Abstract: A facial beauty prediction method includes: acquiring an original image and Gaussian noise; generating a fake facial image according to the Gaussian noise; respectively performing a determination on the fake facial image and the original image to obtain a first probability and a second probability; optimizing a generative adversarial network when a difference between the first probability and the second probability is greater than a preset threshold; generating a training set by using the optimized generative adversarial network; and inputting the training set into a facial beauty prediction task network and training the facial beauty prediction task network to obtain a trained first task network.

Abstract: A face scoring model training method, a face scoring method, and related devices are disclosed. The training method includes generating pseudo-labels based on first images annotated with scoring labels, and assigning the pseudo-labels to second images without annotated scoring labels to obtain third images; obtaining a first scoring result based on the first images and third images; performing feature extraction based on the first scoring result and the first features to obtain second features, and performing feature fusion based on the second features to obtain fused features; obtaining a second scoring result based on the fused features; extracting multi-scale features of the face images for training, and obtaining a third scoring result based on the third features, the second scoring result, and the multi-scale features; and obtaining a trained face scoring model based on the first scoring result, the second scoring result, and the third scoring result.

Abstract: A multi-task facial beauty prediction method and apparatus, device, and medium are disclosed. The method includes: training a multi-task teacher model according to a facial image to obtain a first model; performing regularization on the first model according to a beauty grade label and a noise label to obtain a second model; performing cooperative teaching on a plurality of second models to obtain a target model parameter; substituting the target model parameter into a multi-task student model to obtain the target model; and performing facial beauty prediction processing using the target model to obtain a facial beauty prediction result.

Abstract: Embodiments of the disclosure discloses method and apparatus for facial beauty prediction, device and storage medium. The method includes the steps of classifying a training set into a plurality of first images with noise labels and a plurality of second images with non-noise labels; performing a re-weighting processing on the plurality of the second images; training a first model by using a target training set to obtain a second model; labeling image data by using the second model to obtain first data with labels and second data without labels; generating third data with pseudo labels through a classifier by using the second data; training the classifier according to the first data, the second data and the third data; processing an image to be predicted through a model with a target classifier to obtain a facial beauty prediction result.

Abstract: Embodiments of the disclosure discloses method and apparatus for facial beauty prediction, device and storage medium. The method includes the steps of classifying a training set into a plurality of first images with noise labels and a plurality of second images with non-noise labels; performing a re-weighting processing on the plurality of the second images; training a first model by using a target training set to obtain a second model; labeling image data by using the second model to obtain first data with labels and second data without labels; generating third data with pseudo labels through a classifier by using the second data; training the classifier according to the first data, the second data and the third data; processing an image to be predicted through a model with a target classifier to obtain a facial beauty prediction result.

Abstract: An array-type facial beauty prediction method, a device and a storage medium are disclosed. The method includes: extracting a plurality of facial beauty features of different scales from a face image by means of a plurality of feature extractors; performing array-type fusion on the plurality of facial beauty features of different scales to obtain a plurality of fused features; performing binary classification processing on the plurality of fused features multiple times by means of a facial beauty classification network to obtain a plurality of classification results, where the facial beauty classification network is obtained by means of supervised training using a cost-sensitive loss function, and the cost-sensitive loss function is a loss function that is set according to cost-sensitive training labels; and making a decision on the basis of the plurality of classification results to obtain a facial beauty prediction result.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.