Abstract: Antibodies are provided which comprise at least one Fab portion that binds CD47 and at least one Fab portion that binds the tumor associated antigen (TAA) CD20; wherein the Fab portion that binds CD47 exhibits low affinity for CD47; and, wherein the Fab portion that binds CD20 exhibits high affinity for CD20; and, wherein the antibody selectively binds CD47 and blocks CD47 interaction with SIRP? in tumor cells while exhibiting no substantial binding to CD47 in normal cells.

Abstract: An image processing method, an image processing device, a training method of a neural network, an image processing method based on a combined neural network model, a constructing method of a combined neural network model, a neural network processor, and a storage medium are provided. The image processing method includes: obtaining, based on an input image, initial feature images of N stages with resolutions from high to low, where N is a positive integer and N>2, performing, based on initial feature images of second to N-th stages, cyclic scaling processing on an initial feature image of a first stage, to obtain an intermediate feature image; and preforming merging processing on the intermediate feature image to obtain an output image. The cyclic scaling processing includes hierarchically-nested scaling processing of N?1 stages, and scaling processing of each stage includes down-sampling processing, concatenating processing, up-sampling processing, and residual link addition processing.

Abstract: The innovations provides an antimicrobial and synergistic phytochemical composition, consisting of two active ingredients (FZ and YC). This innovation also consists of formulation processes and its applications to fungi, bacteria and/or virus-caused plant diseases such as citrus Huanglongbing, grape downy mildew, strawberry gray mold etc. This innovation combines two ingredients (FZ and YC) with different modes of action, dramatically improving plant inducible acquired resistance. Therefore, this composition dramatically enhances the control efficacy of plant diseases caused by fungi, bacteria or virus, expands the antimicrobial spectrum and promotes the plant growth.

Abstract: An image processing method, an image processing device and a computer storage medium are provided. The method includes: performing a maximum-filtering processing on an original image and obtaining a luminance channel of the image; and performing a guided-filtering processing on the obtained luminance channel, and estimating and obtaining an incident component.

Abstract: The present disclosure discloses a method and device of dynamic processing of an image and a computer-readable storage medium. Based on the position data of the critical points in the original image and the target image, by unit splitting and affine transformation, the mapping relation between any two neighboring states of the initial state, the intermediate states and the ending state is determined, in turn the intermediate images formed in the intermediate states are determined and obtained based on the mapping relation and the correspondence of all of the points in the basic units, and finally the original image, the intermediate images and the target image are sequentially displayed to present a dynamic effect of the images.

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 relates to an image enhancement method. The image enhancement method may include acquiring an input image; solving an incident component of the input image that minimizes a loss function; and obtaining an optimized image of the input image based on the incident component, wherein the loss function comprises an activation function.

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: The present disclosure discloses a user interface system, electronic equipment and an interaction method for picture recognition. The electronic equipment includes a display screen, a memory and a processor, a first interface is displayed on the display screen, and the first interface includes at least one primary function classification tag, a plurality of secondary function classification tags and at least one tertiary function classification tag included in each of the secondary function classification tags; and by selecting a tertiary function classification tag, a second interface can be displayed such that a function effect corresponding to the tertiary function classification tag is experienced on the second interface. The electronic equipment can be used such that different tertiary function classification tags can be selected for experience in the first interface, can provide users with practicality, and has certain tool properties.

Abstract: The present disclosure relates to an image processing method. The image processing method may include upscaling a feature image of an input image by an upscaling convolutional network to obtain a upscaled feature image; downscaling the upscaled feature image by a downscaling convolutional network to obtain a downscaled feature image; determining a residual image between the downscaled feature image and the feature image of the input image; upscaling the residual image between the downscaled feature image and the feature image of the input image to obtain an upscaled residual image; correcting the upscaled feature image using the upscaled residual image to obtain a corrected upscaled feature image; and generating a first super-resolution image based on the input image using the corrected upscaled feature image.

Abstract: The present disclosure generally relates to the field of deep learning technologies. A cascaded system for classifying an image includes a first cascade layer including a first analysis module coupled to a first input terminal, and a first pooling module coupled to the first analysis module; a second cascade layer including a second analysis module coupled to a second input terminal, and a second pooling module coupled to the first pooling module and the second analysis module; a synthesis layer coupled to the second pooling module, and an activation layer coupled to the synthesis layer.

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: The present disclosure provides a method of training a generative adversarial network. The method includes iteratively enhancing a first noise input in a generative network to generate a first output image; iteratively enhancing a second noise input in the generative network to generate a second output image; transmitting the first output image and a second reference image to a discriminative network, the second reference image corresponding to the first reference image and having a higher resolution than the first reference image; obtaining a first score from the discriminative network based on the second reference image, and a second score from the discriminative network based on the first output image; calculating a loss function of the generative network based on the first score and the second score; and adjusting at least one parameter of the generative network to lower the loss function of the generative 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: The present disclosure generally relates to the field of deep learning technologies. An apparatus for generating a plurality of correlation images may include a feature extracting unit configured to receive a training image and extracting at least one or more of feature from the training image to generate a first feature image based on the training image; a normalizer configured to normalize the first feature image and generate a second feature image; and a shift correlating unit configured to perform a plurality of translational shifts on the second feature image to generate a plurality of shifted images, correlate each of the plurality of shifted images with the second feature image to generate the plurality of correlation images.

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 invention provides a maleate, phosphate, sulfate, hydrochloride of a cyclohexane derivative, N?-[trans-4-[2-[7-(benzo[b]thiophene)-7-piperazinyl]ethyl]cyclohexyl]-N,N-dimethylurea, as shown in Formula I and crystal forms thereof. The crystal forms have low hygroscopicity and good stability and are convenient for long-term storage and transportation; or the crystal forms have a long half-life in vivo, high bioavailability and small individual difference, and thus have obvious clinical application advantages.

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.