公开(公告)号：US12154036B2

公开(公告)日：2024-11-26

申请号：US16999118

申请日：2020-08-21

Applicant: SHENZHEN INSTITUTES OF ADVANCED TECHNOLOGY CHINESE ACADEMY OF SCIENCES

Inventor： Shuqiang Wang ,  Yanyan Shen ,  Wenyong Zhang

IPC: G06N3/088 ,  G06F18/214 ,  G06F18/241 ,  G06F18/2431 ,  G06N3/044 ,  G06N3/045 ,  G06N3/08

Abstract: The present disclosure relates to an enhanced generative adversarial network and a target sample recognition method. The enhanced generative adversarial network in the present disclosure includes at least one enhanced generator and at least one enhanced discriminator, where the enhanced generator obtains generated data by processing initial data, and provides the generated data to the enhanced discriminator; the enhanced discriminator processes the generated data and feeds back a classification result to the enhanced generator; the enhanced discriminator includes: a convolution layer, a basic capsule layer, a convolution capsule layer, and a classification capsule layer, and the convolution layer, the basic capsule layer, the convolution capsule layer, and the classification capsule layer are sequentially connected to each other.

公开(公告)号：US12254684B2

公开(公告)日：2025-03-18

申请号：US17763513

申请日：2019-11-19

Applicant: SHENZHEN INSTITUTES OF ADVANCED TECHNOLOGY CHINESE ACADEMY OF SCIENCES

Inventor： Shuqiang Wang ,  Wen Yu ,  Yanyan Shen ,  Zhuo Chen

IPC: G06V10/80 ,  G06T7/00 ,  G06V10/40 ,  G06V10/764 ,  G06V10/82 ,  G16H30/40

Abstract: The present application is suitable for use in the technical field of computers, and provides a smart diagnosis assistance method and terminal based on medical images, comprising: acquiring a medical image to be classified; pre-processing the medical image to be classified to obtain a pre-processed image; and inputting the pre-processed image into a trained classification model for classification processing to obtain a classification type corresponding to the pre-processed image, the classification model comprising tensorized network layers and a second-order pooling module. As the trained classification model comprises tensor decomposed network layers and a second-order pooling module, when processing images on the basis of the classification model, more discriminative features related to pathologies can be extracted, increasing the accuracy of medical image classification.

公开(公告)号：US11270526B2

公开(公告)日：2022-03-08

申请号：US16623397

申请日：2017-08-07

Applicant: SHENZHEN INSTITUTES OF ADVANCED TECHNOLOGY CHINESE ACADEMY OF SCIENCES ,  SHENZHEN SIBIKU TECHNOLOGY CO., LTD

Inventor： Shuqiang Wang ,  Yongcan Wang ,  Yue Yang ,  Yanyan Shen ,  Minghui Hu

IPC: G06N3/08 ,  G09B19/00 ,  G06V40/16 ,  G06V20/40 ,  G06V20/52 ,  G06V40/20

Abstract: A teaching assistance method and a teaching assistance system using said method, the teaching assistance method comprising implementing behaviour detection of students in classroom images by means of using a trained depth tensor column network model, thus providing higher image recognition precision and reducing the hardware requirements for algorithms, and being able to be used on an embedded device, reducing the usage costs of the teaching assistance method; in addition, a teaching assistance system using said teaching assistance method has the same advantages.