Abstract: A method for vehicle damage identification, includes: obtaining a vehicle damage picture to be identified; inputting the vehicle damage picture into a plurality of pre-trained target detection models respectively, and obtaining corresponding detection results from the plurality of target detection models as a detection result set, wherein the detection result set comprises candidate bounding boxes detected by the plurality of target detection models and category prediction results of the candidate bounding boxes; determining an integrated feature vector of a first candidate bounding box of the candidate bounding boxes; and separately inputting integrated feature vectors corresponding to the candidate bounding boxes into a pre-trained classification model, and optimizing the detection result set according to output results of the classification model.

Abstract: A vehicle insurance image processing method includes: acquiring a vehicle insurance image; processing the vehicle insurance image by using a preset image classification algorithm, to determine at least one category label of the vehicle insurance image; and storing, based on classification, the vehicle insurance image in a corresponding usage scenario according to the at least one category label.

Abstract: A natural language sentence is generated for a radiology report. One or more words are obtained where the one or more words were produced based on image processing of a radiology image. A computer implemented text analysis process is used to analyse the one or more words to generate a natural language sentence representing the radiology image. The natural language sentence is output. The computer implemented text analysis process includes: determining, for each of the one or more words, and using word modified embeddings, a vector representing the word; and determining, based on the determined one or more vectors, and using a text generator model, the natural language sentence.

Abstract: A method for vehicle damage identification, includes: obtaining a vehicle damage picture to be identified; inputting the vehicle damage picture into a plurality of pre-trained target detection models respectively, and obtaining corresponding detection results from the plurality of target detection models as a detection result set, wherein the detection result set comprises candidate bounding boxes detected by the plurality of target detection models and category prediction results of the candidate bounding boxes; determining an integrated feature vector of a first candidate bounding box of the candidate bounding boxes; and separately inputting integrated feature vectors corresponding to the candidate bounding boxes into a pre-trained classification model, and optimizing the detection result set according to output results of the classification model.

Abstract: A system is provided for identifying damages of a vehicle. During operation, the system can obtain a set of digital images associated with a set of tagged digital images as training data. Each tagged digital image in the set of tagged digital images may include at least one damage object. The system can train a damage identification model based on the training data. When training the damage identification model, the system may identify at least a damage object in the training data based on a target detection technique. The system may also generate a set of feature vectors for the training data. The system can use the set of feature vectors to optimize a set of parameters associated with the damage identification model to obtain a trained damage identification model.

Abstract: Embodiments described herein provide a system for facilitating image sampling for training a target detector. During operation, the system obtains a first image depicting a first target. Here, the continuous part of the first target in the first image is labeled and enclosed in a target bounding box. The system then generates a set of positive image samples from an area of the first image enclosed by the target bounding box. A respective positive image sample includes at least a part of the first target. The system can train the target detector with the set of positive image samples to detect a second target from a second image. The target detector can be an artificial intelligence (AI) model capable of detecting an object.

Abstract: One embodiment can provide a system for detecting optimizing a damage detection result. During operation, the system can obtain a digital image of a damaged vehicle, identify a set of candidate damaged areas from the digital image as the damage detection result. The system can then extract a set of feature vectors corresponding to the set of candidate damaged areas For each candidate damaged area, the system can calculate a set of similarity features between the candidate damaged area and other candidate damaged areas in the set of candidate damaged areas based on the set of feature vectors. The system can input the set of similarity features to a damage prediction module. The system can then determine whether the candidate damaged area is an exceptional area based on an output of the damage prediction module to optimize the damage detection result.

Abstract: Embodiments of the specification provide a method, apparatus, server and terminal device for obtaining a vehicle loss assessment image. The method may include: receiving video data of a damaged vehicle and information of a damaged portion of the damaged vehicle; extracting one or more video images from the video data; classifying the one or more video images into one or more candidate image classification sets of the damaged portion based on the information of the damaged portion; and selecting a vehicle loss assessment image from the one or more candidate image classification sets according to a screening condition.

Abstract: Embodiments of the application provide a method, apparatus, server, and terminal device for obtaining a vehicle loss assessment image. A computer-implemented method for obtaining a vehicle loss assessment image comprises: receiving video data of a damaged vehicle; detecting one or more video images in the video data to identify a damaged portion in the one or more video images; classifying the one or more video images into one or more candidate image classification sets of the damaged portion based on the identified damaged portion; and selecting a vehicle loss assessment image from the one or more candidate image classification sets according to a screening condition.

Abstract: Structured report data is generated from a medical text report. A medical text report including one or more natural language statements is acquired. A computer implemented text analysis process analyses the medical text report to determine, for each natural language statement, one or more labels for the natural language statement. Structured report data including the determined one or more labels each in association with natural language data from the natural language statement to which the label corresponds is generated. The computer implemented text analysis process includes, for each natural language statement: determining, for each of the one or more words of the natural language statement, and, using word embeddings, a vector representing the word; and determining, based on the determined one or more vectors, and using a text classification model, the one or more labels associated with the natural language statement.

Abstract: Information from a radiology report is obtained based on machine learning. Rather than a natural language processing system designed to answer a question based on a large corpus of generic information, deep learning is used to train a machine-learnt network to contribute to extraction of a patient-specific answer from a patient-specific radiology report. Natural language questions, including questions with less informed terminology from a patient and questions with more informed terminology from a physician, are answered using evidence from the patient-specific radiology report even where that report and/or questions are not used in training the machine-learnt network.

Abstract: Methods, systems, and computer-readable storage media for generation of a vehicle repair plan. Implementations include actions of receiving vehicle damage data including an image of a damaged vehicle. The vehicle damage data is processed to determine a damaged area and a damage type of a portion of the damaged vehicle. A repair plan is generated for the damaged vehicle based on the damaged area and the damage type. The repair plan is initiated for the damaged vehicle.

Abstract: Methods, systems, and computer-readable storage media for generation of a vehicle repair plan. Implementations include actions of receiving vehicle damage data including an image of a damaged vehicle. The vehicle damage data is processed to determine a first vehicle component. The first image is matched to a second image to determine a second vehicle component within the second image. The second vehicle component is processed to determine a damaged area and a damage type of a portion of the damaged vehicle. A maintenance plan is generated for the damaged vehicle based on the damaged area and the damage type. The maintenance plan is initiated for the damaged vehicle.

Abstract: Methods, systems, and computer-readable storage media for generation of a vehicle repair plan. Implementations include actions of receiving vehicle damage data including an image of a damaged vehicle. The vehicle damage data is processed to determine a component region. The component region is processed to determine a damaged area and a damage type of a portion of the damaged vehicle. A maintenance plan is generated for the damaged vehicle based on the damaged area and the damage type. The maintenance plan is initiated for the damaged vehicle.

Abstract: In the present invention are a method and apparatus for processing a block to be processed of a urine sediment image. The method comprises: dividing a block to be processed into a plurality of grids; calculating an n-dimensional local feature vector of each grid of the plurality of grids, where n is a positive integer; in the block to be processed, merging at least two adjacent grids of the plurality of grids into an intermediate block; calculating an intermediate block merging feature vector of the intermediate block; according to a predetermined combination rule, combining the intermediate block merging feature vectors obtained for different intermediate blocks of the block to be processed into a general combination feature vector of the block to be processed; and by way of taking the general combination feature vector as a feature in a feature set of block processing, processing the block to be processed.

Abstract: Provided in the present invention are a method and apparatus for processing a block to be processed of a urine sediment image. The method comprises: calculating a variable number of local feature vectors of a block to be processed, wherein the local feature vector is a vector representing a feature of a local location of the block to be processed; classifying the variable number of local feature vectors into a fixed number of clusters so as to obtain a statistical histogram vector of the fixed number of clusters of the block to be processed, wherein the statistical histogram vector reflects the statistical distribution of the variable number of local feature vectors in the fixed number of clusters; and taking the statistical histogram vector as a feature in a feature set of block processing and processing the block to be processed.

Abstract: Methods, systems, and computer-readable storage media for generation of a vehicle repair plan. Implementations include actions of receiving vehicle damage data including an image of a damaged vehicle. The vehicle damage data is processed to determine a first vehicle component. The first image is matched to a second image to determine a second vehicle component within the second image. The second vehicle component is processed to determine a damaged area and a damage type of a portion of the damaged vehicle. A maintenance plan is generated for the damaged vehicle based on the damaged area and the damage type. The maintenance plan is initiated for the damaged vehicle.

Abstract: Methods, systems, and computer-readable storage media for generation of a vehicle repair plan. Implementations include actions of receiving vehicle damage data including an image of a damaged vehicle. The vehicle damage data is processed to determine a component region. The component region is processed to determine a damaged area and a damage type of a portion of the damaged vehicle. A maintenance plan is generated for the damaged vehicle based on the damaged area and the damage type. The maintenance plan is initiated for the damaged vehicle.

Abstract: Methods, systems, and computer-readable storage media for generation of a vehicle repair plan. Implementations include actions of receiving vehicle damage data including an image of a damaged vehicle. The vehicle damage data is processed to determine a damaged area and a damage type of a portion of the damaged vehicle. A repair plan is generated for the damaged vehicle based on the damaged area and the damage type. The repair plan is initiated for the damaged vehicle.

Abstract: Concepts herein relate to processing urine sediment images. An example method comprises: approximating the color of a pixel in a block to be processed to one of the kc colors in a code book, wherein the code book is a set of the kc colors generated in a set of urine sample blocks; obtaining a distribution histogram of the number of pixels the color approximation results of which fall on each color of the kc colors; using an occurrence frequency correction factor to correct the number of pixels the color approximation results; standardizing the corrected number of pixels the color approximation results; and processing the block to be processed.