63 results about "Final Labeling" patented technology

The invention discloses an automatic label labeling and classifying method and system oriented to unstructured system documents, and relates to the technical field of artificial intelligence. The method comprises the steps that semantic structure pre-analysis is conducted on an original system text, and a system semantic structure tree is constructed; establishing a system semantic enhancement vector space based on the semantic units and the logic relationship thereof; performing semantic deconstruction on the preset tag and extracting a feature vector; realizing cross-space semantic matching of the document and the tag through a system semantic attention mechanism; a confidence evaluation module is introduced to screen high-confidence labels from the three dimensions of structural integrity, coverage and logic consistency; and outputting a final label and a score through semantic conflict detection and resolution. According to the method, the problems that in the prior art, unstructured system text labeling accuracy is low and large-scale labeling samples are dependent on polysemy ambiguity, high context dependency, complex semantic structure and the like are solved, and labeling accuracy and robustness are remarkably improved.

The invention provides a vehicle target automatic labeling method and system based on deep learning. The method comprises the steps of obtaining video stream monitoring data collected by a road camera; based on the video stream monitoring data, utilizing a pre-trained vehicle identification model to automatically label a vehicle target in the video stream monitoring data to obtain a preliminary labeling result; performing uncertainty evaluation on the preliminary labeling result through an adversarial sample generation strategy to obtain an uncertainty score; when the uncertainty score does not exceed a preset threshold value, taking the preliminary labeling result as a final labeling result of the vehicle target; according to the invention, by using the pre-trained convolutional neural network model, large-scale video stream data can be quickly processed and preliminary annotation can be completed, so that the dependence on manual annotation is reduced; uncertainty evaluation is carried out on the preliminary labeling result through an adversarial sample generation strategy, errors or low-confidence-coefficient areas possibly existing in the labeling result can be effectively recognized, and the accuracy of the final labeling result can be guaranteed.

The embodiment of the invention relates to a multi-round medical image annotation method and device introducing a cross validation mechanism, and the method comprises the steps: transmitting a first image to a plurality of expert annotation interfaces, and merging the feedback annotation sets of all interfaces to obtain a first annotation set; setting cross validation items of every two annotation boxes in the first annotation set and constructing a corresponding cross validation matrix; on the basis of a cross validation matrix, dispute-free annotation box extraction, dispute-free annotation box aggregation and consistency score estimation are carried out; if the score is lower than a threshold value, a round of multi-person labeling is initiated again, otherwise, when the dispute labeling box set is empty, the non-dispute labeling box set serves as a final labeling result, when the dispute labeling box set is not empty, reexamination is conducted on the dispute labeling box set, and a set of the reexamination result and the non-dispute labeling box set serves as the final labeling result. According to the method, the labeling error of each image can be reduced, the overall labeling quality of all the images is improved, and the stability of the overall labeling quality is guaranteed.

The invention provides a public opinion video tag aggregation method and system based on artificial intelligence, and relates to the technical field of artificial intelligence. Comprising the following steps: acquiring pictures and text information in a short video, and performing semantic alignment; different large language models are adopted to generate preliminary labels for the pictures and the text information after semantic alignment; clustering the pictures and the texts after semantic alignment to obtain clusters; calculating the labeling probability of each primary label type in the current cluster by each large language model, and selecting the primary label with the highest probability sum as a clustering label of the current cluster; calculating the reliability weight of each large language model in the current cluster based on the clustering label of the current cluster; and based on the reliability weight, calculating the weighted support degree of all the large language models to different preliminary label types of each piece of data in the current cluster, calculating the weighted label of the current data, and further determining a final label. According to the method, the condition of few labels or no labels can be effectively processed, and the manual workload is greatly reduced.

The invention discloses a visual large model and point cloud projection fused 3D target labeling method and system, and relates to the technical field of 3D target automatic labeling, and the method comprises the steps: carrying out the pixel-level semantic segmentation of a group of collected images and point clouds through a visual large model, and obtaining a segmentation image of a target pixel region and the semantic label of each pixel; generating a point cloud with a semantic tag by using projection of the point cloud to an image plane and semantic mapping; the method comprises the following steps: extracting foreground point clouds belonging to the same semantic tag, then performing clustering processing, obtaining a point cloud cluster belonging to the same semantic tag, traversing the point cloud cluster, fitting a 3D bounding box, combining parameters of a 3D target box obtained by fitting with the same semantic tag, generating 3D target annotations of the same semantic tag, then performing processing, and obtaining the 3D target annotations of the same semantic tag. Obtaining a final labeling result file; according to the 3D target labeling method, efficient and accurate 3D target automatic labeling is realized, the labeling cost is remarkably reduced, and the quality and consistency of labeled data are improved.

The invention relates to the field of electronic design automation, provides a method for semi-automatically processing an EDA (Electronic Design Automation) project file and generating a YOLO format annotation data set, and aims to solve the technical problems of low efficiency and poor precision of the traditional pure manual data annotation data set. According to the main scheme, the method comprises the steps of reading an engineering file of a jia-creative EDA project, analyzing and extracting element information, generating a standardized CSV data format, converting the standardized CSV data format into a label in a YOLO format, calculating normalized bounding box coordinates of elements, and recognizing special original elements and supplementing labeling information in combination with a template matching technology; drawing a bounding box on the schematic diagram image according to a YOLO label, highlighting different element types by using different colors, and generating a visual annotation image; revising and combining the newly generated YOLO label and a preliminary YOLO label generated by the AI model to obtain a label set; then importing into Labelimg for manual correction, and generating a final label set; and training a target detection model by using the final label set, and improving the pre-labeling precision through data set expansion iteration.

The invention discloses a quality inspection and labeling system for bid invitation file generation based on deep learning, and the system comprises a data standardization module which is used for collecting items and historical texts and executing data standardization, and generating a standardized text sequence; the semantic coding and candidate generation module is used for tensorizing the standardized text sequence and generating a candidate text of the bid invitation file; the text decoding module is used for executing limited CRF decoding on the candidate text and outputting an aligned labeling result file; the hypergraph labeling module is used for constructing a hypergraph and globally decoding the hypergraph to generate a multi-level labeling result; the selective re-checking module is used for calculating uncertainty, triggering manual re-checking and outputting a confirmed labeling result and an uncertainty record; the consistency verification module is used for verifying hierarchical consistency to form a final labeling result; and the structured export module is used for exporting the bid invitation file finished product text and the structured annotation data. According to the invention, quality inspection and labeling of bid invitation file generation are realized.

The invention provides a multi-label path reasoning method and system, a medium and a terminal. The method comprises the following steps: constructing a label system; dynamically updating a keyword set in the tag system; and based on the tag system and the keyword of the tag node, allocating a final tag to the to-be-classified text. According to the multi-label path reasoning method and system, the medium and the terminal, multi-label attribution can be efficiently and accurately completed under a complex label system through label system structured grouping, upper and lower layer path collaborative judgment and an embedded semantic fusion and path scoring mechanism.

The invention discloses a multi-dimensional periodontitis identification and detection method based on incomplete supervision information, and the method comprises the steps: firstly, obtaining a marked periodontal disease image, eliminating an image which cannot be identified, cutting out a part which needs to be identified in a remaining image, and carrying out the classification of the image according to two dimensions, namely whether the gingiva is inflamed or not and the periodontal retraction degree; thirdly, estimating label distribution of all samples based on a multi-dimensional classification model, and modifying training loss of each category of data; then, based on a multi-dimensional classification model, generating a pseudo label for the non-labeled periodontitis image data, obtaining a more accurate pseudo label by estimating the category deviation of existing parameters, and retraining the model by using a correction label; and finally, generating a final label corresponding to the image by using the trained model, and obtaining a final recognition result. According to the method, a semi-supervised learning framework is utilized, unbiased loss is obtained by estimating label distribution under the scene that only a small amount of annotated data exists and data categories are unbalanced, false labels are corrected by estimating category deviation, the two problems that the categories are unbalanced and the annotated data are limited are solved, and the model can achieve high recognition accuracy.

The application provides a kind of pipelined data labeling method, to solve the problem of low efficiency in prior art for complex labeling task labeling. A kind of pipelined data labeling method, comprising: S100, obtaining data to be labeled, and determining labeling scheme;S200, the overall labeling task in labeling scheme is split into several labeling subtasks;S300, for each labeling subtask, according to the preset labeling mode, select into the preset labeling mode;S400, AI pre-audit is carried out to the pre-labeling result data circulated to AI pre-audit step;S500, the data that passes the audit in all labeling subtasks is merged to form the final labeling result and output. The application can significantly reduce the complexity of the overall labeling task by decomposing the overall labeling task into several labeling subtasks, so that it is converted into a simple labeling subtask, which is convenient for artificial and AI learning and identification, and can improve the efficiency and quality of data labeling.

The invention relates to an e-commerce scene-oriented commodity label generation method and device, and a storage medium, and relates to the technical field of artificial intelligence. The method comprises the steps of obtaining native text information and an image set of a target commodity; performing information identification and extraction on the image set, converting related visual data into structured text description data, and fusing the structured text description data into a to-be-processed text corpus with multi-dimensional information; dynamically generating a theme label structure for the target commodity; performing text segmentation on the to-be-processed text corpus according to the topic tag structure to obtain grouped texts corresponding to different topic tags, and feeding back each grouped text to a corresponding topic expert sub-model to perform candidate tag extraction; and semantic fusion is carried out, and a fused label set is used as a final label of the target commodity. According to the method, the integrity, accuracy and robustness of commodity label generation in an e-commerce scene are remarkably improved, and the limitation of dependence on manpower or a fixed label library is reduced.

The invention discloses a visual segmentation large model data automatic annotation method combined with image prompt, which comprises the following steps: extracting deep features of a prompt image and a to-be-annotated image by adopting a shared visual coding technology of deep learning, and carrying out cross-image feature matching and similarity calculation. And positioning a high-response area which is consistent with a prompt target in semantics in the to-be-labeled image, and automatically screening out an accurate prompt point set from the high-response area. Furthermore, based on the point set, the visual segmentation large model is guided to complete accurate segmentation and mask generation of the target, so that full-process automation from image prompting to final labeling is realized, and labeling efficiency and consistency are remarkably improved. In this way, the bottleneck problems that in the prior art, an automatic labeling method is insufficient in universality and the application of a segmentation large model must depend on human interaction are solved, and an effective technical approach is provided for large-scale, low-cost and high-quality visual segmentation data production.

The application discloses a text data intelligent labeling system and method based on a large language model, and belongs to the technical field of natural language processing. The system and method are characterized in that a complete modification operation sequence of a large language model pre-labeling result of an artificial labeler is collected to construct an interactive log containing an original text, a pre-labeling result, a modification action and a final labeling result; the distribution law of the modification operation in the log is analyzed, a multi-level defect classification system is constructed in combination with the association between a text semantic feature and a modification type, and the influence weight of each defect is quantified through the construction of a defect association network atlas; feature samples are extracted for various defects, a defect training set is constructed, and a semantic enhancement instruction set is generated; meanwhile, resources are distributed according to the defect influence weight, the large language model is subjected to staged parameter calibration, and the corresponding calibrated model parameters are dynamically called for pre-labeling in subsequent labeling.

Labelling in machine learning is disclosed. Assistant models are trained to generate the labelling behavior of corresponding labelers. When an observation is received, the label generated by the assistant model is compared to the user-generated label. If the labels are different, an ensemble operation is performed wherein multiple assistant models, each configured to mimic the labelling behavior of a different user, are used to resolve the conflict and determine a final label for the observation. Labels generated by the ensemble operation may be incorporated into retraining operations performed to retrain the assistant models.

The invention belongs to the technical field of astronomical image data processing, particularly relates to an astronomical image data set labeling method, and aims to solve the problem that existing single threshold segmentation-based labeling and star map projection-based labeling are difficult to meet the requirement of a depth model for a high-quality data set. According to the method, on the basis of threshold segmentation labeling and star map projection labeling, a first labeling result and a difference set are adopted as a labeling result A for an observation image large-probability detectable target, a second labeling result is adopted as a labeling result B for an observation image small-probability detectable target, and the labeling result A and the labeling result B are integrated to obtain a labeling result A and a labeling result B; and obtaining a final labeling result. According to the method, the integrity of target labeling can be improved, the probability of missing labeling and wrong labeling can be reduced, the actual fitting degree of the labeling result and the image can be met, unification of integrity and accuracy is achieved, and the depth model performance can be improved through the manufactured data set.

The application provides a multimedia material label management method and system based on a multi-modal large model, which first performs hierarchical structural processing on multimedia material text information to obtain structured text information containing a text content hierarchical structure and semantic association information, then performs label group matching based on a preset tree-shaped label system to determine an associated target label group, then calls a multi-modal large model to execute a labeling scheme according to a target label group labeling scheme attribute to generate a label value labeling result and a weight value, integrates the labeling result and the weight value and performs screening and sorting to generate a final label set, and finally performs versioned maintenance management on the tree-shaped label system and the labeling scheme, thereby improving the efficiency, accuracy and adaptability of multimedia material label management and meeting the requirements of rapid retrieval and accurate query.

The application discloses a stamp retrieval method and system based on a knowledge graph, an electronic device and a medium. The method constructs a KV database based on a stamp image, multi-level labels and a first weight, and constructs a stamp knowledge graph according to stamp entity data and label entity data in the KV database. A plurality of target labels in a text to be queried are extracted, a plurality of candidate labels are obtained from the stamp knowledge graph according to the plurality of target labels, and a candidate label set is obtained. If the candidate label set contains only one label, the label in the candidate label set is expanded to obtain an expanded label set. The candidate label set and the expanded label set are merged to obtain a final label set. Based on the final label set, an incremental stamp set containing a first stamp and a stamp weight is constructed. According to the stamp weight, the first stamp in the incremental stamp set is sorted, so that a target stamp is retrieved according to a stamp sorting result. The application can improve the accuracy of stamp retrieval.

The invention relates to an annotation method and device based on reinforcement learning, computer equipment, a storage medium and a computer program product. The method comprises the steps of defining a labeling environment based on feature data corresponding to initial data; obtaining target feature data from the feature data, initializing an annotation reinforcement learning model according to the target feature data and a triple annotation result corresponding to the target feature data, enabling the initialized annotation reinforcement learning model to interact with the annotation environment, and outputting to obtain a final annotation strategy; and annotating data to be annotated through the final annotation strategy to obtain a triple annotation result of the data to be annotated. By adopting the method, the implicit relationship between the data can be identified, and the precision and efficiency of triple labeling are remarkably improved.

The application discloses a 3D target labeling method and system fusing a visual large model and point cloud projection, relates to the technical field of 3D target automatic labeling, and comprises the following steps: performing pixel-level semantic segmentation on a group of collected images and point clouds through a visual large model to obtain a segmentation graph of a target pixel region and a semantic label of each pixel; generating point clouds with semantic labels by using projection of the point clouds to an image plane and semantic mapping; clustering and processing foreground point clouds belonging to the same semantic label to obtain point cloud clusters belonging to the same semantic label; traversing the point cloud clusters, fitting a 3D bounding box, combining parameters of the fitted 3D target box with the same semantic label, generating 3D target labeling post-processing of the same semantic label, and obtaining a final labeling result file; and the 3D target labeling method realizes efficient and accurate 3D target automatic labeling, significantly reduces labeling cost, and improves the quality and consistency of labeled data.

In some aspects, a computing device can receive, at a data processing system, a set of utterances for training a named entity recognizer or for inference with a named entity recognizer to assign a label to each token segment in the set of utterances. The computing device can determine a length of each utterance in the set and, when the length of the utterance exceeds a predetermined threshold of token segments: divide the utterance into a plurality of overlapping token segment chunks; assign a label and a confidence score to each token segment in the token chunk; determine a final label and an associated confidence score for each token segment chunk by merging two confidence scores; determine a final annotated label for the utterance based at least on merging the two confidence scores; and store the final annotated label in a memory.

The application discloses a kind of based on AI big model's semi-automatic labeling and semi-automatic auditing method, it is related to image processing, target detection and artificial intelligence application field.The application includes: first step: using a big model and unsupervised labeling technology has obtained pre-labeling model, obtains preliminary preselection label frame;Second step: training a labeling effect auditing network, for excluding target frame that does not meet the requirements;Third step: based on the first two steps obtained target frame pre-extraction and target frame confidence analysis two network models, further labeling is carried out on the pre-labeled label screened by artificial, perfect label;Subsequently, based on SAM semantic segmentation big model for perfect label result, obtain target contour, to further screen target frame using label frame detection model, and the labeling result is audited, and the final labeling result is formed after auditing and perfecting.

The invention discloses a data security labeling method and system based on privacy computing, and relates to the technical field of data processing, and the method comprises the steps: receiving a labeling task request, decomposing the labeling task request into a plurality of sub-tasks based on a task analysis model, and classifying and routing the sub-tasks to a federated learning space, a trusted execution environment (TEE) space and a secure multi-party computing (SMC) space; in the federated learning space, executing a first type of subtasks based on a teacher-student model architecture; and executing the second type of subtasks in the TEE space, executing the third type of subtasks in the SMC space, and aggregating subtask processing results to generate a final labeling result. The technical problem that in the prior art, a complex data labeling task cannot be executed safely and with high quality under the condition that multi-mechanism data does not go out of a domain is solved, and the purpose that multi-mechanism data labeling is completed through cooperation of federated learning, a trusted execution environment and safe multi-party calculation is achieved. The technical effect of safe and high-quality data annotation processing on the premise that multi-mechanism data is not out of a domain is achieved.

The application provides a multi-modal data labeling method and device and a storage medium, and relates to the field of automatic driving. The method comprises the following steps: synchronously collecting multi-modal data and obtaining environment parameters; based on the environment parameters, obtaining modal reliabilities, judging a modal with the highest modal reliability as a reliable modal, and judging other modals as unreliable modals; a pre-labeling result of the reliable modal is a final labeling result of the reliable modal; the pre-labeling result of the reliable modal is transformed to obtain a projection result of the pre-labeling result of the reliable modal on the unreliable modals; the projection result of the reliable modals and the pre-labeling result of the unreliable modals are input into a target association module to output an intersection-over-union ratio; and the pre-labeling result of the unreliable modals is corrected according to the intersection-over-union ratio to obtain a final labeling result of the unreliable modals. The method effectively solves the problems of poor environment adaptability and insufficient multi-modal data fusion precision in the prior art.

The invention relates to the technical field of semiconductor manufacturing quality control, in particular to a space-time coupling virtual measurement intelligent labeling method based on feature space geometry, which comprises the following steps: S1, acquiring multi-dimensional sensor time sequence data of semiconductor manufacturing equipment as a labeling sample set, and performing manifold value evaluation on the labeling sample set; s2, establishing an adaptive time weight model based on data complexity, establishing a dynamic space weight model based on local sparseness based on an adaptive kernel function, combining the adaptive time weight model, the dynamic space weight model and manifold value evaluation, and defining a time-space coupling value score; s3, taking the time-space coupling value score as an input, and establishing a three-layer optimization architecture to realize a globally optimal sample combination; s4, combining the samples selected by each cluster to form a final label set, performing quality evaluation on the final label set, and generating standardized label output according to a quality evaluation result; and an accurate value basis is provided for subsequent intelligent selection.

The application relates to an intelligent label generation method and device, computer equipment and a storage medium. The method comprises the following steps: performing word segmentation calculation and text length judgment on filtered text, performing word segmentation weight calculation and keyword weight calculation on the filtered text according to the judgment result, extracting keywords from the filtered text according to the obtained word weight, judging the extracted keywords based on a deep learning BERT model, performing label feature word association matching by using the judgment result and a pre-set word library rule, performing weight summation calculation on an initial label by using a weight summation algorithm, performing label screening on a candidate label according to a pre-set rule, performing weight scaling calculation on the screened label, setting a rule word library based on a badcase and prior knowledge, performing mutual exclusion label processing on the scaled label according to the rule word library, and obtaining a final label. The method can improve the robustness of a label generation system.

The application discloses a kind of road scene automatic labeling method, it is related to the field of automatic driving, comprising: obtaining multimodal sensor data, time synchronization camera image and laser radar point cloud;The multimodal sensor data is preprocessed;The camera image and laser radar point cloud after pre-processing are input into pre-labeling model, and pre-labeling result containing target category, three-dimensional space information and uncertainty score is generated;According to the uncertainty score, the pre-labeling result is sorted to obtain the final labeling result.High-efficiency multi-sensor fusion is realized, and more rich labeling of semantic and geometric information is generated;Through uncertainty quantification, the labeling efficiency is greatly improved, and the labeling quality is guaranteed.

The invention relates to the technical field of intelligent recognition, in particular to a voice annotation method and system based on long and short audios, and the method comprises the steps: obtaining audio data containing long audios, and carrying out the preprocessing of the audio data; segmenting long audios in the audio data based on a voice endpoint detection method to generate short audios containing semantics; labeling the short audio containing semantics based on a deep learning model of Transform, and generating a first labeling result; performing manual annotation on the short audio containing semantics to generate a second annotation result; and generating a final labeling result based on the first labeling result and the second labeling result through a consistency check and fusion strategy method. According to the method, the results of two-time labeling can be integrated into the final labeling result through the consistency check and the fusion strategy, so that the consistency and the reliability of the labeling results are ensured.

The invention relates to the technical field of data labels, and particularly discloses a label setting method and system for unstructured data, and the method comprises the steps: carrying out the multi-dimensional preprocessing of the unstructured data, and obtaining standardized data; performing feature extraction on the standardized data by adopting a hybrid model fused with a feature extraction operator to obtain deep feature information of the data; generating a dynamic label set based on the deep feature information; matching the dynamic label set with the standardized data through a semantic association degree calculation method to obtain a preliminary label; and monitoring the dynamic change of the data and new business requirements by means of a time sequence feature tracking module, and updating and expanding the preliminary label through a label evolutionary algorithm to obtain a final label. According to the method, a multi-dimensional preprocessing technology is adopted, the quality and pertinence of preprocessed data are improved, and a good foundation is laid for subsequent feature extraction and label setting.

The application discloses a kind of intelligent labeling method, equipment and medium of text data, belong to electric digital data processing technical field, comprising: obtaining the label ontology of source field and target field;Based on the concept granularity, semantic boundary and axiom form between the two, construct the semantic alignment graph of cross ontology;According to the formal semantic equivalence determination of the logic rule of the graph to source field, generate the translated logic rule suitable for target field;After multi-label prediction is carried out to target text, the consistency check is carried out to candidate label combination using translation rule, and the final labeling result is output.The application solves the logic rule invalidation problem caused by label system isomerism and semantic boundary dynamic change in cross-domain scene by constructing the migration framework based on formal ontology semantic alignment and hierarchical translation verification, realizes the semantic fidelity and structure consistent translation of logic rule between heterogeneous knowledge system, effectively avoids semantic drift and conflict misjudgment.

The embodiment of the invention provides a tag generation method and device, equipment, a storage medium and a product. The tag generation method comprises the following steps: performing multi-round iteration on a tag set of a full-quantity file set comprising a plurality of files; in each round of iteration, the fitness of the label set is calculated, the label set is adjusted according to the fitness, and a final label set is output until a preset iteration termination condition is met; the fitness is calculated according to evaluation comprehensive parameters, and the evaluation comprehensive parameters at least comprise one of semantic relevancy between each file and a label set, relevancy among all files under the same label and repetition among all files under the same label; file preference dynamic vectors driven by importance and / or user access behavior of each file can also be selectively incorporated. According to the embodiment of the invention, the tag set is optimized through multiple rounds of iteration, and the fitness is calculated in combination with the related core parameters between the files and the tags and the selectable user related factors, so that intelligent tag generation of massive files is realized, and the file management retrieval efficiency and the service competitiveness are effectively improved.