Wuyi University

Junying Gan

Yikui Zhai

Wenbo Deng

Junying Zeng

Qirui Ke

Ying Xu

Zilu Ying

Tianlei Wang

Chuanbo Qin

Wenlue Zhou

Chaoyun Mai

Guohui He

Yihang Zhi

Cuilin Yu

He Cao

Yanyang Liang

Yueting Wu

Yu Zheng

Jihua Zhou

Zhenfeng Bai

Fan Wang

Wenlve Zhou

Huixin Guan

Boyuan Zhu

Xi Wu

Jinxin Wang

Zhiyong Hong

Jingming Zhu

Zhongxin Yu

Li Xiang

Bicheng Wu

A multi-dimensional task facial beauty prediction method and system, and a storage medium are disclosed. The method includes the steps of: at a training phase, using first facial images to optimize a shared feature extraction network for extracting shared features and to train a plurality of sub-task networks for performing facial beauty classification tasks; at a testing phase, extracting shared features of second facial images; inputting the shared features to the trained plurality of sub-task networks; and obtaining a first beauty prediction result based on first output results of the plurality of sub-task networks.

Multi-dimensional task facial beauty prediction method and system, and storage medium

Disclosed are a method and device for predicting face beauty based on migration and weak supervision and a storage medium. The method includes: preprocessing an inputted face image; training a source domain network by using the preprocessed image, and migrating a parameter of the source domain network to a target domain network; inputting a noise image marked with a noise label and a truth-value image marked with a truth-value label into the target domain network to obtain an image feature; and inputting the image feature into a classification network to obtain a final face beauty prediction result.

Method and device for predicting beauty based on migration and weak supervision, and storage medium

A beauty prediction method and device based on multitasking and weak supervision, and a storage medium are disclosed. The method includes the steps of pre-processing inputted facial images; allocating the pre-processed images to multiple tasks; extracting shared image features; and obtaining a plurality of classification results via a plurality of classification networks each including a residual network, a standard neural network and a classifier.

Beauty prediction method and device based on multitasking and weak supervision, and storage medium

Disclosed are a facial beauty prediction method and device based on multi-task migration. The method includes: performing similarity measurement based on a graph structure on a plurality of tasks to obtain an optimal combination of the plurality of tasks; constructing a facial beauty prediction model including a feature sharing layer based on the optimal combination; migrating feature parameters of an existing large-scale facial image network to the feature sharing layer of the facial beauty prediction model; inputting facial images for training to pre-train the facial beauty prediction model; and inputting a facial image to be tested to the trained facial beauty prediction model to obtain facial recognition results.

Facial beauty prediction method and device based on multi-task migration

A method for measuring an antenna downtilt angle based on a multi-scale deep semantic segmentation network is disclosed, including: collecting base station antenna data by using an unmanned aerial vehicle, and labeling an acquired antenna image with a labeling tool to make a data set; calling the data set for training and debugging a model; recognizing and detecting a target antenna, performing semantic segmentation on an output image, finally obtaining a target image finally segmented, and calculating a downtilt angle of the target image. The method is highly applicable, cost-effective, and safe.

Method for measuring antenna downtilt angle based on multi-scale deep semantic segmentation network

Disclosed are a method and apparatus for SAR image data enhancement, and a storage medium. The method includes: processing an SAR target image by electromagnetic simulation to acquire an SAR electromagnetic simulation image; and processing the SAR electromagnetic simulation image and the SAR target image by a generative adversarial network to obtain a set of virtual samples of the SAR target image.

Method and apparatus for SAR image data enhancement, and storage medium

Disclosed are a method and an apparatus for end-to-end SAR image recognition, and a storage medium. According to the disclosure, a generative adversarial network is used to enhance data and improve data richness of a SAR image, which is beneficial to subsequent network training; a semantic feature enhancement technology is also introduced to enhance semantic information of a SAR deep feature by a coding-decoding structure, which improves performances of SAR target recognition; and meanwhile, an end-to-end SAR image target recognition model with high integrity for big scenes like the Bay Area is constructed, which is helpful to improve a synthetic aperture radar target recognition model for big scenes like the Bay Area from local optimum to global optimum, increases the stability and generalization ability of the model, reduces the network complexity, and improves the target recognition accuracy.

Method and apparatus for end-to-end SAR image recognition, and storage medium

Disclosed is a method for vein recognition, the method includes: performing a difference operation and a channel connection on two to-be-verified target vein images respectively to obtain a difference image and a two-channel image of the two target vein images; performing the channel connection on the obtained difference image and two-channel image to obtain a three-channel image, so as to use the three-channel image as an input of a CNN network; fine-tuning a pre-trained model SqueezeNet that completes training on an ImageNet; integrating the difference image and the three-channel image through a cascade optimization framework to obtain a recognition result; regarding a pair of to-be-verified images as a sample, transforming the sample, taking the transformed sample as the input of the CNN network, obtaining a recognition result by supervised training on the network.

Method for vein recognition, and apparatus, device and storage medium thereof

The present disclosure discloses an error modeling method and device for prediction context of reversible image watermarking. A predictor based on omnidirectional context is established; then, the prediction context is self-adaptively error modeled to obtain a self-adaptive error model; and finally, output data from the self-adaptive error model is fed back to the predictor to update and correct the prediction context, so as to correct a prediction value of a current pixel x[i,j]. Since the non-linear correlation between the current pixel and the prediction context thereof, i.e., the non-linear correlation redundancy between pixels can be found by the error modeling of the prediction context of the predictor, the non-linear correlation redundancy between the pixels can be effectively removed. Thus, the embeddable watermarking capacity can be increased.

Error modeling method and device for prediction context of reversible image watermarking

A double attention network (DANet)-based drone patrol and inspection system for coastline floating garbage, including: an image acquisition module configured to shoot a video of a coastline in need of patrol and inspection by using a drone, and obtain an image from the video; a feature extraction module configured to extract shallow features and deep features, fuse the shallow features and the deep features to obtain a shared feature, and finally output a panoramic recognition result; a network training module configured to perform training on the labeled image so that the network can recognize the coastline and floating garbage; and a path correction module configured to adjust a flying direction of the drone.

DANet-based drone patrol and inspection system for coastline floating garbage

A method for measuring an antenna downtilt angle based on a deep instance segmentation network is disclosed, including: shooting an omni-directional antenna video using a drone; and transmitting the antenna video to a server in real time, and the server measuring an antenna downtilt angle in real time using a deep learning algorithm, the deep learning algorithm includes: a feature extraction network module for acquiring an antenna feature image; an instance segmentation module for binary segmentation of an antenna image and the background to distinguish antenna image pixels from background pixels; an antenna candidate box module for identifying and detecting the antenna image, acquiring an antenna candidate box and determining an antenna calibration box therefrom; and an antenna downtilt angle measuring module for measuring an antenna downtilt angle.

Method for measuring antenna downtilt angle based on deep instance segmentation network

Disclosed is a method for measuring an antenna downtilt based on a multi-scale detection algorithm, including: capturing an image of an antenna using an unmanned aerial vehicle, and returning data to a server in real time; obtaining a ground truth box where the antenna is located by performing the multi-scale detection algorithm of a server; segmenting the ground truth box based on an antenna target segmentation algorithm of the server; and obtaining an antenna downtilt angle based on an antenna downtilt measurement algorithm of the server and determining whether the antenna properly functions. This method avoids the danger of tower worker climbing, is fast and accurate, saves labor costs and time, and ensures the measurement of an antenna downtilt to be smoother.

Method for measuring antenna downtilt based on multi-scale detection algorithm

A method for predicting a face beauty grade includes the following steps of: acquiring a beautiful face image of a face beauty database, preprocessing the beautiful face image, and extracting a beauty feature vector of the beautiful face image, the preprocessing unifying data of the beautiful face image; recognizing continuous features of samples of the same type in a feature space by using a bionic pattern recognition model, and classifying the beauty feature vector to obtain a face beauty grade prediction model; and collecting a face image to be recognized, and inputting the face image to be recognized into the face beauty grade prediction model to predict a face beauty grade and obtain the beauty grade of the face image to be recognized.

Method and apparatus for predicting face beauty grade, and storage medium

A method and system for mobile communication base station antenna measurement is disclosed. The method comprises steps of: acquiring a set of images containing antennas of a base station; processing the set of images with a model based on instance segmentation network, and generating visualized images corresponding to the set of images of antennas; calculating, from the visualized images, the quantity of antennas of the base station and separating data for each antenna; measuring parameters of each antenna by data fitting. The system comprises a processor and a memory storing program instructions thereon, the program instructions executable by the processor to cause the system to perform the steps of the method.

Method and system of antenna measurement for mobile communication base station

Disclosed are a method and an apparatus for multi-scale SAR image recognition based on attention mechanism. According to the method, a whole image recognition network is adjusted by training a SAR training image by an attention prediction subnet, a region-of-interest positioning subnet and an image classification subnet in combination with a network loss, which greatly improves a network performance; and in addition, an attention prediction map is generated by attention mechanism to position a most prominent feature part in the SAR image, which greatly eliminates a redundancy of image features in a machine vision, effectively determines a region-of-interest, reduces interference of image noises, greatly reduces an image processing time, improves a target recognition accuracy, is beneficial to next target positioning, and has a significant improvement on a network recognition speed integrally.

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Jiangmen, China

Junying Gan