63 results about "Text annotation" patented technology

Some examples of the disclosure are directed to object manipulators and associated processes for manipulating an object representation in a three-dimensional environment. The object representation may correspond to a scan of a real-world object in a real-world environment. The object manipulators may include an object cleanup manipulator and a text annotation manipulator. The object cleanup manipulator may be selectable to display one or more control affordances providing functionality for selectively removing portions of the object representation in the three-dimensional environment and / or selectively adjusting one or more parameters of the object representation in the three-dimensional environment. The text annotation manipulator may be selectable to display one or more control affordances providing functionality for selectively generating one or more text labels in the three-dimensional environment. The one or more text labels may be associated with the object representation in the three-dimensional environment.

A large language model (LLM) message generation system and method tor generating a response to a user message. The method includes: obtaining a text document specified by a user; chunking the text document into a plurality of chunks; generating text gloss data for each of the plurality of chunks based on the chunk and a predetermined prompt; storing the text gloss data for each chunk into a vector data store along with an identifier for the chunk and / or tine chunk itself; receiving a user message; querying the vector data store with a vector data store query, wherein the vector data store query is generated based on the user message; obtaining chunk(s) based on querying the vector data store with the vector data store query; generating a language model input based on the one or more identified chunks; and generating a response message based on inputting the language model input.

The embodiment of the invention relates to an autoregressive language model for video generation. Embodiments are provided for autoregressively generating a video using a video generation model. An aspect includes a method including performing a progressive multi-stage training process including a first stage and a second stage, where: the first stage includes training a video generation model to perform text-to-image generation; and the second stage comprises further training the video generation model using a training dataset comprising tagged video-text pairs, wherein further training the video generation model comprises: for each tagged video-text pair: generating at least one text tag using a text markup device and a text annotation of the tagged video-text pair; generating a plurality of video tags using a video tokenizer and a video of the tagged video-text pair; generating a frame tag from regression using the at least one text tag; and training a video generation model using loss values calculated from the frame tags and the video tags.

The invention relates to a PID (Proportion Integration Differentiation) drawing element intelligent identification and topology reconstruction method based on visual detection, which comprises the following steps of: cooperatively extracting multi-modal information, detecting components based on improved YOLOv11, identifying all text labels in a graph by adopting PaddleOCR, and carrying out pipeline identification algorithm and merging filtering method based on probability Hough transform of pixel points. Identifying and defining the T-shaped connection point as a special topological node to obtain positioning information of components and characters in a PID drawing, associating the components with the characters by using a regular expression and an Euclidean distance, and converting image elements into a topological graph model with a semantic relationship; according to the method, the end-to-end automation process of detection-association-reconstruction is achieved, finally, display is conducted in a graphical interface mode, a data basis is provided for subsequent application such as drawing analysis, system simulation and equipment management, end-to-end automatic conversion is achieved, and efficiency is greatly improved.

The invention relates to the technical field of data processing, and discloses an automobile fault code labeling method and device, computer equipment and a storage medium, and the automobile fault code labeling method comprises the steps: carrying out the recognition processing of fault maintenance data, generating structured data, carrying out the standardization processing of the structured data, and generating text verification data; fault code fields and state information are extracted based on the text verification data, preliminary annotation data are constructed, recognition enhancement processing is carried out according to the preliminary annotation data, and fusion annotation data are generated; carrying out maintenance part matching processing on the fusion annotation data to generate image-text annotation structure data; and performing format processing on the image-text annotation structure data to generate target automobile fault code annotation data. According to the method, the labeling capability of the complex maintenance data can be effectively improved, the manual operation burden is reduced, and the efficiency, accuracy and consistency of data labeling are greatly improved.

The invention provides a lead mark arrangement method and device, electronic equipment and a storage medium, relates to the technical field of computers, in particular to the fields of engineering drawing, computer aided design and the like, and can be used for application scenes such as drawing lead mark arrangement and the like. According to the specific implementation scheme, the method comprises the steps of obtaining a plurality of to-be-arranged lead marks and a plurality of mark objects; aiming at each to-be-arranged lead mark, determining a lead-out point coordinate of a lead based on a corresponding mark object; in response to a text arrangement mode selected by a user, determining text coordinates according to the extraction point coordinates; and generating a lead mark arrangement result according to the lead-out point coordinate and the text coordinate corresponding to each to-be-arranged lead mark. According to the scheme, the lead text annotation arrangement can be quickly carried out, various arrangement effects can be obtained by setting a simple arrangement mode, and the operation efficiency of arrangement and annotation when a user makes a picture is greatly improved.

The invention discloses a handwritten comment extraction method and device, equipment and a storage medium, and belongs to the technical field of handwritten comment extraction, and the method comprises the steps: extracting a comment image in a drawing image; extracting multi-scale text features and symbol features of the handwritten annotation information, identifying a text annotation according to the text features, identifying a non-text annotation according to the symbol features, and splicing the text annotation and the non-text annotation to obtain electronic annotation information; establishing a coordinate conversion relationship between the drawing image and the electronic drawing; position coordinates of the electronic annotation information on the electronic drawing are determined according to the coordinate conversion relation; and embedding the electronic annotation information into the electronic drawing according to the position coordinates to obtain the electronic drawing with the annotation information. According to the method, the electronic annotation information is recognized through the text features and the symbol features, the position coordinates are determined according to the dynamic mapping relation between the drawing photo with the annotation content and the electronic drawing, and the electronic annotation content is embedded into the electronic document according to the position coordinates.

The invention discloses a speech recognition model training method and device, equipment and a readable storage medium, and relates to the technical field of artificial intelligence. Comprising the following steps: firstly, acquiring voice training data and annotation data corresponding to the voice training data; the annotation data comprises text annotation data and intention annotation data; fuzzy processing is carried out on the text labeling data, and voice features of the voice training data are extracted; and training a speech recognition model based on the speech features, the text annotation data and the intention annotation data until the speech recognition model converges. According to the method, the text annotation data is fuzzified, some characters which are not concerned about intention classification are ignored, the speech recognition model is more focused on keywords, intention classification information is introduced in the training process, and the accuracy of the recognition result generated by the speech recognition model for intention classification is improved.

This invention proposes a method and system for recommending English annotation methods with low forgetting rate based on the Analytic Hierarchy Process (AHP), belonging to the fields of optimization and intelligent decision theory. First, from the perspective of English learners, this invention analyzes and identifies five key factors affecting the rate of forgetting English vocabulary. Then, using the Analytic Hierarchy Process (AHP), a weight matrix that passes consistency detection is constructed for each influencing factor, and the actual influencing factor values ​​are normalized based on fuzzy logic. Based on the weight matrix that passes consistency detection and the normalized influencing factor scores under different text annotation methods, the estimated forgetting rate of English vocabulary for English learners under the corresponding annotation methods is obtained. The annotation method with the lowest estimated forgetting rate is selected as the optimal recommended annotation method to assist English learners in vocabulary learning. Finally, a one-month experimental verification of the proposed recommendation algorithm is conducted, and the experimental results demonstrate the effectiveness of this invention.

The application provides a multi-screenshot editing and annotation system and a use method thereof, which comprises: a screenshot module, which realizes execution area screenshot, full-screen screenshot or window screenshot operation; an interface control module, which realizes dynamic adjustment of main interface size and menu layout according to screenshot size, controls interface top edge display and shelter area avoidance; a puzzle and overlay module, which realizes zooming, rotating, mirroring or transparency transformation of screenshots and arranges them in an automatic puzzle or overlay mode; a graphic editing module, which realizes addition / modification of graphic elements, realizes picture comparison and CAD auxiliary design function; a graphic and text annotation module, which realizes addition of graphic symbol marks, text annotation and mechanical drawing standard annotation; and a fast picture file management module, which realizes automatic saving of screenshots according to serial numbers, quick browsing and modification and user self-defined setting management. The application provides a more efficient and convenient multi-screenshot editing and annotation system and method, so as to simplify operation process, improve work efficiency and enhance user experience.

The invention relates to the field of artificial intelligence technology and medical information processing, and discloses a nursing teaching task-oriented nursing field text annotation corpus construction method, which comprises the following steps of S1, collecting original nursing text data and constructing a training data set; s2, nursing text data cleaning and entity labeling standardization; s3, constructing an entity recognition model oriented to the nursing field; s4, calculating a total loss function based on the main loss and the auxiliary loss; s5, training the entity recognition model by adopting the cleaned and standardized training data set; and S6, carrying out automatic labeling on the original nursing text by utilizing the entity recognition model. The method has the beneficial effects that the nursing field dynamic dictionary is constructed, and the fuzzy matching function fusing the editing distance similarity and the semantic similarity is introduced, so that spelling errors, term variants and the like in the original text can be intelligently mapped to the standard words, deep standardized cleaning of the nursing text is realized, and the user experience is improved. And the problem of data noise is effectively solved.

The invention discloses a large-model multi-agent-based bridge management and maintenance knowledge graph construction method, system and device and a medium, and the method comprises the steps: processing a bridge inspection report, converting an original text into a unit through a decomposition agent, and building a text annotation database; the method comprises the following steps: taking a subject text block as input, extracting a triple by an extraction agent, then checking whether the extracted triple conforms to a bridge detection domain ontology and domain knowledge or not by a verification agent, finally correcting the triple with errors by a correction agent, iteratively extracting, verifying and correcting the triple, and outputting a high-quality triple; establishing a dynamic knowledge learning mechanism, and updating a knowledge base; constructing an initial knowledge graph according to the verified knowledge triad; and the review agent reviews the initial knowledge graph and feeds back a review result to the construction agent for iterative updating, and finally, the bridge management and maintenance knowledge graph is constructed. According to the method, the accuracy and the fact consistency of the atlas data can be ensured.

Systems and methods for improving displays of media assets are disclosed herein. In an embodiment, a system receives a plurality of text comments from a plurality of devices to which a media asset was transmitted. The system analyzes the comments to identify text strings within the text comments. The system generates interactable elements from the text strings in the text comments, such that an interaction with the text string causes display of identifiers of media assets corresponding to the text string.

A training method includes: obtaining a synthesis text image set, a synthesis text image being obtained through synthesizing a real scenario background image and a random word, the synthesis text image being provided with a line text annotation box and a single character annotation box; training an initial algorithm network using the synthesis text image set, so as to obtain an intermediate model; processing a real scenario text image set using the intermediate model, so as to obtain a pseudo label of a real scenario text image, the real scenario text image being provided with a line text annotation box, the pseudo label being a single character annotation box; and training the intermediate model using the synthesis text image set and the real scenario text image set having the pseudo label, so as to obtain the single character detection model.

The embodiment of the invention discloses a machine-readable building engineering standard digital processing method and a machine-readable building engineering standard digital processing device. A specific embodiment of the method comprises the steps of generating text sequence information according to a first preset processing mode; generating layout information and bibliography and reference relation information according to preset layout annotation information, the layout level identification model and preset bibliography annotation information; text information is generated according to preset text labeling information and a paragraph extraction model; according to a pre-trained formula identification model, generating special format text information; determining the text sequence information, the layout information, the bibliography and reference relation information, the text information and the special format text information as construction standard control information; and generating control parameter information according to the construction standard control information, and controlling the construction robot to perform construction processing according to the control parameter information. According to the embodiment, the accuracy of obtaining the building engineering standard information is improved, the construction time consumption is shortened, and the waste of equipment resources is reduced.

The invention provides a CAD drawing information extraction method for constructing multiple agents based on a multi-modal large model, and belongs to the technical field of multi-modal large models. Geometric figure visual features and text annotation language features are extracted and aligned by using a spiral progressive combination network and a multi-head cross attention cross-modal semantic alignment model, and hierarchical attention processing is performed by using quadtree space division in combination with dense attention and a sparse global token communication mechanism. Initial information extraction and consistency check are carried out by utilizing an analysis agent and a verification agent, when the consistency confidence is lower than a preset threshold value, primitives are re-divided through minimum segmentation optimization, information extraction is carried out, and finally complete drawing information is output. The technical problem of high matching error rate of geometric primitives and text annotations caused by inaccurate cross-modal semantic alignment during CAD drawing information extraction is solved.

The application discloses a kind of system and method for quickly changing drawing unit in the field of mining engineering design and computer aided design technology, comprising: reading drawing file through CAD bottom interface, identifying the coordinate value and text content of dimension mark, text annotation and figure attribute parameter;Standardized conversion rule base is constructed, and the conversion rule and exception handling rule of dimension mark, text annotation and figure attribute parameter are set;According to the conversion rule and exception handling rule, the coordinate value and text content of the identified dimension mark, text annotation and figure attribute parameter are batch converted, and the figure coordinate and scale factor are synchronously adjusted;The converted drawing is checked for proportion consistency, and new drawing file conforming to target unit is generated.The application realizes batch conversion of mining drawing from millimeter to meter unit through programmed processing, improves the drawing collaboration efficiency of design and production unit, and guarantees the accuracy and standardization of engineering drawing.

The invention discloses a drawing update identification method and system, and the method comprises the following steps: S1, analyzing drawing files of new and old versions, and extracting multi-dimensional information including geometric primitives, layers and text annotations, S2, comparing the extracted multi-dimensional information of new and old versions, and identifying difference items, S3, based on a preset rule base, analyzing the identified difference items, and carrying out the identification of the new and old versions of the new and old versions of the new and old versions of the new and old versions of the new and old versions. The method comprises the following steps of S1, comparing and analyzing a drawing version, S2, automatically judging the change type and the influence level of the drawing version, S4, automatically generating a visual change report on the basis of a comparison and analysis result, highlighting a change area in a graphical mode by the report, and attaching a structured change list.According to the method, a traditional drawing version management mode is thoroughly innovated through an automatic, multi-dimensional and intelligent technical means; accurate, efficient and intelligent change identification is realized, project risks are effectively reduced, and cooperation efficiency and management level are improved.

This invention relates to the field of speech synthesis technology, specifically to a speech synthesis method based on an implicit continuous consistency model. The method involves constructing a dataset containing audio and its text annotations; building a residual vector quantization variational autoencoder, training it using a joint loss until convergence, and extracting the mean and variance of all audio data mapped to a latent vector distribution; sampling Gaussian noise latent variables based on the mean and variance, sampling Gaussian noise and time steps, and adding noise to obtain speaker features; calculating the continuous consistency loss using the time steps, speaker features, text, audio data, and the consistency model; and optimizing the continuous consistency loss until the consistency model converges. This method utilizes residual vector quantization technology to achieve high-rate audio feature compression and decoupling, and combines the single-step sampling characteristics of the consistency model in the latent space to improve the inference efficiency and training stability of speech synthesis, enabling the model to generate high-fidelity speech in a very small number of iterations.

Different methods and apparatuses applicable to prepare materials for a user. One embodiment includes a device, with an imaging sensor. Based on analyzing a user attribute from the imaging sensor monitoring the user, the device can identify and access an area of the materials for the user. The area includes a section, and the device can layout the section by keeping a piece of text together with an illustration to be displayed in at least two screens of different sizes. Another embodiment includes materials received by a mobile device with a text sub file including a piece of text, an illustration sub file including an illustration, and computer instructions to layout materials for the user in the mobile device, with at least the illustration linked to the piece of text. Annotations could be included in the layout.

Systems and methods are provided. In one example, a method includes presenting, via a graphical user interface (GUI), a GUI screen on a display of a computing device, wherein the GUI screen is configured to present textual information, and capturing an annotation made by a user on a portion of the GUI screen, wherein the annotation comprises a textual annotation, a drawing annotation, or a combination thereof. The method also includes deriving a context for the annotation based at least on the portion of the GUI screen having the annotation, wherein the context comprises a subset of the presented textual information, and creating a data store query based on the context and on the annotation. The method further includes querying, via the data store query, a data store, and presenting, via the GUI, a result based on the querying of the data store.

The present application discloses a multi-target tracking method, a multi-target tracking device, an electronic device, and a storage medium, and relates to the field of computer vision technology. The method comprises: obtaining an image feature vector and a text feature vector of a detection object in a first image frame, wherein the text feature vector is obtained by a text feature extraction module, and the text feature extraction module is trained based on a data set without text annotations; obtaining a first feature similarity between the image feature vector and an image feature template of a track to be matched, and obtaining a second feature similarity between the text feature vector and a text feature template of a track to be matched; performing tracking and matching based on the first feature similarity and the second feature similarity, and obtaining a track tracking result of the detection object in the first image frame and the track to be matched. This method can introduce semantic features into multi-target tracking while being free from the limitations of manually set text, thereby effectively improving the performance of multi-target tracking.

Provided are a data processing method and apparatus, and a model optimization method and apparatus. The data processing method comprises: respectively sampling in a plurality of data sets so as to determine target sample data on the basis of sampling results; inputting the target sample data into a plurality of language models for respectively processing to obtain target prediction data respectively outputted by the plurality of language models; using a preset text annotation model to perform text annotation processing on each piece of target prediction data to obtain text annotation information corresponding to each piece of target prediction data; and constructing a sample data group on the basis of the target sample data, the target prediction data, and the text annotation information, wherein the sample data group is used for executing a model training task and a model verification task.

The invention relates to the technical field of artificial intelligence, and discloses a text generation three-dimensional binary voxel data model training method, which comprises the following steps of S1, training a three-dimensional diffusion model by using three-dimensional voxel data without text annotation, so that the three-dimensional diffusion model learns basic cognition of a three-dimensional geometric structure; and S2, coupling a pre-trained language model with the three-dimensional diffusion model trained in the step S1, according to the text generation three-dimensional binary voxel data model training method, through unconstrained training and a gradual training strategy of freezing and unfreezing in sequence, the model loss value convergence is faster and more stable, the development and debugging period is shortened, and the development efficiency is improved. By jointly controlling the generation process through time step information and text embedding vectors, the model can perceive the current denoising stage, so that staged generation of early construction of a global structure and later refinement of local details is realized, and the mechanism fundamentally solves the problems of disordered structure and detail missing of an existing model generation result.

This invention discloses a controllable generation method for digital printing pattern layout based on multi-factor attention excitation, specifically including the following steps: Step 1, establishing a digital printing pattern dataset containing text annotations, which includes text descriptions, bounding box diagrams, and digital printing patterns; Step 2, training the model on the dataset constructed in Step 1 using the ControlNet framework based on a diffusion model to obtain model training weights; Step 3, using the weights trained in Step 2 for sampling inference, extracting cross-attention weights and self-attention weights during the inference process; Step 4, calculating the cross-attention and self-attention losses inside and outside the bounding box for the attention weights extracted in Step 3; Step 5, updating the noisy image through stepwise loss minimization and gradient descent; Step 6, generating a clear printing pattern through multi-step iterative denoising. This invention solves the problem of inaccurate layout control in existing pattern layout control methods.

The invention provides a cross-modal medical video segmentation method and system based on text reference, and relates to the technical field of medical video segmentation and computer vision, the cross-modal medical video segmentation method based on text reference comprises the following steps: collecting cross-domain medical videos and text labeling data, and constructing a text-medical video segmentation data set; constructing a cross-modal medical video segmentation model based on text reference, wherein the cross-modal medical video segmentation model comprises a coding module, a cross-modal time sequence context aggregation module and a decoding module; and iteratively training the cross-modal medical video segmentation model through the text-medical video segmentation data set until a training completion condition is reached, and outputting the segmented medical video by the trained cross-modal medical video segmentation model based on the input medical video and text information. According to the segmentation method, representative key frames can be obtained through segmentation, so that a doctor can provide more accurate diagnosis based on a dynamic image.

This invention relates to the field of artificial intelligence technology, specifically providing a text annotation method, system, device, and medium based on multi-model collaboration. The method includes: acquiring user-submitted text to be annotated, annotation requirements, and performance constraint parameters; parsing the annotation requirements to generate a standardized annotation task set and extracting text features; decomposing the tasks into atomic annotation tasks and constructing task execution paths based on dependencies; using a multi-attribute decision algorithm to match the optimal model for each atomic task based on text features, performance constraints, and a model profile library, and generating a scheduling plan according to the execution path; invoking multi-model collaborative inference to obtain the original annotation results, and outputting structured annotation results after fusion processing. This invention achieves dynamic decoupling between annotation tasks and models, supports multi-model collaborative inference and result verification, and significantly improves the flexibility, accuracy, and efficiency of text annotation.

This invention discloses a hierarchical multi-label attribution method and system that integrates atomic rule-driven credible features and knowledge distillation, belonging to the field of natural language processing technology. First, this invention constructs an atomic rule base for weakly supervised text annotation. Then, it uses a large language model as a teacher model to correct and supplement the weak annotation results, extracting the probability distribution of soft labels and intermediate layer feature representations on each level of labels. Next, it evaluates the credibility of the teacher model's output, selecting a subset of credible soft labels and credible feature dimensions. Then, it constructs a student model with a hierarchical output structure, designs a joint loss function, and distills the student model for training. Finally, it deploys only the student model for inference, outputting hierarchical multi-label attribution results and key evidence fragments. This invention, through the combination of atomic rules and credible knowledge distillation, significantly reduces inference costs while improving the accuracy, stability, and interpretability of hierarchical multi-label attribution.

The invention discloses an electric power inspection credible detection method and system based on a visual language model thinking chain and rule perception reinforcement learning, and relates to the technical field of computer vision, large model application and artificial intelligence safety monitoring. A large visual language model is driven to generate an explicit thinking chain text before outputting a detection box, and the problems that existing detectors such as YOLO / DETR are lack of semantic reasoning ability, black box decision cannot be explained and generalization ability is poor under the small sample condition are solved. According to the method, an end-to-end large visual language model architecture is adopted, a rule perception reinforcement learning mechanism is combined, an electric power safety regulation is converted into a computable logic reward function, and physical constraint and compliance verification are carried out on a reasoning process. According to the method, high-precision detection is guaranteed, semantic-level interpretation of violation behaviors is achieved, the data efficiency and the system credibility in a few-sample scene are remarkably improved, and the method is suitable for power operation safety monitoring.

The invention relates to a method for iteratively annotating textual data implemented in a computer device (CD), said method comprising the following at a current iteration (i): - receiving (S2) an unannotated text (Ti) as input from at least three different text annotation models, the at least three models (MA1, MA2, MA3) having been trained from a first corpus of training data comprising annotated textual data, - generating (S4) three annotated texts respectively (TA1i, TA2i, TA3i), - selecting (S5) one of the three annotated texts, the selected annotated text (TAsi) being the one that contains the fewest semantic and / or syntactic differences, compared with the other two annotated texts, - constructing (S7) a new corpus of annotated textual data (N_CO) by adding to it the selected annotated text (TAsi) and the unannotated text (Ti),said new corpus constituting a second corpus of training data for the text annotation model. Figure 3,