Abstract: An embodiment provides a system and method for sample labeling. During operation, the system obtains a plurality of historical loss assessment images and obtains a plurality of candidate samples from the plurality of loss assessment images. A respective candidate sample comprises an image of a candidate damage area detected in a corresponding historical loss assessment image. The system clusters the plurality of candidate samples into a plurality of class clusters. For a respective class cluster, the system determines a center candidate sample set corresponding to a class cluster center of the respective class cluster, receives a manual labeling result associated with candidate samples in the determined center candidate sample set, and performs, according to the manual labeling result, damage classification labeling on other unlabeled candidate samples in the respective class cluster to obtain a plurality of labeled samples.

Abstract: An article generation method and device, and a computer storage medium. According to an example of the method, after a content source is mined based on requirement information inputted by a user, at least one topic dimension vector may be extracted from the mined content source by using a particular topic generation model. Then, for each extracted topic dimension vector, topic sentence mining is performed on the content source according to the topic dimension vector, to obtain topic sentences corresponding to the topic dimension vector. Finally, the topic sentences corresponding to the at least one topic dimension vector are spliced and synthesized, to generate an article conforming to the requirement information.

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: A method for assessing vehicle damage, includes: acquiring a to-be-identified vehicle damage image; acquiring damage object detection information in the image; acquiring vehicle component information for the vehicle damage image, the vehicle component information including a detected first component; generating a first comprehensive feature for the first component; determining a plurality of candidate prediction results for the first component, each of the candidate prediction results comprising a candidate component category and a candidate damage category; obtain a first prediction result including, for each of the candidate prediction results, a probability that the candidate damage category thereof is correct; obtaining a second prediction result including, for each of the candidate prediction results, a probability that the candidate component category thereof is correct; and determining the component category of the first component and the corresponding damage category thereof at least based on the firs

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: The inventive concepts herein relate to performing block retrieval on a block to be processed of a urine sediment image. The method comprises: using a plurality of decision trees to perform block retrieval on the block to be processed, wherein each of the plurality of decision trees comprises a judgment node and a leaf node, and the judgment node judges the block to be processed to make it reach the leaf node by using a block retrieval feature in a block retrieval feature set to form a block retrieval result at the leaf node, and at least two decision trees in the plurality of decision trees are different in structures thereof and/or judgments performed by the judgment nodes thereof by using the block retrieval feature; and integrating the block retrieval results of the plurality of decision trees so as to form a final block retrieval result.

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 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: A method for assessing vehicle damage, includes: acquiring a to-be-identified vehicle damage image; acquiring damage object detection information in the image; acquiring vehicle component information for the vehicle damage image, the vehicle component information including a detected first component; generating a first comprehensive feature for the first component; determining a plurality of candidate prediction results for the first component, each of the candidate prediction results comprising a candidate component category and a candidate damage category; obtain a first prediction result including, for each of the candidate prediction results, a probability that the candidate damage category thereof is correct; obtaining a second prediction result including, for each of the candidate prediction results, a probability that the candidate component category thereof is correct; and determining the component category of the first component and the corresponding damage category thereof at least based on the firs

Abstract: The inventive concepts herein relate to performing block retrieval on a block to be processed of a urine sediment image. The method comprises: using a plurality of decision trees to perform block retrieval on the block to be processed, wherein each of the plurality of decision trees comprises a judgment node and a leaf node, and the judgment node judges the block to be processed to make it reach the leaf node by using a block retrieval feature in a block retrieval feature set to form a block retrieval result at the leaf node, and at least two decision trees in the plurality of decision trees are different in structures thereof and/or judgments performed by the judgment nodes thereof by using the block retrieval feature; and integrating the block retrieval results of the plurality of decision trees so as to form a final block retrieval result.

Abstract: A method for assessing vehicle damage, includes: acquiring a to-be-identified vehicle damage image; acquiring damage object detection information in the image; acquiring vehicle component information for the vehicle damage image, the vehicle component information including a detected first component; generating a first comprehensive feature for the first component; determining a plurality of candidate prediction results for the first component, each of the candidate prediction results comprising a candidate component category and a candidate damage category; obtain a first prediction result including, for each of the candidate prediction results, a probability that the candidate damage category thereof is correct; obtaining a second prediction result including, for each of the candidate prediction results, a probability that the candidate component category thereof is correct; and determining the component category of the first component and the corresponding damage category thereof at least based on the firs

Abstract: A method for assessing vehicle damage, includes: acquiring a to-be-identified vehicle damage image; acquiring damage object detection information in the image; acquiring vehicle component information for the vehicle damage image, the vehicle component information including a detected first component; generating a first comprehensive feature for the first component; determining a plurality of candidate prediction results for the first component, each of the candidate prediction results comprising a candidate component category and a candidate damage category; obtain a first prediction result including, for each of the candidate prediction results, a probability that the candidate damage category thereof is correct; obtaining a second prediction result including, for each of the candidate prediction results, a probability that the candidate component category thereof is correct; and determining the component category of the first component and the corresponding damage category thereof at least based on the firs

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 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: 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: 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: 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.