one or more objects from the first set of object classes and one or more objects from the second set of object classes.
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公开(公告)号：US20190266388A1
公开(公告)日：2019-08-29
申请号：US15905609
申请日：2018-02-26
Applicant: Samsung Electronics Co., Ltd.
Inventor： Abhishek Kolagunda , Xiaolong Wang , Serafettin Tasci , Dawei Li
IPC: G06K9/00
Abstract: One embodiment provides a method for face liveness detection. The method comprises receiving a first image comprising a face of a user, determining one or more two-dimensional (2D) facial landmark points based on the first image, and determining a three-dimensional (3D) pose of the face in the first image based on the one or more determined 2D facial landmark points and one or more corresponding 3D facial landmark points in a 3D face model for the user. The method further comprises determining a homography mapping between the one or more determined 2D facial landmark points and one or more corresponding 3D facial landmark points that are perspectively projected based on the 3D pose, and determining liveness of the face in the first image based on the homography mapping.
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