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: 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 a three-dimensional face reconstruction model includes inputting an acquired sample face image into a three-dimensional face reconstruction model to obtain a coordinate transformation parameter and a face parameter of the sample face image; determining the three-dimensional stylized face image of the sample face image according to the face parameter of the sample face image and the acquired stylized face map of the sample face image; transforming the three-dimensional stylized face image of the sample face image into a camera coordinate system based on the coordinate transformation parameter, and rendering the transformed three-dimensional stylized face image to obtain a rendered map; and training the three-dimensional face reconstruction model according to the rendered map and the stylized face map of the sample face image.

Abstract: An image rendering method includes the steps below. A model of an environmental object is rendered to obtain an image of the environmental object in a target perspective. An image of a target object in the target perspective and a model of the target object are determined according to a neural radiance field of the target object. The image of the target object is fused and rendered into the image of the environmental object according to the model of the target object.

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: 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: The present application discloses an image recognition method, apparatus, device, and a computer storage medium, which is related to a technical field of artificial intelligence, and in particular, to a technical field of image processing. The method includes: performing organ recognition on a human face image and marking positions of the human facial five sense organs in the human face image, obtaining a marked human face image; inputting the marked human face image into a backbone network model and performing feature extraction, obtaining defect features of the marked human face image outputted by different convolutional neural network levels of the backbone network model; and fusing the defect features of different levels that are located in a same area of the human face image, obtaining a defect recognition result of the human face image.

Abstract: A method and an apparatus for measuring temperature, and a computer-readable storage medium includes detecting a target position of an object in an input image; determining key points of the target position and weight information of each key point based on a detection result of the target position, in which the weight information is configured to indicate a probability of each key point being covered; acquiring temperature information of each key point; and determining a temperature of the target position at least based on the temperature information and the weight information of each key point.

Abstract: An image recognition method is provided, which is related to a technical field of artificial intelligence, and in particular, to a technical field of image processing. An implementation includes: performing five-sense-organ recognition on a preprocessed human face image and marking positions of the human facial five sense organs in the human face image, to obtain the marked human face image; determining human face images at multiple scales of the marked human face image, inputting the human face images of multiple scales into a backbone network model, and performing feature extraction, to obtain a wrinkle feature of the human face image at each of the multiple scales; and fusing the wrinkle feature at each scale that is located in a same area of the human face image, to obtain a wrinkle recognition result of the human face image.

Abstract: A method for training a face recognition model includes: acquiring a plurality of first training images being uncovered face images, and acquiring a plurality of covering object images; generating a plurality of second training images by separately fusing the plurality of covering object images with the uncovered face images; and training the face recognition model by inputting the plurality of first training images and the plurality of second training images into the face recognition model.

Abstract: A model determination method and electronic device is provided, and relates to the technical field of artificial intelligence and, in particular, to the field of computer visions and deep learning, and can be applied to image processing, image identification and other scenarios. A specific implementation solution includes an image sample and a text sample are acquired, wherein text data in the text sample is used for performing text description to target image data in the image sample; at least one image feature in the image sample is stored to a first queue, and at least text feature in the text sample is stored to a second queue; the first queue and the second queue are trained to obtain a first target model; and the first target model is determined as an initialization model for a second target model.

Abstract: A method for recognizing text includes: obtaining an image sequence feature of an image to be recognized; obtaining a full text string of the image to be recognized by decoding the image sequence feature; obtaining a text sequence feature by performing a semantic enhancement process on the full text string, in which the image sequence feature, the full text string and the text sequence feature are of the same length; and determining text content of the image to be recognized based on the full text string and the text sequence feature.

Abstract: The present disclosure provides a text detection method, a text recognition method and an apparatus, which relate to the field of artificial intelligence technology, in particular to the field of deep learning and computer vision technologies, and can be applied to scenarios such as optical character recognition. The text detection method is: acquiring an image feature of a text strip in a to-be-recognized image; performing visual enhancement processing on the to-be-recognized image to obtain an enhanced feature map of the to-be-recognized image; comparing the image feature of the text strip with the enhanced feature map for similarity to obtain a target bounding box of the text strip on the enhanced feature map.

Abstract: The present disclosure provides an image processing method, a text recognition method and an apparatus. The image processing method includes: preprocessing acquired sample images to obtain position information, image blocks and text content corresponding to fields in the sample images respectively; making a mask prediction on the position information of the fields according to the position information, the image blocks and the text content corresponding to the fields respectively to obtain a prediction result; and training according to the prediction result to obtain a text recognition model, where the text recognition model is used to perform text recognition on a to-be-recognized image.

Abstract: The present disclosure provides a training method of a text recognition model, a text recognition method, and an apparatus, relating to the technical field of artificial intelligence, and specifically, to the technical field of deep learning and computer vision, which can be applied in scenarios such as optional character recognition, etc. The specific implementation solution is: performing mask prediction on visual features of an acquired sample image, to obtain a predicted visual feature; performing mask prediction on semantic features of acquired sample text, to obtain a predicted semantic feature, where the sample image includes text; determining a first loss value of the text of the sample image according to the predicted visual feature; determining a second loss value of the sample text according to the predicted semantic feature; training, according to the first loss value and the second loss value, to obtain the text recognition model.

Abstract: Embodiments of the present disclosure provide a method and apparatus for training an image recognition model based on a semantic enhancement, a method and apparatus for recognizing an image, an electronic device, and a computer readable storage medium. The method for training an image recognition model based on a semantic enhancement comprises: extracting, from an inputted first image being unannotated and having no textual description, a first feature representation of the first image; calculating a first loss function based on the first feature representation; extracting, from an inputted second image being unannotated and having an original textual description, a second feature representation of the second image; calculating a second loss function based on the second feature representation, and training an image recognition model based on a fusion of the first loss function and the second loss function.

Abstract: A method, apparatus and system for retrieving an image is provided, the method comprises: detecting, in response to receiving a query request comprising a target image, a target subject from the target image; extracting a subject feature from the target subject if a confidence level of a detection box of the detected target subject is greater than a first threshold, the subject feature comprising an identical feature, a similar feature and a category; performing matching on the subject feature of the target image and a subject feature of a candidate image pre-stored in a database, to obtain a similarity score and an identicalness score of the candidate image; and selecting, according to the similarity score and the identicalness score, a predetermined number of candidate images as a search result for output.

Abstract: The present disclosure discloses an image processing method, an electronic device and a storage medium, and relates to the field of artificial intelligence technologies, and particularly to the fields of computer vision technologies, deep learning technologies, or the like. The image processing method includes: acquiring a multi-modal feature of each of at least one text region in an image, the multi-modal feature including features in plural dimensions; performing a global attention processing operation on the multi-modal feature of each text region to obtain a global attention feature of each text region; determining a category of each text region based on the global attention feature of each text region; and constructing structured information based on text content and the category of each text region.

Abstract: Provided are a method and apparatus for extracting information about a negotiable instrument, an electronic device and a storage medium. The method includes inputting a to-be-recognized negotiable instrument into a pretrained deep learning network and obtaining a visual image corresponding to the to-be-recognized negotiable instrument through the deep learning network; matching the visual image corresponding to the to-be-recognized negotiable instrument with a visual image corresponding to each negotiable-instrument template in a preconstructed base template library; and in response to the visual image corresponding to the to-be-recognized negotiable instrument successfully matching a visual image corresponding to one negotiable-instrument template in the base template library, extracting structured information of the to-be-recognized negotiable instrument by using the negotiable-instrument template.