公开(公告)号：US20200042829A1

公开(公告)日：2020-02-06

申请号：US16596938

申请日：2019-10-09

Applicant: Huawei Technologies Co., Ltd.

Inventor： Yasheng Wang ,  Yang Zhang ,  Shuzhan Bi ,  Youliang Yan

IPC: G06K9/62

Abstract: A classification model training method includes obtaining a positive training set and a first negative training set, where the positive training set includes samples of a positive sample set in a corpus, where the first negative training set includes samples of an unlabeled sample set in the corpus, training, using the positive training set and the first negative training set, to obtain a first classification model, determining, using the first classification model, a pseudo negative sample in the first negative training set, removing the pseudo negative sample from the first negative training set, updating the first negative training set to a second negative training set, and training, using the positive training set and the second negative training set, to obtain a target classification model.

公开(公告)号：US20240265572A1

公开(公告)日：2024-08-08

申请号：US18614676

申请日：2024-03-24

Applicant: HUAWEI TECHNOLOGIES CO., LTD.

Inventor： Qi Cao ,  Di Zhang ,  Shuzhan Bi

IPC: G06T7/73 ,  G06T7/564 ,  G06T7/593

CPC classification number: G06T7/74 ,  G06T7/564 ,  G06T7/593 ,  G06T2207/10012 ,  G06T2207/10024 ,  G06T2207/10028 ,  G06T2207/20081 ,  G06T2207/30252

Abstract: In a depth estimation method, a depth estimation apparatus obtains a first color image, and inputs the first color image into a first depth estimation model to obtain a first intermediate depth image. The depth estimation apparatus then inputs the first color image and the first intermediate depth image into a second depth estimation model to obtain a first target depth image. The second depth estimation model is obtained through training based on a color image and a target depth image corresponding to the color image, and the first depth estimation model is obtained through training based on the color image and an intermediate depth image corresponding to the color image.

公开(公告)号：US20200073665A1

公开(公告)日：2020-03-05

申请号：US16677816

申请日：2019-11-08

Applicant: Huawei Technologies Co., Ltd.

Inventor： Jun Yao ,  Yasuhiko Nakashima ,  Tao Wang ,  Wei Zhang ,  Zuqi Liu ,  Shuzhan Bi

IPC: G06F9/38 ,  G06F9/30 ,  G06F9/48 ,  G06F9/54 ,  G06F13/16 ,  G06F12/0862

Abstract: A method for accessing a memory of a multi-core system, a related apparatus, a system, and a storage medium involve obtaining data from a system memory according to a prefetch instruction, sending a message to a core that carries the to-be-accessed data. Each segment of data is stored in an intra-core cache based on the prefetch instruction.

公开(公告)号：US11294675B2

公开(公告)日：2022-04-05

申请号：US16677816

申请日：2019-11-08

Applicant: Huawei Technologies Co., Ltd.

Inventor： Jun Yao ,  Yasuhiko Nakashima ,  Tao Wang ,  Wei Zhang ,  Zuqi Liu ,  Shuzhan Bi

IPC: G06F9/312 ,  G06F9/30 ,  G06F12/0862 ,  G06F13/16

Abstract: A method for accessing a memory of a multi-core system, a related apparatus, a system, and a storage medium involve obtaining data from a system memory according to a prefetch instruction, and sending a message to a core that carries the to-be-accessed data. Each segment of data is stored in an intra-core cache based on the prefetch instruction.

公开(公告)号：US11151182B2

公开(公告)日：2021-10-19

申请号：US16596938

申请日：2019-10-09

Applicant: Huawei Technologies Co., Ltd.

Inventor： Yasheng Wang ,  Yang Zhang ,  Shuzhan Bi ,  Youliang Yan

IPC: G06F16/36 ,  G06F40/237 ,  G06K9/62 ,  G06N20/00 ,  G06N3/04 ,  G06N20/10 ,  G06F40/00

Abstract: A classification model training method includes obtaining a positive training set and a first negative training set, where the positive training set includes samples of a positive sample set in a corpus, where the first negative training set includes samples of an unlabeled sample set in the corpus, training, using the positive training set and the first negative training set, to obtain a first classification model, determining, using the first classification model, a pseudo negative sample in the first negative training set, removing the pseudo negative sample from the first negative training set, updating the first negative training set to a second negative training set, and training, using the positive training set and the second negative training set, to obtain a target classification model.