1464results about "Still image data clustering/classification" patented technology

A method of training a text model using a plurality of text passage combinations, each text passage combination comprising a respective first text passage and a respective second text passage describing a same matter as the respective first text passage but being differently worded than the respective first text passage. The training comprises minimizing a measure of statistical difference between a respective value of a first text embedding and the corresponding value of a second text embedding over the plurality of text passage combinations. The method then comprises jointly training the text model and an image model based on plurality of image-text combinations, each comprising a respective image and a respective textual report describing the respective image. The joint training comprises minimizing a measure of statistical difference between the value of an image embedding and the corresponding value of a third text embedding over the plurality of image-text combinations.

The invention relates to the technical field of Otwei interactive maps, and discloses a geological data interaction method and system based on an Otwei interactive map. The method comprises the following steps: carrying out feature recognition and structural analysis on original geological data to obtain a standardized data packet, and establishing a geographic space reference conversion index table; performing feature extraction and classification on geological elements in the standardized data packet to obtain structured geological data; importing the structured geological data into an Ovoucher interactive map, and carrying out local registration through a mesh generation technology to obtain a visual geological element map layer; geological element drawing and attribute input are carried out, and an edited geological data set is obtained; and carrying out structure recombination and reverse coordinate conversion to obtain a standard format data file adaptive to the target geological information system. According to the method, accurate conversion of multi-source geological data between different coordinate systems and measuring scales is realized, and the problem of spatial dislocation during integration of different-source geological data in a traditional method is effectively solved.

The invention discloses a knowledge distillation method, electronic equipment and a computer readable storage medium, and relates to the technical field of artificial intelligence, and the method comprises the steps: carrying out the grouping of an attention map matrix of a teacher network based on the number of attention heads in an initial network structure of a student network, a high-dimensional attention space of a teacher network can be divided into subspaces with the same dimension as a student network, and attention map matrixes in groups are spliced, so that each group of spliced matrixes is aligned with an attention head of an initial network structure, a relatively regular corresponding relationship between the attention maps of the teacher network and the student network is realized, and the attention map splicing efficiency is improved. Therefore, the distillation loss can be calculated, and the knowledge of the complex teacher network can be accurately transmitted to the lightweight student network. Therefore, the problem that in a multi-head attention mechanism, the number difference between the teacher network and the student network hinders the student network to fully learn the teacher network knowledge can be solved, and the technical effects of improving the knowledge distillation effect and enhancing the student network performance are achieved.

The present application discloses an image generation method and apparatus, a device, a storage medium, and a vehicle. The method comprises: obtaining a prompt word input by a user, wherein the prompt word is used for instructing to generate a target image; on the basis of a preset first correspondence, determining an image generation model and a sampler corresponding to the prompt word, wherein the first correspondence is a correspondence among the prompt word, the image generation model and the sampler, and the sampler is used for controlling a noise reduction mode of the image generation model; performing text expansion on the prompt word to obtain a final text; loading the sampler into the image generation model to update noise reduction parameters of the image generation model; and using the image generation model with updated noise reduction parameters to convert the final text into the target image. According to the embodiments of the present application, the quality of generated images can be improved.

The invention discloses a method for detecting industrial hazardous gas leakage based on a multispectral infrared imaging system formed by a micro-lens array and a partition optical filter, which is used for solving the problem that the current infrared imaging equipment cannot identify various leaked gases, and belongs to the technical field of infrared imaging. The multispectral infrared imaging system is constructed on the basis of the microlens array and the partition optical filter, multispectral band design is carried out on common gas in the current petrochemical industry field, and the types of common leaked gas are identified by means of spectral characteristics of the leaked gas in an infrared multispectral channel. Firstly, a snapshot type infrared imaging system is constructed based on a micro-lens array and a partition optical filter, and the system comprises a plurality of spectral intervals which respectively correspond to optical absorption peaks of specific gas; imaging the hazardous gas based on a multispectral infrared imaging system to obtain a multispectral infrared image of a hazardous gas leakage scene; and finally, based on a foreground detection method, extracting gray information of the gas cloud target, constructing a spectral absorption normalization curve of the gas cloud target in each channel, and comparing the spectral absorption normalization curve with the spectral absorption curve in the database to judge the gas type of the current gas cloud target. The invention provides a reliable multispectral imaging scheme for a hazardous gas imaging system based on infrared imaging, gas types can be effectively distinguished during actual detection, interference of safe gases such as carbon dioxide and water vapor is eliminated, and the detection capability and practicability of an infrared imaging hazardous gas detection system are improved.

The invention provides a method and a device for generating a parameterized model, a medium and a program product. The method comprises the following steps: S1, receiving text data which is input by a user and is related to a design intention or a command; s2, preprocessing the text data through a predetermined natural language processing algorithm to obtain structured parameter information of the text data; and S3, constructing a parameterized model through predetermined modeling software according to the structured parameter information. By means of the technical scheme, the modeling efficiency can be improved, the quality and consistency of the model are guaranteed, and the maintainability of the model is further enhanced.

The invention provides a noise robust text-to-image pedestrian retrieval method and device based on a soft label. The method comprises the following steps: calculating cosine similarity based on image global features and text global features, and normalizing the cosine similarity to generate a soft label representing image-text pairing confidence; distributing a sample weight for each training sample according to the soft label, and obtaining a joint weight for current iteration in combination with a dynamic weight factor progressively increased along with the training process; respectively constructing cross-modal contrast learning loss and similarity distribution matching loss by using the joint weight, and carrying out weighted summation on the cross-modal contrast learning loss and the similarity distribution matching loss to obtain a total loss function; and updating parameters of the image encoder and the text encoder by using the total loss function until training convergence, and obtaining a cross-modal alignment model. According to the method, robust cross-modal alignment can be realized, and the pedestrian retrieval accuracy in a noise scene is improved.

The invention relates to the field of data visualization, in particular to a visual scheduling method and system for a scientific and technological achievement evaluation system. The method comprises the following steps: identifying system access behavior data of a user; performing attention hotspot field prediction on system access behavior data of the user, performing deep personalized demand evolution, and constructing a personalized scientific and technological achievement demand graph; real-time data flow matching retrieval is carried out according to the personalized scientific and technological achievement demand graph, semantic feature analysis is carried out, and deep semantic features of each text item and visual semantic features of each image and table item are extracted; and performing dynamic clustering processing according to the deep semantic features of each text item and the visual semantic features of each image and table item, and performing multi-dimensional scientific and technological achievement item aggregation to construct a scientific and technological achievement display library. According to the invention, comprehensive, dynamic, accurate and personalized scientific and technological achievement evaluation visualization is realized.

A method comprising generating image descriptions of images in an original training set of images; determining, using at least one LLM, at least one domain and / or class which is under-represented in the original training set; generating, using a second LLM and based on the determination of the at least one domain and / or class, at least one instruction for a third LLM to generate at least one text prompt; generating, using the third LLM and based on the at least one instruction, the at least one text prompt for a text-to-image model; generating, using the text-to-image model and based on the at least one text prompt, at least one synthetic image; and generating an enhanced training set of images for use in training an image processing machine learning, ML, model, the enhanced training set of images comprising the original training set of images and the at least one synthetic image.

The invention discloses a multi-modal power grid fault diagnosis method and system based on a causal event atlas, and belongs to the technical field of intelligent operation and maintenance of power systems. The method comprises the following steps: preprocessing historical fault case data of power grid equipment, and constructing a causal event atlas database; when a fault diagnosis request is received, analyzing the fault diagnosis request by the planning agent, generating an initial fault hypothesis set in combination with power field knowledge, endowing a corresponding credibility score to the fault hypothesis, retrieving the cause subgraph as evidence in an iterative loop, and updating the credibility score of the fault hypothesis by the reasoning agent; and generating a multi-modal diagnosis report after the termination condition is met. According to the method, the problems of insufficient causal modeling and poor interpretability of a traditional method are solved, and the diagnosis accuracy, efficiency and user credibility are remarkably improved.

The invention discloses a cross-domain expression motion unit detection method based on text bridging, and the method comprises the steps: extracting AU visual features of different scales through employing a visual encoder, introducing a group of learnable prompt vectors, and combining with AU text description to obtain AU text features; the source domain features and the text features are aligned through comparative learning, and the discrimination of the source domain AU visual features is improved; a cross-modal attention mechanism is adopted to realize preliminary interaction of vision and texts, and AU interaction feature representation is obtained; further promoting deep interaction and fusion of the text and the visual information through a graph neural network to obtain a fused AU feature; and on the basis of a negative sample AU description-based comparative learning method, target domain features are aligned with text features, and feature distribution differences between a source domain and a target domain are reduced. According to the method, the field invariance of the text is utilized, the visual features of the source domain and the target domain are unified, the text features are aligned, and the generalization performance of an AU detection model and the accuracy of the AU detection model on the target domain are remarkably improved.

The invention discloses an unmanned aerial vehicle-satellite scene matching method based on two stages. Precise scene matching and positioning are completed from coarse granularity to fine granularity. An existing satellite base map retrieval task is used as a first-stage task, and a local feature point matching method is introduced in a second stage. Compared with an existing method, the method has the advantages that the matching robustness in a complex scene is remarkably improved, meter-scale accurate positioning of the unmanned aerial vehicle can be realized on an existing satellite map, and a new universal solution is provided for the field of scene matching.

The invention discloses a lightweight gateway electric energy meter thermal defect target detection method based on improved YOLOv8s. The method comprises the following steps: firstly, extracting features by using MobileNetV4 as a backbone network; secondly, the Neck part is replaced with a CCFF feature fusion module in the RT-DETR model; in addition, a light-weight C2fStar module is provided in combination with the Star Block thought to replace an original C2f module; meanwhile, a lightweight shared convolution detection head LSCD is introduced; then, an original CIoU loss function is replaced by a Focaler-SIOU (System Identity of Units); and finally, pruning the network model by means of an LAMP mode. The method has the characteristics of light weight, real-time performance, high accuracy and the like.

The invention discloses a three-area three-line database automatic quality inspection evaluation method based on deep learning. The method comprises the following steps of S1, obtaining three-area three-line database data and performing preprocessing; s2, checking a directory structure and a file name of the original data set; s3, extracting spatial layer data from the hierarchical data set; s4, performing segmentation and attribute information extraction on various pattern spots in the layer data after spatial verification; s5, inputting the pattern spot relation characteristic data into an artificial fish swarm algorithm; s6, automatically comparing the pattern spot area and distribution data with a quality inspection standard preset in a three-area three-line database; and S7, performing parallel processing on an automatic comparison result through a multi-thread thread pool. According to the method, an artificial fish swarm algorithm, a relational attention network and a task allocation and thread pool management technology are combined, and three-area three-line database automatic quality inspection evaluation based on deep learning is realized.

The embodiment of the invention provides a recommendation method and device based on a large language model, and the method comprises the steps: obtaining at least one main body inquired by a user, and enabling the at least one main body to be obtained through recognition from a target image; obtaining first information of the user, wherein the first information is used for indicating the interest tendency of the user; determining a target subject in the at least one subject based on the first information; and through the large language model, based on the target subject, generating at least one item of recommended content for the user.

The invention discloses a dance costume prop retrieval method based on causal reasoning and cross-modal matching, which comprises the following steps: in a matching task, calculating the similarity between costume prop images and text matching features by a model, and judging whether the costume prop images and the text matching features are matched or not according to a threshold value; and when the task is retrieved, calculating the similarity between the query text and the image feature set, and sorting to obtain a result. The process of calculating the features and the similarity comprises the following steps: firstly, extracting multi-scale visual features of an image by using Faster R-CNN, and extracting multi-granularity semantic features of a text by using BERT; then, the two types of features are subjected to self-attention and gating fusion respectively and then are input into a Transform layer, and final visual and text representation is obtained; then, multi-head attention calculation is carried out on the two representations, self-attention calculation is carried out after sequences are combined, and matching features are obtained; and finally, dividing the dot product of the two feature vectors by the modular length product to calculate the similarity. According to the invention, the matching accuracy of dance costume props can be improved.

The invention relates to the technical field of wastewater treatment, in particular to a regulation and control method and system for an activated sludge process and electronic equipment. The method comprises the following steps: acquiring activated sludge property evaluation information; matching the activated sludge property evaluation information with data in an activity standard database of biochemical sludge; and implementing negative feedback optimization of the working condition of the activated sludge system on the activated sludge property evaluation information which is unqualified in matching with the activity standard database of the biochemical sludge. According to the regulation and control method of the activated sludge process, stable and reliable operation of the activated sludge process is achieved according to the constructed activity standard database of biochemical sludge, parameters such as system regulation and control logic and signal intensity of the method are subjected to mass data adjustment and calibration, self-adaptive regulation and control are achieved based on real-time monitoring results, timeliness is higher, and the method is suitable for popularization and application. The actual operation condition can continuously tend to an ideal condition point, the system unbalance risk is remarkably reduced, and the labor cost is greatly reduced.

The invention relates to the field of BIM modeling, in particular to a historical building BIM parametric modeling method. The method comprises the following steps that a historical building component classification database is constructed, the historical building component classification database divides historical building components into seven categories, each category is associated with a parameterization template, the parameterization template comprises geometric parameters and culture attribute fields, and the culture attribute fields are associated with a culture database. Historical building basic data are obtained, contour lines and feature points in the historical building basic data are extracted, and then the parameterized template is matched, so that a parameterized instance is obtained; and assembling the parameterized instances into a historical building BIM model, loading the BIM model and the multi-modal culture data in the culture database in a visual platform, and generating an interactive three-dimensional scene. The modeling of the historical building in a parameterized manner is realized, the engineering difficulty is reduced, and the finally generated three-dimensional scene is more real and immersive.

The invention discloses a CAD drawing instant positioning method and device based on spatial index analysis, and relates to the related technical field of CAD drawing positioning, and the method comprises the steps: carrying out the multi-stage splitting identification of a multi-version target CAD drawing; performing associated indexing on the drawing version information, the drawing layer information and the drawing component set; semantic analysis and spatial association are carried out on the drawing text annotations; embedding and associating the drawing component space semantic tag set into a component-layer-drawing three-level association graph; performing matching indexing on the drawing semantic query request based on the drawing spatial index mapping table; and inquiring a topology path based on the component to carry out instant positioning and visual feedback. The technical problems that in the prior art, complex spatial relations and semantic information in the drawings are difficult to effectively process, and multi-version CAD drawings are low in retrieval efficiency, insufficient in instantaneity and inaccurate in positioning are solved, and the technical effect of improving the retrieval efficiency, query instantaneity and positioning accuracy of the multi-version drawings is achieved.

The invention provides a method and system for constructing a geographic entity based on a DLG topographic map, and the method comprises the steps: taking the DLG topographic map as a data basis, and carrying out the objectification processing of basic geographic information elements; the method comprises the following steps: constructing a data graph based on an association relationship between geographic information elements and a target geographic entity in dimensions such as spatial scale, geometric morphology, geographic features, attribute expression and the like, belonging to a mapping conversion standard, and extracting an intermediate entity library from an objectified result library; entity construction operators are assembled according to needs to complete entity production scheme configuration, processing such as assembly and fusion is carried out on primitive objects in the middle entity library based on an entity production scheme, and rapid production and construction of geographic entity achievements are achieved.

Systems and methods for tracking inventory items in an area of real space are disclosed. The method includes receiving a signal generated in dependence on sensors. The signal indicates a change to a portion of an image of an area of real space. The method includes, in response to receiving the signal, implementing a trained location detection model to determine, based on inputs, whether an inventory item identified in the portion of the image has changed a position in the area of real space. The method includes implementing a trained item classification model to determine a classification of the inventory item. The method includes updating an inventory database with inventory item data determined in dependence on the classification of the inventory item to provide an updated map of the area of real space as a result of the received signal indicating the change to the portion of the image.

The invention discloses an automatic driving scene generation method and electronic equipment, and the method comprises the steps: obtaining a driving scene material used for generating an automatic driving scene, and obtaining driving scene description information used for describing the automatic driving scene according to the obtained driving scene material through employing a multi-mode large model; based on the obtained driving scene description information, generating a road network structure in an automatic driving scene by using a multi-modal large model and a traffic simulation device; based on the obtained driving scene description information, obtaining a driving scene script file used for generating an automatic driving scene by using a multi-modal large model; according to the obtained driving scene script file and the automatic driving simulator, controlling traffic facilities and road obstacles in the automatic driving scene to be displayed in the generated road network structure, and controlling traffic participants in the automatic driving scene to move in the generated road network structure so as to obtain the automatic driving scene. Therefore, the generation rate of the automatic driving scene is improved.

Embodiments of the present disclosure relate to multimodal AI-based search for digital assets via an indexing and / or search pipeline. With respect to the indexing pipeline, some embodiments obtain first data and second data associated with a first digital asset. Such data represents different data types or modalities of the same digital asset. After obtaining the first and second data, some embodiments then generate a composite index. After the composite index is built such index can then be used to execute a query via the search pipeline. To execute the query some embodiments compute a relevance score for each digital asset, of multiple digital assets, based at least in part on a measure in which each digital asset satisfies one or more parameters or conditions for two or more data types of the query. Various embodiments then rank each digital asset and present one or more associated indicators.

The invention relates to the technical field of computer vision and multi-modal data processing, and discloses a multi-modal picture data processing method and device, equipment and a storage medium, and the method comprises the steps: carrying out the multi-dimensional feature extraction of a pre-obtained standardized picture set, and obtaining composite feature data; inputting the composite feature data and a preset cue word into a multi-modal large model to generate a natural language description text, and extracting key semantic information from the natural language description text to form a structured semantic set; and performing associative storage on the standardized picture set, the composite feature data and the structured semantic set, and constructing a multi-modal index based on associative storage data. According to the method, deep understanding of multi-modal picture data is realized based on multi-dimensional feature extraction and semantic understanding of a multi-modal large model.

The invention discloses a multi-modal image searching method, and solves the problems that in the prior art, effective utilization of document image text information is lacked, and the multi-modal retrieval requirement in a complex scene is difficult to meet. According to the invention, the input image is classified by using the deep learning model, and the image classification result is obtained; the image classification result is a text image or a non-text image; generating a multi-dimensional composite feature vector for the text image; for a non-text image, generating a visual feature vector; retrieving a text image based on the multi-dimensional composite feature vector, calculating a comprehensive score by fusing the text semantic similarity, the abstract semantic similarity and the format visual similarity, and returning a retrieval result according to the comprehensive score; and for a non-text image, performing retrieval based on the cosine similarity of the visual feature vector, and returning a retrieval result.

A method of clustering patterns of an integrated circuit includes; providing a pattern image and numeric data, as input data corresponding to a first pattern to a first model, wherein the first model is trained by a plurality of sample images and a plurality of sample values, obtaining a content latent variable using the first model, and grouping a plurality of content latent variables corresponding to a plurality of patterns into a plurality of clusters based on a Euclidean distance, wherein the numeric data represents at least one attribute of the first pattern.

The embodiments of the present disclosure relate to a method and an apparatus, a device, a vehicle, and a medium for generating a classification model. The method for generating a classification model according to the embodiments of the present disclosure comprises detecting a plurality of images in association with a target text using a semantic text-image matching model, wherein the target text indicates a target scenario. The method further comprises generating an image sample set comprising image samples with category labels by determining the category to which each image of the plurality of images belongs. The method further comprises generating a classification model for the target scenario based on the image sample set, wherein generating the classification model is based on a pre-trained model using linear recognition.In this way, it is possible to quickly generate customized classification models for specific scenarios that meet actual requirements, improving flexibility and accuracy while ensuring the efficiency and stability of the model generation process.

The embodiment of the invention discloses a building engineering crack detection method and system based on image recognition, and the method comprises the steps: obtaining a building surface image, carrying out the preprocessing of the image, obtaining a standardized image, and carrying out the multi-scale decomposition extraction and integration of various features, and forming a multi-dimensional feature descriptor set; after feature importance is evaluated, a compact feature vector is generated through dimension reduction, quantization coding and compression, and then a multi-level feature index mechanism for optimized compression is constructed. A query feature vector is extracted from a newly collected image, searching and screening are completed by means of an index mechanism and a tolerance threshold, and a crack matching result is obtained; and based on the result, positioning cracks, classifying types, measuring parameters and evaluating severity, and generating a crack state report. The crack trend is analyzed in combination with the historical data time sequence, a multi-stage early warning mechanism is designed, maintenance suggestions are provided, and a real-time monitoring and early warning system is formed. According to the embodiment of the invention, the technical problems of high storage pressure and low real-time detection efficiency in the prior art can be effectively solved.

The invention relates to the technical field of data management, and discloses a database management system based on building remote sensing images. Comprising the following steps: identifying a target image from a building remote sensing image, dividing the target image into an image set, correspondingly importing the image set into a data layer of a library architecture, generating an image database, constructing an associated channel with an on-off point, configuring on-off conditions on the associated channel, analyzing a query demand from request data, and storing the query demand in the database. Selecting a corresponding query mode; compared with the prior art, the method has the advantages that the image association and query channels can be constructed among different data levels in the image database, and meanwhile, the on-off points configured with the on-off conditions are combined, so that the target images in the image database can be dynamically associated through the association channels on the basis of keeping independent, and the dynamic association efficiency is improved. Therefore, the dynamic storage association effect of the target image in the image database is realized, and the diversified query requirements of the image database are met.

The invention discloses a power grid GIS data editing method and system, electronic equipment and a storage medium. The method comprises the steps that a server constructs power grid vector slice data based on power grid GIS data and converts power grid equipment data into GeoJSON data; the client obtains and loads the vector slices, picks up a power grid graph from the vector slices according to the position of the edited equipment, adsorbs the power grid graph to the edited equipment, and requests the server for GeoJSON data of the corresponding equipment; the server performs query based on the edited device to obtain a device associated with the edited device, and sends GeoJSON data of the edited device and the associated device to the client; and the client establishes a relation with the GeoJSON data according to a pre-configured vector slice layer, captures an editing operation, superposes the vector slice and the GeoJSON data and then displays the superposed vector slice and GeoJSON data in real time. The problems of graph rendering and large data volume online editing are solved by using a data layering mechanism combining vector slicing and GeoJSON, and the rendering efficiency is improved.