Abstract: The present disclosure discloses a method for generating a face image, an electronic device, and a non-transitory computer-readable storage medium, the method includes: receiving a first face image and target facial expression information, and determining first facial expression information corresponding to the first face image; selecting a first reference face image matched with the first facial expression information and a second reference face image matched with the target facial expression information from a face image library; respectively extracting feature points in the first reference face image and the second reference face image, and determining face deformation information between the first reference face image and the second reference face image based on the feature points; and extracting feature points in the first face image, and generating a second face image based on the face deformation information and the feature points in the first face image.

Abstract: A computer-implemented method of recognizing a facial expression of a subject in an input image is provided. The method includes filtering the input image to generate a plurality of filter response images; inputting the input image into a first neural network; processing the input image using the first neural network to generate a first prediction value; inputting the plurality of filter response images into a second neural network; processing the plurality of filter response images using the second neural network to generate a second prediction value; weighted averaging the first prediction value and the second prediction value to generate a weighted average prediction value; and generating an image classification result based on the weighted average prediction value.

Abstract: A computer-implemented method using a convolutional neural network is provided. The computer-implemented method includes processing an input image through the convolutional neural network to generate an output image including content features of the input image morphed with style features of a style image. The convolutional neural network includes a feature extraction sub-network, a morpher, and a decoder sub-network.

Abstract: The present disclosure discloses a convolutional neural network processor, an image processing method and an electronic device. The method includes: receiving, by the first convolutional unit, the input image to be processed, extracting the N feature maps with different scales in the image to be processed, sending the N feature maps to the second convolutional unit, and sending the first feature map to the processing unit; fusing, by the processing unit, the received preset noise information and the first feature map, to obtain the second feature map, and sending the second feature map to the second convolutional unit; and fusing, by the second convolutional unit, the received N feature maps with the second feature map to obtain the processed image.

Abstract: Disclosed are an image processing method and device, a training method of a neural network and a storage medium. The image processing method includes: obtaining an input image, and processing the input image by using a generative network to generate an output image. The generative network includes a first sub-network and at least one second sub-network, and the processing the input image by using the generative network to generate the output image includes, processing the input image by using the first sub-network to obtain a plurality of first feature images; performing a branching process and a weight sharing process on the plurality of first feature images by using the at least one second sub-network to obtain a plurality of second feature images; and processing the plurality of second feature images to obtain the output image.

Abstract: An image processing method is disclosed. The image processing method may include inputting a first image and a third image to a pre-trained style transfer network model, the third image being a composited image formed by the first image and a second image; extracting content features of the third image and style features of the second image, normalizing the content features of the third image based on the style features of the second image to obtain target image features, and generating a target image based on the target image features and outputting the target image by using the pre-trained style transfer network model.

Abstract: A computer-implemented method using a convolutional neural network is provided. The computer-implemented method includes processing an input image through the convolutional neural network to generate an output image including content features of the input image morphed with style features of a style image. The convolutional neural network includes a feature extraction sub-network, a morpher, and a decoder sub-network.

Abstract: Disclosed is a two-dimensional code image generation method and apparatus, a storage medium and an electronic device related to the field of two-dimensional code image technology. The method includes obtaining an initial two-dimensional code image and a background image, and performing structured processing on the initial two-dimensional code image according to the background image to obtain a structured two-dimensional code image, performing mode transfer processing on the background image to obtain a background image of a target mode by a mode transfer model, and performing a fusion operation on the structured two-dimensional code image and the background image of the target mode to obtain a target two-dimensional code image.

Abstract: A computer-implemented method using a convolutional neural network is provided. The computer-implemented method using a convolutional neural network includes processing an input image through at least one channel of the convolutional neural network to generate an output image including content features of the input image morphed with style features of a reference style image. The at least one channel includes a down-sampling segment, a densely connected segment, and an up-sampling segment sequentially connected together. Processing the input image through the at least one channel of the convolutional neural network includes processing an input signal through the down-sampling segment to generate a down-sampling segment output; processing the down-sampling segment output through the densely connected segment to generate a densely connected segment output; and processing the densely connected segment output through the up-sampling segment to generate an up-sampling segment output.

Abstract: The present disclosure provides a detection system and a detection method for intravenous injection leakage. The detection system for intravenous injection leakage, includes a detection module, configured to detect a status parameter of a skin at an acupuncture point; and an analysis module, configured to acquire the status parameter, and to determine whether the intravenous injection leakage happens or not depending on the status parameter.

Abstract: The present invention discloses a Fire-fighting water cannon comprising: a first motor assembly including a first housing, a first stator assembly, a first rotor assembly and a central shaft; both ends of the first housing are respectively provided with a first end cover and a second end cover, the first end of the first stator assembly is connected with the second end cover, the first rotor assembly is sleeved on the outside of the first stator assembly, and the first rotor assembly is rotatably connected with the first housing; the cannon head assembly including a first cannon head and a second cannon head, the first cannon head is connected with the first stator assembly, the second cannon head is sleeved on the outside of the first cannon head, and the second cannon head is threadedly connected with the first rotor assembly, the first cannon head is provided with a first anti-rotation device; the cannon core assembly is arranged on the central axis and cooperates with the cannon head assembly.

Abstract: The present disclosure provides an image segmentation apparatus, method and relevant computing device. The image segmentation apparatus comprises: a feature extractor configured to extract N image semantic features having different scales from an input image, where N is an integer not less than 3; and a feature processor comprising cascaded dense-refine networks and being configured to perform feature processing on the N image semantic features to obtain a binarized mask image for the input image. A dense-refine network is configured to generate a low-frequency semantic feature from semantic features input thereto by performing densely-connected convolution processing on the semantic features respectively to obtain respective image global features, performing feature fusion on the image global features to obtain a fused image global feature, and performing pooling processing on the fused image global feature to generate and output the low-frequency semantic feature.

Abstract: A method and a system for converting a font of a Chinese character in an image, a computer device and a medium are disclosed. A specific implementation of the method includes: acquiring a stroke of a to-be-converted Chinese character in the image and spatial distribution information of the stroke; and generating a Chinese character in a target font that corresponds to the to-be-converted Chinese character in the image according to the stroke of the to-be-converted Chinese character, the spatial distribution information of the stroke and standard stroke information of the target font, to replace the to-be-converted Chinese character.

Abstract: A neural network is provided. The neural network includes 2n number of sampling units sequentially connected; and a plurality of processing units. A respective one of the plurality of processing units is between two adjacent sampling units of the 2n number of sampling units. A first sampling unit to an n-th sample unit of the 2n number of sampling units are DeMux units. A respective one of the DeMux units is configured to rearrange pixels in a respective input image to the respective one of the DeMux units following a first scrambling rule to obtain a respective rearranged image. An (n+1)-th sample unit to a (2n)-th sample unit of the 2n number of sampling units are Mux units. A respective one of the Mux units is configured to combing respective m? number of input images to the respective one of the Mux units to obtain a respective combined image.

Abstract: A neural network is provided. The neural network includes 2n number of sampling units sequentially connected; and a plurality of processing units. A respective one of the plurality of processing units is between two adjacent sampling units of the 2n number of sampling units. A first sampling unit to an n-th sample unit of the 2n number of sampling units are DeMux units. A respective one of the DeMux units is configured to rearrange pixels in a respective input image to the respective one of the DeMux units following a first scrambling rule to obtain a respective rearranged image. An (n+1)-th sample unit to a (2n)-th sample unit of the 2n number of sampling units are Mux units. A respective one of the Mux units is configured to combine respective m? number of input images to the respective one of the Mux units to obtain a respective combined image.

Abstract: This disclosure relates to techniques for performing radio link monitoring and cell searching when moving at high speeds. A movement speed of a wireless device may be determined. When performing a cell search, the manner of the cell search may depend on the movement speed of the wireless device, potentially including cells that are associated with high movement speed being more highly prioritized when the wireless device is determined to be at a higher movement speed than when the wireless device is determined to be at a lower movement speed.

Abstract: An image processing method implemented by a computing device is described herein, which includes acquiring an image to be processed and a target style of image, the image to be processed being an image of a second resolution level, and inputting the image to be processed and the target style into a trained image processing neural network for image processing to obtain a target image of the target style, the target image being an image of a first resolution level. The resolution of the image of the first resolution level is higher than that of the image of the second resolution level.

Abstract: A trash sorting and recycling method, a trash sorting device and a trash sorting and recycling system are provided. The trash sorting and recycling method includes: acquiring a detection image of trash to be sorted; processing the detection image with a deep learning neural network to judge whether or not the trash to be sorted belongs to recyclable trash; if yes, sending a first control signal, to control to deliver the trash to be sorted into a recycling region; if no, sending a second control signal, to control to deliver the trash to be sorted into a non-recycling region.

Abstract: A computer-implemented method of recognizing a facial expression of a subject in an input image is provided. The method includes filtering the input image to generate a plurality of filter response images; inputting the input image into a first neural network; processing the input image using the first neural network to generate a first prediction value; inputting the plurality of filter response images into a second neural network; processing the plurality of filter response images using the second neural network to generate a second prediction value; weighted averaging the first prediction value and the second prediction value to generate a weighted average prediction value; and generating an image classification result based on the weighted average prediction value.

Abstract: The present disclosure relates to a method, an apparatus and a device for converting a style of an image, wherein the method comprises: acquiring a luminance component (Y) and chrominance components (U, V) in a YUV space of an image to be processed; performing a group convolution processing on the luminance component (Y) and the chrominance components (U, V) in the YUV space of the image to be processed to obtain content features and style features of the image to be processed; and performing a fusion processing on the content features, the style features and target style features of the image to be processed to convert the image to be processed into an image of a target style.