Abstract: The present disclosure relates to a method for training a natural language processing model, a text processing method and an apparatus, where the method for training a natural language processing model includes: obtaining a sample text; determining a triple in the sample text, where the triple includes two entities in the sample text and a relation between the two entities; processing the sample text based on the triple to obtain a knowledge fusion vector; and inputting the knowledge fusion vector into the natural language processing model for training to obtain a target model. The sample text may be processed based on the triple in the sample text to obtain a knowledge fusion vector. Compared with the sample text, the knowledge fusion vector contains other triple information besides the sample text information itself, so that a computer obtains the true meaning of the sample text more accurately.

Abstract: The present disclosure provides a training method and apparatus for an image-text matching model, a device and a storage medium. The method includes: acquiring a positive sample and a negative sample; where the positive sample includes text and an image, the text in the positive sample is used to describe content of the image in the positive sample; the negative sample includes text and an image, the text in the negative sample describes content that is inconsistent with content of the image in the negative sample; training the image-text matching model by using the acquired positive sample and the acquired negative sample based on a manner of contrastive learning; where the image-text matching model is used to predict, for an input image and input text, whether the input text is used to describe content of the input image.

Abstract: A model training method includes: acquiring a sample set including a plurality of sample groups; the sample group includes an original image sample and original text samples; performing mask processing on the original image sample and the original text samples to generate a mask image sample and mask text samples; using the mask image sample and the mask text samples to perform adversarial training on a generator and a discriminator to obtain a target model; the generator includes a feature extraction network and an output network, the feature extraction network is used to perform feature extraction after information fusion of an input image and an input text of the generator.

Abstract: The present disclosure provides a tunable antenna control method and apparatus, and a tunable antenna system. The tunable antenna control method includes: acquiring a beam pointing angle of a tunable antenna, calculating a phase-configuration parameter according to the beam pointing angle through a parameter calculation model, where the parameter calculation model is an artificial intelligence model taking the beam pointing angle as an input and the phase-configuration parameter of a phase shifter as an output, and controlling the phase shifter of the tunable antenna to perform phase configuration according to the phase-configuration parameter outputted by the parameter calculation model.

Abstract: The present disclosure relates to a method for training a natural language processing model, including: obtaining a sample text of natural language; determining one or more triples in the sample text, wherein each of the triples comprises two entities in the sample text and a relation between the two entities; processing the sample text based on the triples to obtain one or more knowledge fusion vectors; and training a natural language processing model by inputting the knowledge fusion vectors into the natural language processing model to obtain a target model.

Abstract: Disclosed are an asset value evaluation method and apparatus, a model training method and apparatus, and a readable storage medium. The asset value evaluation method includes: acquiring input asset value query information for a user; when it is determined that there is historical asset interaction information of the user, determining an asset set obtained by means of making a query using the asset value query information, the asset set includes at least one asset; performing embedding representation on each asset, so as to determine an asset embedding vector of each asset, the asset embedding vector is obtained by means of training based on the relationship between each asset and an attribute, and the attribute is used for representing an inherent parameter of the asset; and inputting the asset embedding vector of each asset into a graph convolutional network model to obtain the value of each asset for the user.

Abstract: Provided in the present disclosure are a text recommendation method and apparatus, a model training method and apparatus, and a readable storage medium. The text recommendation method includes: acquiring text retrieval information from a user; when it is determined that there is historical text retrieval information for the user, determining text information of each text in a text set retrieved by using the text retrieval information; performing embedded representation on the text information of each text based on a self-attention model, and determining a text embedding vector of each text; inputting the text embedding vector of each text into a trained graph convolutional network model, to obtain the probability of interaction between the user and each text in the text set; and screening out, from the text set, target text which meets a preset interaction probability, and recommending the target text to the user.

Abstract: The present disclosure relates to a method for natural language processing, a method of training a natural language processing model, an electronic device, and a non-transitory computer-readable storage medium, and relates to the technical field of natural language processing. The method for natural language processing includes: acquiring text data; and processing the text data by using a natural language processing model to obtain output information, wherein the natural language processing model comprises a first attention model, the first attention model comprising a sequential coding matrix for adding, on the basis of the text data, sequential relation information between at least one word and other words in the text data.

Abstract: Provided in the present disclosure are a text recognition method, and a model and an electronic device, which are applied to a mode in which primary classification is first performed from different dimensions, and secondary classification is then performed, such that the meaning of text is analyzed from different dimensions, thereby improving the accuracy of text recognition. The method includes: acquiring text to be recognized, and performing primary classification on the text to obtain a plurality of text features, wherein the primary classification is used for performing feature extraction on the text from different dimensions, and there are differences between features extracted from the different dimensions (100); splicing the plurality of text features, so as to obtain spliced features (101); and performing secondary classification on the spliced features to obtain a text category corresponding to the text, wherein the secondary classification is used for classifying the spliced features (102).

Abstract: The present disclosure relates to a meter recognition method, which includes: determining embedded features of pixels in a target image of a meter, and encoding position information of the pixels to obtain encoded position features; inputting superimposed features obtained by superimposing the encoded position features and the embedded features into an encoder of a target model; wherein an input of the target model includes the labels, and an output of the target model includes coordinates of key points in a sample image of the meter. According to the present disclosure, the image of the meter can be processed by the trained target model, the coordinates of key points in the target image of the meter are outputted. It can reduce manual operations and improve efficiency, and on the other hand, it can avoid possible misoperations during manual operations, which is beneficial for improving accuracy.

Abstract: Provided are a method and device for recommending goods, a method and device for training a goods knowledge graph, and a method and device for training a model. The method for training a goods knowledge graph includes: constructing an initial goods knowledge graph based on a first type of triples and a second type of triples, where a format of the first type of triples is head entity-relation-tail entity, and a format of the second type of triples is entity-attribute-attribute value (S101); and training the initial goods knowledge graph based on a graph embedding model to obtain embedding vectors of entities in the trained goods knowledge graph (S102).

Abstract: The present disclosure relates to an image recognition method and system for a display panel, a training method, and an electronic device and a non-volatile computer-readable storage medium. The image recognition method includes: acquiring an image of a display panel, wherein the image includes gate lines extending in a first direction and data lines extending in a second direction, the gate lines and the data lines intersecting to define a plurality of sub-pixel regions, and the image further includes a defect pattern; and recognizing the defect pattern in the image by using an image recognition model to obtain defect information, wherein the defect information includes at least one of a defect type or a defect position of the defect pattern, the image recognition model comprises a first attention model configured to learn a weight proportion of a feature of the defect pattern in the image.

Abstract: The present disclosure provides an image processing method, apparatus, device, and computer-readable storage medium. The method includes: obtaining an image dataset, the image dataset including an image and an accompanying text related to an unseen class in the image; and generating a probability and/or distribution of the unseen class using an unseen class obtaining model, the probability and/or distribution of the unseen class including a probability that each pixel in the image is from the unseen class, a probability that the unseen class is present in the image, and a regional probability after the image is subdivided into a plurality of regions.

Abstract: An image processing method and device, an image detecting method and system, and a storage medium are provided. The image processing method comprises: acquiring a target image; counting pixels of the target image to obtain a count value; adding compensation data at a peripheral position of an edge pixel of the target image according to the count value, to obtain pixel data of the compensated target image.

Abstract: An electronic apparatus and a method for displaying an image on a display device are disclosed. The electronic apparatus includes a display device; an image acquisition device configured to acquire a surrounding image of the display device; and a processor configured to: determine a background image of the display device according to the surrounding image; acquire a target range and a target object in the background image; determine a target image according to the background image, the target range and the target object; and control the display device to display the target image, wherein the target image excludes the target object.

Abstract: A knowledge graph updating method, an apparatus, an electronic device, a storage medium and a program thereof, relates to the field of computer technology. The method includes: receiving an updating request of a technical knowledge graph, the updating request includes: a technical-event node of the technical knowledge graph; extracting historical event information corresponding to the technical-event node from the technical knowledge graph; and updating event information of the technical-event node after a current time by using a target-technical-event prediction model; wherein, the target-technical-event prediction model is obtained by training a technical-event prediction model with the historical event information.

Abstract: An image processing model generation method includes: inputting at least one training sample lesion image into an initial image processing model, the initial image processing model including a classification layer and a marking layer; calling the classification layer; calling the marking layer; obtaining a loss value of the at least one training sample lesion image in the initial image processing model; determining whether the loss value is within a preset range; if not, updating parameters of the initial image processing model, an image processing model with updated parameters being used as an initial image processing model in next training; and repeating above steps until the loss value is within the preset range.

Abstract: The present disclosure provides a method and apparatus of labeling a target in an image, and a computer recording medium. The method includes: acquiring a first neural network, the first neural network includes a multi-layer convolutional neural network and a fully connected layer, wherein each layer of the multi-layer convolutional neural network includes a convolutional layer, an activation function layer and a down-sampling layer arranged successively; processing the image by using the multi-layer convolutional neural network of the first neural network acquired so as to obtain a target position mask for the image; and labeling the target in the image based on the target position mask.

Abstract: Disclosed are an electronic apparatus, and a method for displaying an image on a display device. The electronic apparatus comprises: a display device; an image acquisition device, which is configured to acquire a surrounding image of the display device; and a processor, which is configured to determine a background image of the display device according to the surrounding image, acquire a target range, and a target object in the background image, determine a target image according to the background image, the target range and the target object, and control the display device to display the target image, wherein the target image does not include the target object.

Abstract: It is disclosed a method, an apparatus, a computing device, and a computer-readable storage medium for identifying a signal. The method includes demodulating a modulated signal to generate a transmission signal, transmitting the transmission signal, receiving an echo signal generated by a reflection of the transmission signal, demodulating the echo signal to obtain demodulated information, identifying the demodulated information by using a target network model to obtain an identification result of the echo signal, and outputting the identification result to a graphical user interface for display.