Sichuan University

Jiancheng Lv

Yanan Sun

Zhenan He

Quanhui Liu

Qing Ye

Mao Li

Yong Qing Wang

Chuan Liu

Dezhong Peng

Yongsheng Sang

Chenwei Tang

Xi Peng

Youcheng Huang

A method for differentiable architecture search based on a hierarchical grouping mechanism includes: obtaining a target dataset; selecting a set number of normal cells and two reduction cells; enabling the reduction cells to be located at ⅓ and ⅔ of all cells, respectively, and then concatenating all cells to form an initial search network; training the initial search network to optimize a cost function to complete a one-level search or a two-level search, wherein in the one-level search, control weight parameters are shared by the inter-group operations and are shared by the intra-group operations among the cells; and in the two-level search, the control weight parameters are shared by the inter-group operations and are not shared by the intra-group operations, obtaining normal cells and reduction cells based on the one-level search or the two-level search; and constructing a target network using the normal cells and the reduction cells.

Method for differentiable architecture search based on a hierarchical grouping mechanism

A method for generating a human-computer interactive abstract image includes: S: obtaining and preprocessing the original abstract images used as a training dataset B to obtain edge shape feature maps used as a training dataset A; S: using the training dataset A and the training dataset B as cycle generative objects of a Cycle-GAN model, and training the Cycle-GAN model to capture a mapping relationship between the edge shape feature maps and the original abstract images; S: obtaining a line shape image drawn by a user; and S: according to the mapping relationship, intercepting a generative part in the Cycle-GAN model that the dataset B is generated from the dataset A, discarding a cycle generative part and a discrimination part in the Cycle-GAN model, and generating a complete abstract image based on the line shape image to generate the human-computer interactive abstract image.

Method for generating human-computer interactive abstract image

A method for positioning key features of a lens based on ocular B-mode ultrasound images includes: acquiring and preprocessing the ocular B-mode ultrasound images to obtain a preprocessed B-mode ultrasound image, eyeball coordinates and lens coordinates; sending the preprocessed B-mode ultrasound image, the eyeball coordinates and the lens coordinates into a trained target detection network YOLOv3 to obtain eyeball position images and lens position images; substituting the eyeball position images and the lens position images into a trained feature extraction network group to obtain image features and feature coordinates corresponding to the eyeball position images and the lens position images, respectively; substituting the image features into a trained collaborative learning network to screen key image features; and marking a feature coordinate corresponding to the key image features on the ocular B-mode ultrasound images to complete positioning the key features of the lens.

Method for positioning key features of a lens based on ocular B-mode ultrasound images

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Jiancheng Lv