1222results about "Still image data querying" patented technology

The invention relates to the technical field of information retrieval, in particular to an image search method based on a multi-modal algorithm, which comprises the following steps: S1, acquiring image data and corresponding text description data, and generating a text feature vector; s2, constructing a multi-modal feature alignment model, and outputting an alignment feature vector; s3, performing feature fusion processing on the aligned feature vectors output in the S2; s4, hierarchical clustering is carried out, and a hierarchical feature index structure is generated; s5, receiving query data input by a user, and generating a query feature vector; and S6, matching the query feature vector generated in the step S5 with the hierarchical feature index generated in the step S4. According to the cross-modal image search method and device, through the multi-modal feature alignment, deep fusion and hierarchical index construction technologies, the precision, efficiency and robustness of cross-modal image search are remarkably improved, and the problems that in the prior art, modals are inconsistent, relevance is insufficient, and the query speed is low are solved.

The invention belongs to the field of cross-modal pedestrian re-identification, and discloses a cross-modal pedestrian re-identification method based on a coal mine scene, and the method comprises the steps: 1, obtaining an identity representing a pedestrian image and an identity tag corresponding to the pedestrian image, and carrying out the preprocessing; step 2, constructing a cross-modal pedestrian re-identification backbone network; step 3, constructing a multi-scale feature enhancement module and adding the multi-scale feature enhancement module into the cross-modal pedestrian re-identification backbone network; 4, determining a loss function, and completing the final training of the cross-modal pedestrian re-identification backbone network; and 5, taking one modal pedestrian image as a query set, taking the other modal pedestrian image as a to-be-retrieved set, and matching the pedestrian image in the query set with the pedestrian image in the to-be-retrieved set to obtain a cross-modal pedestrian re-identification result. According to the method, cross-modal image features can be effectively fused, feature information of pedestrian images can be effectively mined, and the prediction ability and generalization ability of the model in a cross-modal pedestrian image retrieval scene are effectively improved.

A data processing system implements obtaining an image of a document and an indication of one or more content items to generate; analyzing the image of the document to generate a textual representation of contents of the document in the image; constructing a query based on the textual representation; analyzing the query using a second machine learning model to obtain embeddings representing one or more categories of information represented in the query; searching a knowledge graph based on the query embeddings to obtain results of the query; providing the query results to a content generation unit to generate the one or more content items based on the results of the query; and obtaining the one or more content items from the content generate unit.

A method and apparatus for generating photorealistic synthetic images by receiving multiple forms and multiple instances of user input corresponding to a user's visual idea, executing an iterative image search to identify pre-existing images semantically aligned with the user's visual idea, and using an image synthesis deep learning model to generate at least one synthetic image based on the multiple forms and instances of user inputs.

Embodiments described herein provide systems and techniques for training large language models. In one aspect, a process for performing in-context training of a language model is disclosed. This process may begin by receiving a language model that includes a context window of a predetermined size, as well as receiving a set of in-context prompt / completion pairs prepared for a target task. The process then constructs a first token sequence based on the set of in-context prompt / completion pairs. Next, the process fits the first token sequence into the context window. The process subsequently performs a first in-context training pass using the first token sequence to train the language model to generate a next token in accordance with the target task.

The invention provides an archive retrieval method and system based on AI images and talent key information, and the method comprises the steps: collecting archive data, carrying out the preprocessing and feature extraction, carrying out the multi-modal fusion of features, obtaining multi-modal features, and mapping the multi-modal features to a unified feature space; generating high-order features by using the archive features, and combining the high-order features of different modals to generate global multi-modal feature representation; repairing a defect part in the archive image, outputting a repaired image, and then performing dynamic enhancement; converting the final multi-mode feature representation into a quantum state, performing amplitude amplification and search, extracting the most matched feature and decoding; extracting user preferences, optimizing retrieval result sorting, optimizing a model and recommending personalized services to the user; and dynamically adjusting the system state, optimizing the state space model to obtain an optimal behavior strategy, constraining the optimal behavior strategy, dynamically adjusting the multi-modal features, and performing incremental updating of the system.

The invention relates to the technical field of image recognition, in particular to a harmful image detection and recognition system and method based on agent collaboration, and the system comprises a first agent module, a second agent module, a third agent module, a fourth agent module and a fifth agent module, the first agent module is used for analyzing image element semantic information, outputting structured data and constructing an image comprehensive semantic knowledge graph; and the second intelligent agent module is used for carrying out harmful feature detection and associated query on the image in combination with the structured data, and outputting a coarse-grained harmful semantic detection result and the confidence coefficient of the coarse-grained harmful semantic detection result. According to the harmful image detection and identification system based on intelligent agent cooperation, through hierarchical processing and cooperative work of the first to fifth intelligent agents, comprehensive analysis of the image is realized to obtain meta-semantic information, coarse-grained and fine-grained harmful semantic detection is performed on the meta-semantic information, and through multiple rounds of challenge and dynamic comprehensive analysis, the detection and identification efficiency of the harmful image is improved. Hidden harmful information can be accurately identified, so that the accuracy and interpretability of the harmful information are improved.

The invention relates to a lightweight and privacy protection cross-modal retrieval method in an Internet of Things environment. The method comprises the steps that S1, a data owner generates and sends a secret key to a data user; s2, training the cross-modal feature extraction model; s3, the data owner inputs data to be uploaded into the trained model, and the obtained data features are converted into binary vectors; s4, encrypting the data obtained in the S3, generating a quadtree index, encrypting data to be uploaded, and sending the data and the quadtree index to a cloud server; s5, the data owner generates and sends AAI and digital signature information to the data user; s6, the data user converts the query data into binary vectors, encrypts the binary vectors and sends the binary vectors to the cloud server; s7, the cloud server generates a query result and integrity verification certification information and sends the query result and the integrity verification certification information to the data user; and S8, the data user verifies the query result, and when the query result is correct, the cloud server sends the encrypted data to the data user. According to the method, the complexity of a model for extracting data features is reduced while privacy protection is carried out on the data.

The invention provides a visualization system capable of synchronously displaying multiple types of data according to time. Comprising a distributed service cluster composed of a micro-service, a user management micro-service, a data transmission micro-service, a data storage management micro-service, a data extraction micro-service, a data visualization display micro-service, a data visualization template management micro-service and a resource monitoring micro-service; the micro-services are packaged in a containerized manner and interact by adopting a lightweight communication protocol, and automatic deployment and dynamic expansion are realized through a container arrangement tool; the system is provided with a basic parameter set with customizable micro-service configuration and a graphical configuration interface which are used for cooperatively generating a system operation environment configuration strategy. The user management micro-service establishes a multi-dimensional authority system based on a role access control model, and records an operation log to realize audit tracking; the data transmission micro-service integrates a plurality of heterogeneous data access modes, carries out transmission process verification and carries out secure channel transmission through the service gateway.

The invention provides a vehicle retrieval question and answer method, a controller and a vehicle, and the method comprises the steps: carrying out the analysis of a vehicle specification document through a pre-trained layout analysis model, and obtaining target information; the target information comprises a text and a picture; constructing a text vector database and an image vector database according to the target information; constructing a question proposed by a user, and retrieving the constructed question based on the text vector database and the image vector database to obtain an initial answer; reasoning the initial answer by using a multi-modal large model to obtain a target answer; the target answer comprises a picture and text content; therefore, after the question asked by the user is obtained, in order to improve the retrieval quality, the question needs to be constructed, so that the question better fits the understanding of the vehicle; and then the constructed questions are retrieved in the text vector database and the image vector database at the same time, and text answers and image answers are given after the initial answers are reasoned, so that the accuracy of vehicle reply is improved, a user can conveniently understand related operation steps, and the intelligence of the vehicle is improved.

The invention discloses a laser point cloud shielded vehicle completion method and device based on a thunder-vision fusion deep learning framework in the technical field of automatic driving environment perception. The method comprises the following steps: acquiring original laser point cloud data and an aerial image; constructing a fan-shaped shielding area based on the original laser point cloud data; identifying two-dimensional bounding boxes, orientations and category labels of all vehicle targets in the image; inputting the fan-shaped occlusion area and the two-dimensional bounding boxes, orientation and category labels of all the vehicle targets into a pre-trained double-branch deep learning network, and predicting whether an occluded vehicle exists or not and the position, orientation and category information of the occluded vehicle; and selecting a vehicle point cloud template based on the category information of the shielded vehicle, and then generating a scene point cloud after vehicle point cloud completion as a final result of vehicle completion in the shielded region. According to the invention, an image-point cloud space mapping mechanism based on the Leiyu fusion deep learning framework is introduced, so that the capability of automatically identifying and positioning the shielded vehicle is remarkably improved.

The invention relates to a Chinese language and literature computer networking query reading system. According to the system, a query request processing module carries out mixed feature analysis processing according to a query request of a user, and a mixed query vector fusing radicals and semantics is generated; a distributed retrieval module performs distributed retrieval in a preset space-time fragment index structure based on the mixed query vector to obtain a candidate literature data set; the preliminary retrieval optimization module is used for carrying out self-adaptive conversion processing on abnormal characters and traditional and simple characters in the candidate literature data set according to a Chinese character evolution rule and a context to obtain standardized text data; and the visual query output module performs knowledge graph enhancement processing on the standardized text data to generate a visual query result. According to the system, efficient query and visual display of Chinese language and literature works are achieved through multi-modal processing, multi-dimensional indexing and knowledge graph technologies, and the problems that a traditional query mode is single in input, limited in retrieval dimension, insufficient in text processing and the like are effectively solved.

When searching for data using information about drawings, it can flexibly accommodate a variety of use cases. [Solution] A program to be executed by a computer having a processor and a memory. The program causes the processor to execute an output step of inputting drawing information related to a drawing, estimate information related to an estimate for an illustrated object illustrated in the drawing, and a first prompt instructing output of first information related to the illustrated object to a generative AI model and outputting the first information from the generative AI model, a construction step of storing the outputted first information in a database and constructing a search database, an input step of accepting input of second information related to a search target, and a presentation step of searching the search database for first information similar to the second information based on the accepted second information, as similar information, and presenting search information related to the search result.

The invention provides an image data and pattern spot management system and method and electronic equipment, and relates to the technical field of geographic information data processing, and the image data and pattern spot management system comprises an image data indexing module which is used for carrying out three-dimensional indexing on a remote sensing image to obtain a space-time-data source association chain of the remote sensing image, the remote sensing image contains pattern spot data with an identifier; the pattern spot complete cycle construction module is used for performing multi-temporal analysis on the pattern spot data to obtain an analysis result, and constructing a pattern spot change life cycle map of the pattern spot data in combination with the association chain; and the tracing module is used for positioning the target pattern spot when the target pattern spot needs to be traced to obtain a change node of the target pattern spot and a space-time-data source index corresponding to the change node, and searching in the association chain according to the change node and the space-time-data source index to obtain a traceable data chain. According to the invention, the storage and management of the geographic image data and the pattern spot data are more systematized and refined.

The invention provides an image retrieval method and system supporting sharing of multiple data sources, and relates to the technical field of image retrieval, the method comprises the steps that secret keys are generated and distributed for retrieval members through a trusted third party, a data owner constructs a ciphertext cluster and a ciphertext image set based on a public key in an index secret key pair, an image secret key and an image set, and the ciphertext cluster and the ciphertext image set are stored; uploading to a first cloud server; the cloud server group re-encrypts the ciphertext cluster and constructs a hierarchical index; the query user processes the query image by using a public key in the retrieval key pair to obtain a query trap door, and uploads the query trap door to the first cloud server; the cloud server group re-encrypts the query trap door to obtain a new query trap door, and searches the hierarchical index based on the new query trap door to obtain a search result; and the query user obtains the image under the plaintext based on the search result. According to the method, the security of privacy information during retrieval is ensured, the complete secret culture of calculation in the retrieval process is realized, and the scene of multi-data source sharing is supported.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for query categorization based on image results. In one aspect, a method includes receiving images from image results responsive to a query, wherein each of the images is associated with an order in the image results and respective user behavior data for the image as a search result for the first query, and associating one or more of the first images with a plurality of annotations based on analysis of the selected first images' content.

Apparatus for visualization within a three-dimensional (3D) model and methods used therein are described, wherein the apparatus includes a processor and a memory communicatively connected to the processor, wherein the memory includes instructions configuring the processor to receive a query image, extract neural network encodings from the received query image, query a synthetic image repository for at least a matching synthetic image, and display an estimated position and orientation within the 3D model, wherein the synthetic image repository includes a plurality of synthetic images and their extracted neural network encodings, each synthetic image therein corresponds to a slice extracted at a specific position and orientation in the 3D model, and querying the synthetic image repository includes comparing the extracted neural network encodings between the query image and synthetic images.

The invention relates to the technical field of intelligent decision and the field of financial science and technology, and discloses a vehicle loss assessment result query method, which comprises the following steps that: a vehicle claim settlement operation end uploads vehicle insurance pictures to a background loss assessment processing end in batches, records a picture ID and an uploading request in a request result table during each uploading, and marks the receiving state of each vehicle insurance picture; and the background loss assessment processing end extracts a picture downloading task from the request result table, completes downloading of each car insurance picture, and updates a receiving state. After downloading is completed, the car insurance picture processing tasks are added into a task queue, the car insurance picture processing tasks are extracted from the task queue through a car insurance processing process, and corresponding damage form description is generated. Based on the generated damage form description, a corresponding repair scheme is generated in combination with the repair strategy set, and the repair scheme and the damage result are stored. The processing efficiency of the loss assessment process is improved, the integrity of the query result is ensured, and the claim settlement process is more efficient and smoother.

Systems and methods for image search result filtering can include obtaining a search query, determining a plurality of candidate image search results, processing the search query with a generative model to determine a plurality of search result criteria, and refining the plurality of candidate image search results based on determining whether the candidate results satisfy the plurality of search results criteria. The systems and methods can perform a plurality of determinations based on the output of the generative model.

The invention provides an AI image detection method and system for an electronic circuit board, and relates to the technical field of circuit board production and detection. Compared with a traditional artificial vision detection method, the method has the advantages that preliminary detection and accurate detection are carried out through real-time images of all production procedures of the circuit board, automatic defect detection of the electronic circuit board is achieved, manual participation is not needed, and the defect detection efficiency is improved. Besides, preliminary detection and accurate detection are performed through the first AI image detection model and the second AI image detection model, so that the resource occupation amount in the defect detection process can be reduced, the real-time performance of defect detection of the electronic circuit board is ensured, and the defect detection efficiency of the electronic circuit board is further improved.

The invention relates to a drawing detail table generation method and device, equipment, a storage medium and a program product. The method comprises the steps that firstly, a target three-dimensional model needing to generate a two-dimensional drawing detailed statement is obtained, then, a detailed statement generation range of the target three-dimensional model and detailed statement attribute parameters of the two-dimensional drawing detailed statement to be generated are obtained, then, first information of parts, located in the detailed statement generation range, in the target three-dimensional model is obtained, and finally, first information of the parts located in the detailed statement generation range in the target three-dimensional model is obtained; and according to the obtained first information and the detail list attribute parameters, generating a two-dimensional drawing detail list. By adopting the method, the two-dimensional drawing detail table of the target three-dimensional model including the parts in the generation range can be automatically generated, and the drawing details of the parts do not need to be generated by manually projecting the target three-dimensional model, so that the generation efficiency and accuracy of the drawing detail table are higher.

The invention relates to a task distribution method and system based on a zero-work market, and a terminal, and relates to the technical field of the zero-work market, and the method comprises the steps: obtaining a preset trigger signal of a mobile terminal; when the trigger signal is consistent with a preset search signal, controlling the mobile terminal to carry out article shooting prompt to obtain article image information; determining article features in the image according to the article image information; determining whether a preset domain database contains an article corresponding to the article feature in the graph or not; if the domain database contains an article corresponding to the article feature in the graph, determining a specific shot article according to the article feature in the graph; determining an appropriate working field and an appropriate field number according to the specific shot object; and when the proper field number does not exceed the preset field excessive number, controlling the mobile terminal to display the working content of the proper working field according to the proper working field. The task allocation method and device have the effect of improving the task allocation efficiency.

The invention provides a molten steel transportation track playback method and device, electronic equipment and a medium, and the method comprises the steps: processing sample transportation data, extracting key transportation track information, and rapidly and accurately obtaining target track data through a preset query condition; the timestamp data provides a basis for the time sequence analysis of the track, each important node in the transportation process can be clearly reflected, the acquisition of the image data enables a user to check the state at a specific time point, the operability of the data is enhanced, track playback is performed by establishing a thread, the dynamic display of the transportation process can be realized, and the transportation efficiency is improved. And through graphical operation and abstract map display, the historical production condition can be backtracked more easily, and the user experience is better.

The invention provides a training method, a search method, a device and equipment for an image character search model, which are applied to the technical field of artificial intelligence, and the training method comprises the following steps: firstly, constructing a data set comprising a base image face image, a target image and a sample label; then, inputting the base image and the target image into an image character search model, extracting feature maps under different scales, and performing feature comparison to generate a similarity feature map; and outputting a face matching result by integrating the similarity feature maps of different scales. And adjusting model parameters according to the face matching result of each group of data by utilizing a preset loss function, and finally obtaining a trained image character search model. According to the method, feature maps of the base map face image and the target image are extracted under multiple scales and are compared, so that detail features of the face on base maps of different scenes can be captured more effectively, non-compliant human portraits can be quickly detected and accurately recognized in a complex background, and compliance of media content is ensured.

Systems and methods for image generation are described. Embodiments of the present disclosure receive a text phrase that describes a target image to be generated; generate text features based on the text phrase; retrieve a search image based on the text phrase; and generate the target image using an image generation network based on the text features and the search image.

The invention provides a deep hash image retrieval method based on hierarchical multi-scale feature fusion. The method comprises the following steps: firstly, adopting a Nested Hierarchical Transform as a backbone feature extraction network, and synchronously capturing global context information and local detail features of an image through a nested local self-attention mechanism and a hierarchical feature aggregation module; then, constructing a multi-scale feature fusion module, extracting visual features of different receptive fields in parallel by using a multi-branch unequal-ratio expansion rate dilated convolution kernel, and adaptively weighting important features in combination with attention to generate discriminative multi-scale fusion features; and finally, designing a mixed loss function, optimizing intra-class feature compactness through center similarity loss, and realizing efficient and semantic similarity keeping Hash code generation in combination with a quantization loss constraint Hash code discretization process. According to the method, full expression of image features is realized through a collaborative mechanism of Transform and multi-scale feature fusion, and the precision and efficiency of large-scale image retrieval are improved through an end-to-end deep hash learning framework.

The invention relates to the technical field of intelligent information retrieval, in particular to an image-text retrieval generation method and device based on reference semantics and electronic equipment. The method comprises the steps of performing semantic segmentation on a document in a document knowledge base to obtain a plurality of paragraphs, performing semantic similarity judgment, and merging similar paragraphs; extracting semantic features of the merged paragraphs to form semantic features of lower-layer paragraphs; abstract description is carried out on the merged paragraphs, semantic features are extracted from abstract description, upper layer description semantic features are formed, and text level semantic features are constructed; image semantic features in the image knowledge base are extracted, normalization processing is carried out on the text level semantic features and the image semantic features, an inverted product quantitative index is constructed, and construction of an image-text reference semantic feature index is achieved; and processing the input text based on the image-text reference semantic feature index to obtain a retrieval result. According to the method, the problems of lack of previous knowledge, insufficient detail information and poor image-text relevance in image-text retrieval are solved.

The invention discloses a professional knowledge domain data processing method based on a large language model, and relates to the technical field of natural language processing, and the method comprises the following specific steps: knowledge graph-driven multi-modal preprocessing: collecting professional domain knowledge graph, text, image and audio data, according to the method, the data quality is effectively improved through multi-modal preprocessing driven by the knowledge graph, a solid foundation is laid for subsequent processing, the attention regulation and control algorithm of multi-modal perception is combined, a large language model can dynamically adjust attention according to modal features such as text context, images and audios, key information is accurately captured, and the method is suitable for popularization and application. The comprehensive understanding and processing capability of professional knowledge data is remarkably improved, and in addition, the generalization capability of the model is further enhanced through the modal self-adaptive data enhancement method, so that the model can better deal with diversity and complexity in the professional field.

Provided in the present invention are a method and system for importing data of a graph database, an electronic device, and a medium. The system comprises an import task initiation module, an import task execution module and an import task monitoring module, wherein the import task initiation module is used for: configuring graph database information, configuring a graph data format conversion script, reading a data file row by row, and calling an API to submit an import task; the import task execution module is used for: receiving the import task by means of the API, allocating the task to a data import cluster, reading graph database configuration information, reading a data dynamic processing script, analyzing data to obtain a graph database insertion statement, and executing the insertion statement to write the data into a graph database; and the import task monitoring module is used for: collecting import execution information, summarizing the data, and generating an import task execution condition report. The method of the present invention supports efficient import of large-scale graph data, is simple to implement, supports data files in different formats, and can allow a user to check import progress at any time.