Abstract: A method for debitterizing wheat protein peptide residue (WPPR) includes steps S1-S5. S1, the wheat protein peptide residue are taken to crush, and then deionized water are added into the crushed WPPR to obtain the peptide residue solution; S2, debitterizing modified solution is added to the peptide residue solution (PRS), and the PRS is filtered to obtain a preliminarily modified peptide residue solution (PMPRS); S3, modified flavor protease is added to the PMPRS, stirred and heated in a water bath to obtain a debitterized peptide residue (DPR) solution; S4, a supernatant of the debitterized peptide residue solution is removed to obtain a remaining solution, and the remaining solution is poured into a centrifuge tube to centrifugate to obtain a peptide residue precipitate; and S5, supercritical carbon dioxide extraction is performed on the peptide residue precipitate, and freeze-dried to obtain a final debitterized peptide residue product.

Abstract: Some embodiments operationally connect a risk score with cybersecurity protection mechanisms and user interactions data in a feedback loop. The risk score guides protection activities by the protection mechanisms, thereby prompting or preventing various user interactions. The protection activities and the user interactions are recorded in audit logs, and curated data based on the audit logs is fed to a risk scoring model as input. The risk scoring model then updates the risk score, and the loop repeats as the protection mechanisms alter their protection activities based on the updated risk score, thereby providing adaptive protection. Security tools for insider risk management, data leak prevention, and conditional access are enhanced to provide adaptive protection, by recording protection activities and user interactions for use as risk model input, and by checking regularly for risk score updates and modifying their protection activities accordingly.

Abstract: The present disclosure provides a character recognition model training method and apparatus, a character recognition method and apparatus, a device and a medium, relating to the technical field of artificial intelligence, and specifically to the technical fields of deep learning, image processing and computer vision, which can be applied to scenarios such as character detection and recognition technology. The specific implementing solution is: partitioning an untagged training sample into at least two sub-sample images; dividing the at least two sub-sample images into a first training set and a second training set; where the first training set includes a first sub-sample image with a visible attribute, and the second training set includes a second sub-sample image with an invisible attribute; performing self-supervised training on a to-be-trained encoder by taking the second training set as a tag of the first training set, to obtain a target encoder.

Abstract: A pre-training method for a Vision and Scene Text Aggregation model includes: acquiring a sample image-text pair; extracting a sample scene text from a sample image; inputting a sample text into a text encoding network to obtain a sample text feature; inputting the sample image and an initial sample aggregation feature into a visual encoding subnetwork and inputting the initial sample aggregation feature and the sample scene text into a scene encoding subnetwork to obtain a global image feature of the sample image and a learned sample aggregation feature; and pre-training the Vision and Scene Text Aggregation model according to the sample text feature, the global image feature of the sample image, and the learned sample aggregation feature.

Abstract: An image processing method and apparatus, an electronic device, and a storage medium, relating to the technical field of image processing. The method includes: acquiring a first image and a second image, wherein a resolution of the second image is greater than a resolution of the first image; determining difference information between a target pixel in the second image and a reference pixel corresponding to the target pixel point in the first image; and acquire a target image with the same resolution as the second image by applying an image differencing process to a predetermined image with the same resolution as the first image based on the difference information. This method can obtain the target image based on the difference value information, and improve image quality.

Abstract: The present disclosure provides a method of training a deep learning model for text detection and a text detection method, which relates to the technical field of artificial intelligence, and in particular, to the technical field of computer vision and deep learning and can be used in scenarios of OCR optical character recognition. A method of training a deep learning model for text detection is provided, in which a single character segmentation sub-network outputs a single character segmentation prediction result, a text line segmentation sub-network outputs a text line segmentation prediction result, the trained deep learning model can be used for detecting a text area; and, can at the same time achieve single character segmentation and text line segmentation, and thus is capable to perform text detection by combining two ways of text segmentation, which further improves the accuracy of text area detection.

Abstract: A method of training a text erasure model, a method of display a translation, an electronic device, and a storage medium. The training method includes: processing a set of original text block images by using a generator of a generative adversarial network model to obtain a set of simulated text block-erased images; alternately training the generator and a discriminator of the generative adversarial network model by using a set of real text block-erased images and the set of simulated text block-erased images, so as to obtain a trained generator and a trained discriminator; and determining the trained generator as the text erasure model, wherein a pixel value of a text-erased region in a real text block-erased image contained in the set of real text block-erased images is determined based on a pixel value of another region in the real text block-erased image other than the text-erased region.

Abstract: The present application provides a method of training a text recognition model. The method includes: inputting a first sample image into the visual feature extraction sub-model to obtain a first visual feature and a first predicted text, the first sample image contains a text and a tag indicating a first actual text; obtaining, by using the semantic feature extraction sub-model, a first semantic feature based on the first predicted text; obtaining, by using the sequence sub-model, a second predicted text based on the first visual feature and the first semantic feature; and training the text recognition model based on the first predicted text, the second predicted text and the first actual text. The present disclosure further provides a method of recognizing a text, an electronic device, and a storage medium.

Abstract: A method training a text detection model and a method of detecting a text. The training method includes: inputting a sample image into a text feature extraction sub-model of a text detection model to obtain a text feature of a text in the sample image, the sample image having a label indicating an actual position information and an actual category; inputting a predetermined text vector into a text encoding sub-model of the text detection model to obtain a text reference feature; inputting the text feature and the text reference feature into a decoding sub-model of the text detection model to obtain a text sequence vector; inputting the text sequence vector into an output sub-model of the text detection model to obtain a predicted position information and a predicted category; and training the text detection model based on the predicted and actual categories, the predicted and actual position information.

Abstract: Some embodiments operationally connect a risk score with cybersecurity protection mechanisms and user interactions data in a feedback loop. The risk score guides protection activities by the protection mechanisms, thereby prompting or preventing various user interactions. The protection activities and the user interactions are recorded in audit logs, and curated data based on the audit logs is fed to a risk scoring model as input. The risk scoring model then updates the risk score, and the loop repeats as the protection mechanisms alter their protection activities based on the updated risk score, thereby providing adaptive protection. Security tools for insider risk management, data leak prevention, and conditional access are enhanced to provide adaptive protection, by recording protection activities and user interactions for use as risk model input, and by checking regularly for risk score updates and modifying their protection activities accordingly.

Abstract: The disclosure provides a method and a device for processing information, an electronic device, and a storage medium, belonging to a field of artificial intelligence including computer vision, deep learning, and natural language processing. In the method, the computing device recognizes multiple text items in the image. The computing device classifies multiple text items into a first set of name text items and a second set of content text items based on semantics of the text items. The computing device performs a matching operation between the first set and the second set based on a layout of the text items in the image, and determines matched name-content text items. The matched name-content text items include a name text item in the first set and a content text item matching the name text item and in the second set. The computing device outputs the matched name-content text items.

Abstract: A method and apparatus for processing an image, a device and a storage medium are provided. An implementation of the method includes: acquiring a template image, the template image including at least one region of interest; determining a first feature map corresponding to each region of interest in the template image; acquiring a target image; determining a second feature map of the target image; and determining at least one region of interest in the target image according to the first feature map and the second feature map.

Abstract: There is provided an image-based information extraction model, method, and apparatus, a device, and a storage medium, which relates to the field of artificial intelligence (AI) technologies, specifically to fields of deep learning, image processing, computer vision technologies, and is applicable to optical character recognition (OCR) and other scenarios. A specific implementation solution involves: acquiring a to-be-extracted first image and a category of to-be-extracted information; and inputting the first image and the category into a pre-trained information extraction model to perform information extraction on the first image to obtain text information corresponding to the category.

Abstract: A text recognition method includes: acquiring an image including text information, the text information including M characters, M being a positive integer greater than 1; performing text recognition on the image to acquire character information about the M characters; recognizing reading direction information about each character in accordance with the character information about the M characters, the reading direction information being used to indicate a next character corresponding to a current character in a semantic reading order; and ranking the M characters in accordance with the reading direction information about the M characters to acquire a text recognition result of the text information.

Abstract: A method for training an image recognition model includes: obtaining a training data set, in which the training data set includes first text images of each vertical category in a non-target scene and second text images of each vertical category in a target scene, and a type of text content involved in the first text images is the same as a type of text content involved in the second text image; training an initial recognition model by using the first text images, to obtain a basic recognition model; and modifying the basic recognition model by using the second text images, to obtain an image recognition model corresponding to the target scene.

Abstract: A pre-training method for a Vision and Scene Text Aggregation model includes: acquiring a sample image-text pair; extracting a sample scene text from a sample image; inputting a sample text into a text encoding network to obtain a sample text feature; inputting the sample image and an initial sample aggregation feature into a visual encoding subnetwork and inputting the initial sample aggregation feature and the sample scene text into a scene encoding subnetwork to obtain a global image feature of the sample image and a learned sample aggregation feature; and pre-training the Vision and Scene Text Aggregation model according to the sample text feature, the global image feature of the sample image, and the learned sample aggregation feature.

Abstract: Embodiments of the present disclosure provide a method for training a face fusion model and an electronic device. The method includes: performing a first face changing process on a user image and a template image to generate a reference template image; adjusting poses of facial features of the template image based on the reference template image to generate a first input image; performing a second face changing process on the template image to generate a second input image; inputting the first input image and the second input image into a generator of an initial face fusion model to generate a fused face area image; and inputting the fused image and the template image into a discriminator of the initial face fusion model to obtain a result, and performing backpropagation correction on the initial face fusion model based on the result to generate a face fusion model.

Abstract: Provided are a method for training a region of interest (ROI) detection model, a method for detecting an ROI, a device, and a medium. The specific implementation includes: performing feature extraction on a sample image to obtain a sample feature data; performing non-linear mapping on the sample feature data to obtain a first feature data and a second feature data; determining an inter-region difference data according to the second feature data and a third feature data of the first feature data in a region associated with a label ROI; and adjusting at least one of a to-be-trained feature extraction parameter and a to-be-trained feature enhancement parameter of the ROI detection model according to the inter-region difference data and the region associated with the label ROI.

Abstract: A method and an apparatus is provided for recognizing a document image, a storage medium and an electronic device, relates to the technical field of artificial intelligent recognition, particularly relates to the technical fields of deep learning and computer vision. The method includes that a document image to be recognized is transformed into an image feature map, where the document image at least includes at least one text box and text information including multiple characters; a first recognition content of the document image to be recognized is predicted based on the image feature map, the multiple characters and the text box; the document image to be recognized is recognized based on an optical character recognition algorithm to obtain a second recognition content; and the first recognition content is matched with the second recognition content to obtain a target recognition content.

Abstract: The present disclosure provides a character recognition model training method and apparatus, a character recognition method and apparatus, a device and a medium, relating to the technical field of artificial intelligence, and specifically to the technical fields of deep learning, image processing and computer vision, which can be applied to scenarios such as character detection and recognition technology. The specific implementing solution is: partitioning an untagged training sample into at least two sub-sample images; dividing the at least two sub-sample images into a first training set and a second training set; where the first training set includes a first sub-sample image with a visible attribute, and the second training set includes a second sub-sample image with an invisible attribute; performing self-supervised training on a to-be-trained encoder by taking the second training set as a tag of the first training set, to obtain a target encoder.