Abstract: Methods, systems, apparatus, and non-transitory computer readable storage media for image processing are provided. In one aspect, an image processing method includes: generating a garment deformation template image and a first human body template image based on a first semantic segmentation image of a human body in a first image and a target garment image, generating a target garment deformation image by performing deformation on the target garment image based on the garment deformation template image, obtaining a second human body template image by adjusting the first human body template image based on a second semantic segmentation image of the human body in the first image and the garment deformation template image, and transforming the first image into a second image including the human body wearing a target garment based on the target garment deformation image, the second human body template image, and the garment deformation template image.

Abstract: A semantic segmentation model training method includes: performing, by a semantic segmentation model, image semantic segmentation on at least one unlabeled image to obtain a preliminary semantic segmentation result as the category of the unlabeled image; obtaining, by a convolutional neural network based on the category of the at least one unlabeled image and the category of at least one labeled image, sub-images respectively corresponding to the at least two images and features corresponding to the sub-images, where the at least two images comprise the at least one unlabeled image and the at least one labeled image, and the at least two sub-images carry the categories of the corresponding images; and training the semantic segmentation model on the basis of the categories of the at least two sub-images and feature distances between the at least two sub-images.

Abstract: Methods, systems, apparatus, and non-transitory computer readable storage media for image processing are provided. In one aspect, an image processing method includes: generating a garment deformation template image and a first human body template image based on a first semantic segmentation image of a human body in a first image and a target garment image, generating a target garment deformation image by performing deformation on the target garment image based on the garment deformation template image, obtaining a second human body template image by adjusting the first human body template image based on a second semantic segmentation image of the human body in the first image and the garment deformation template image, and transforming the first image into a second image including the human body wearing a target garment based on the target garment deformation image, the second human body template image, and the garment deformation template image.

Abstract: A method and an apparatus for segmenting a video object, an electronic device, a storage medium, and a program include: performing, among at least some frames of a video, inter-frame transfer of an object segmentation result of a reference frame in sequence from the reference frame, to obtain an object segmentation result of at least one other frame among the at least some frames; determining other frames having lost objects with respect to the object segmentation result of the reference frame among the at least some frames; using the determined other frames as target frames to segment the lost objects, so as to update the object segmentation results of the target frames; and transferring the updated object segmentation results of the target frames to the at least one other frame in the video in sequence. The accuracy of video object segmentation results can therefore be improved.

Abstract: Target matching method and apparatus, electronic device, and storage medium, including: extracting feature vector of each frame in query image sequence and feature vector of each frame in candidate image sequence; determining self-expression feature vector of query image sequence, collaborative expression feature vector of the query image sequence, self-expression feature vector of candidate image sequence, and collaborative expression feature vector of candidate image sequence based on feature vector of each frame in query image sequence and feature vector of each frame in candidate image sequence; determining similarity feature vector between query image sequence and candidate image sequence based on self-expression feature vector of query image sequence, collaborative expression feature vector of query image sequence, self-expression feature vector of candidate image sequence, and collaborative expression feature vector of candidate image sequence; and determining matching result between query image sequen

Abstract: An image processing method includes: a color feature extracted from a first image is acquired; a customized mask feature is acquired, the customized mask feature being configured to indicate a regional position of the color feature in the first image; and the color feature and the customized mask feature are input to a feature mapping network to perform image attribute edition to obtain a second image.

Abstract: The present disclosure relates to a neural network training method and apparatus, and an image processing method and apparatus. The training method includes: performing classification processing on a target image in a training set by means of a neural network to obtain a prediction classification result of the target image; and training the neural network according to the prediction classification result, and an initial category tag and a corrected category tag of the target image. Embodiments of the present disclosure may supervise the training process of the neural network by means of the initial category tag and the corrected category tag, and simplify the training process and a network structure.

Abstract: The present disclosure relates to a data processing method and apparatus, and a storage medium. The method includes: inputting input data into a neural network model to obtain feature data currently output by a network layer in the neural network model (S100); determining, according to transformation parameters of the neural network model, a normalization mode matched with the feature data (S200), wherein the transformation parameters are used for adjusting a statistical range of statistics of the feature data, and the statistical range is used for representing the normalization mode; and performing normalization processing on the feature data according to the determined normalization mode to obtain normalized feature data (S300). According to embodiments of the present disclosure, the purpose of autonomously learning a matched normalization mode for each normalization layer of the neural network model can be implemented without human intervention.

Abstract: A neural network training method and apparatus and an image matching method and apparatus are provided. The neural network training method at least includes: labeling annotation information of a first clothing instance and a second clothing instance, where the first clothing instance and the second clothing instance are respectively from a first clothing image and a second clothing image; pairing the first clothing image and the second clothing image in response to a state of matching between the first clothing instance and the second clothing instance; and training a neural network to be trained based on the paired first clothing image and second clothing image.

Abstract: A method and device for image recognition and a storage medium are provided. The method includes: a target image is acquired; feature extraction processing is performed on the target image through convolutional layers in a neural network model to obtain feature maps, and Instance Normalization (IN) and Batch Normalization (BN) processing is performed on the feature maps to obtain a recognition result of the target image; and the recognition result of the target image is output.

Abstract: A method for determining a candidate input is provided. Text that precedes and/or follows a current text input location in an input interface is acquired. A candidate sentence is acquired based on the text which is contained in a current sentence that corresponds to the current text input location. A preset number of possible conjunctions for the candidate sentence are determined based on occurrence probabilities of the possible conjunctions that are determined for the candidate sentence. The preset number of possible conjunctions are connected to the candidate sentence to obtain predicted candidate sentences. Occurrence probabilities of the predicted candidate sentences are calculated according to the text that precedes and/or follows the current text input location. Further, a preset number of the predicted candidate sentences are provided based on the calculated occurrence probabilities of the predicted candidate sentences as candidate inputs.

Abstract: An image processing method and apparatus, and a storage medium are provided. The method includes: obtaining a first feature map of an image to be processed (S101); determining a final weight vector of the first feature map (S102); determining a target normalization mode corresponding to the first feature map from a preset normalization set is determined according to the final weight vector (S103); and normalizing the first feature map by means of the target normalization mode to obtain a second feature map (S104).

Abstract: A liquid crystal panel including an array substrate and a color filter substrate which are disposed opposite and multiple fan-out lines and a color resist layer stacked along a direction from the array substrate toward the color filter substrate. The color film substrate is provided with a common electrode layer. A color resist layer and a columnar spacer are mutually abutted to ensure a distance between the array substrate and the color filter. The color resist layer includes a first plane facing the columnar spacer, and the common electrode line or the common electrode layer surrounds the color resist layer and exposes the first plane. When the liquid crystal panel is subjected to a larger external force such that deformation or displacement occurs between the columnar spacer and the color resist layer, the common electrode line and the common electrode layer are not conductive to form a short circuit.

Abstract: A liquid crystal panel including an array substrate and a color filter substrate which are disposed opposite and multiple fan-out lines and a color resist layer stacked along a direction from the array substrate toward the color filter substrate. The color film substrate is provided with a common electrode layer. A color resist layer and a columnar spacer are mutually abutted to ensure a distance between the array substrate and the color filter. The color resist layer includes a first plane facing the columnar spacer, and the common electrode line or the common electrode layer surrounds the color resist layer and exposes the first plane. When the liquid crystal panel is subjected to a larger external force such that deformation or displacement occurs between the columnar spacer and the color resist layer, the common electrode line and the common electrode layer are not conductive to form a short circuit.

Abstract: The present disclosure relates to an information processing method and apparatus, an electronic device, and a storage medium. The method includes: inputting received input information into a neural network; processing the input information by means of the neural network, where in the case that convolution processing is executed by means of a convolution layer of the neural network, a convolution kernel of the convolution layer is updated by using a transformation matrix configured for the convolution layer, so that the convolution processing of the convolution layer is completed by means of the updated convolution kernel; and outputting a processing result of the processing of the neural network. According to embodiments of the present disclosure, group convolution of a neural network in any form can be implemented.

Abstract: A method for image processing, an electronic device, and a storage medium are provided. The method includes the following. For each processing method in a preset set of processing methods, a first feature parameter and a second feature parameter are determined according to image data to-be-processed, where the preset set includes at least two processing methods selected from whitening methods and/or normalization methods, and the image data to-be-processed includes at least one image data. A first weighted average of the first feature parameters is determined according to a weight coefficient of each first feature parameter, and a second weighted average of the second feature parameters is determined according to a weight coefficient of each second feature parameter. The image data to-be-processed is whitened according to the first weighted average and the second weighted average.

Abstract: A method and device for image recognition and a storage medium are provided. The method includes: a target image is acquired; feature extraction processing is performed on the target image through convolutional layers in a neural network model to obtain feature maps, and Instance Normalization (IN) and Batch Normalization (BN) processing is performed on the feature maps to obtain a recognition result of the target image; and the recognition result of the target image is output.

Abstract: A chat message processing method is provided for a server. The method includes receiving a chat message sent by a sending terminal, where the chat message carries information about a receiving terminal, and forwarding the chat message to the receiving terminal. The method also includes, when determining that the chat message comprises an image, generating at least one group of descriptive texts used for describing the image; and sending the at least one group of descriptive texts of the image to the receiving terminal to cause the receiving terminal to, when displaying the image, simultaneously display the at least one group of descriptive texts as at least one candidate entering-item associated with the image.

Abstract: Embodiments of the present disclosure disclose normalization methods and apparatuses for a deep neural network, devices, and storage media. The method includes: inputting an input data set into a deep neural network, the input data set including at least one piece of input data; normalizing a feature map set output by means of a network layer in the deep neural network from at least one dimension to obtain at least one dimension variance and at least one dimension mean; and determining a normalized target feature map set based on the at least one dimension variance and the at least one dimension mean. Based on the embodiments of the present disclosure, normalization is performed along at least one dimension so that statistics information of each dimension of a normalization operation is covered, thereby ensuring good robustness of statistics in each dimension without excessively depending on the batch size.

Abstract: Target matching method and apparatus, electronic device, and storage medium, including: extracting feature vector of each frame in query image sequence and feature vector of each frame in candidate image sequence; determining self-expression feature vector of query image sequence, collaborative expression feature vector of the query image sequence, self-expression feature vector of candidate image sequence, and collaborative expression feature vector of candidate image sequence based on feature vector of each frame in query image sequence and feature vector of each frame in candidate image sequence; determining similarity feature vector between query image sequence and candidate image sequence based on self-expression feature vector of query image sequence, collaborative expression feature vector of query image sequence, self-expression feature vector of candidate image sequence, and collaborative expression feature vector of candidate image sequence; and determining matching result between query image sequen