公开(公告)号：US20210374475A1

公开(公告)日：2021-12-02

申请号：US17402500

申请日：2021-08-13

Applicant: TENCENT TECHNOLOGY (SHENZHEN) COMPANY LIMITED

Inventor： Xinrui Zhuang ,  Yuexiang Li ,  Yefeng Zheng

IPC: G06K9/62 ,  G06K9/00 ,  G06N7/00

Abstract: An image recognition method includes: obtaining a target three-dimensional (3D) image; inputting the target 3D image to a first recognition model; and obtaining the image type of the target 3D image outputted by the first recognition model. The first recognition model is configured to perform image recognition on the target 3D image to obtain an image type of the target 3D image. A convolutional block of the first recognition model is the same as a convolutional block of a second recognition model, and configured to perform image recognition on the target 3D image. The second recognition model is obtained by training an original recognition model using a target training sample, the target training sample including cubes obtained by rotating and sorting N target cubes obtained from a 3D sample image, N being a natural number greater than 1.

公开(公告)号：US12112556B2

公开(公告)日：2024-10-08

申请号：US17402500

申请日：2021-08-13

Applicant: TENCENT TECHNOLOGY (SHENZHEN) COMPANY LIMITED

Inventor： Xinrui Zhuang ,  Yuexiang Li ,  Yefeng Zheng

IPC: G06V20/64 ,  G06F18/214 ,  G06N7/01 ,  G06V10/774 ,  G06V10/82

CPC classification number: G06V20/64 ,  G06F18/214 ,  G06N7/01 ,  G06V10/774 ,  G06V10/82

Abstract: An image recognition method includes: obtaining a target three-dimensional (3D) image; inputting the target 3D image to a first recognition model; and obtaining the image type of the target 3D image outputted by the first recognition model. The first recognition model is configured to perform image recognition on the target 3D image to obtain an image type of the target 3D image. A convolutional block of the first recognition model is the same as a convolutional block of a second recognition model, and configured to perform image recognition on the target 3D image. The second recognition model is obtained by training an original recognition model using a target training sample, the target training sample including cubes obtained by rotating and sorting N target cubes obtained from a 3D sample image, N being a natural number greater than 1.