2560results about "Metadata still image retrieval" patented technology

The technology disclosed assigns quality scores to bases called by a neural network-based base caller by (i) quantizing classification scores of predicted base calls produced by the neural network-based base caller in response to processing training data during training, (ii) selecting a set of quantized classification scores, (iii) for each quantized classification score in the set, determining a base calling error rate by comparing its predicted base calls to corresponding ground truth base calls, (iv) determining a fit between the quantized classification scores and their base calling error rates, and (v) correlating the quality scores to the quantized classification scores based on the fit.

The invention discloses a transformer substation engineering construction drawing intelligent analysis and evaluation system based on image recognition, and relates to the technical field of transformer substation engineering construction informatization. The data processing center is in communication connection with a data acquisition layer, an image preprocessing layer, a feature extraction layer, an intelligent analysis layer, an evaluation decision-making layer and an application display layer, and all layers of architectures are in electric signal connection. According to the method, the improved convolutional neural network model is adopted, and an image preprocessing algorithm specially aiming at engineering drawing feature optimization is combined, so that the drawing recognition accuracy is remarkably improved compared with a traditional OCR technology, the manual proofreading workload is greatly reduced, a drawing element correlation analysis system based on a knowledge graph is constructed, and the engineering drawing recognition accuracy is improved. Key information such as equipment arrangement, line connection and size marking in the drawing can be automatically extracted and associated, and structural storage and intelligent retrieval of drawing content are achieved.

Techniques for learning to personalize vision-language models through meta-personalization are described. In one embodiment, one or more processing devices lock a pre-trained vision-language model (VLM) during a training phase. The processing devices train the pre-trained VLM to augment a text encoder of the pre-trained VLM with a set of general named video instances to form a meta-personalized VLM, the meta-personalized VLM to include global category features. The processing devices test the meta-personalized VLM to adapt the text encoder with a set of personal named video instances to form a personal VLM, the personal VLM comprising the global category features personalized with a set of personal instance weights to form a personal instance token associated with the user. Other embodiments are described and claimed.

The invention provides a robust multi-modal image segmentation method and system based on instance perception query, and the method comprises the steps: obtaining original image data and text description data, and generating training data through semantic analysis and causal graph construction; extracting image features and text features by adopting a multi-layer coding network, and establishing a feature dependency relationship through causal reasoning; generating instance clustering features based on attention calculation, and obtaining fusion features in combination with a multi-scale fusion strategy; and calculating query confidence and generating candidate segmentation masks, and outputting a final segmentation result through post-processing optimization. According to the method, by introducing causal reasoning and instance awareness query mechanisms, the cross-modal feature alignment precision and the robustness of the segmentation result are improved, and the method has better segmentation performance and generalization ability in a complex scene.

The invention relates to the technical field of civil engineering informatization and automation design, in particular to a parameterized construction drawing generation method based on a constraint solution algorithm, and the method comprises the steps: defining a construction drawing foundation structure and variable parameters through an engineering design model embedded with a parameterized template; a parameter constraint relation set is generated in combination with geometric constraints, size constraints and logic constraints input by a user, constraint rules are converted into intermediate expressions through a formula analyzer, a syntax tree is constructed, and an executable parameter association rule set is generated through semantic verification. The auditing module verifies the drawing compliance based on the engineering specification database and generates a correction suggestion; and realizing cross-system data linkage between the multi-terminal collaborative interface and external BIM model parameter mapping. According to the method, the problems of detail loss and specification deviation in three-dimensional model projection are effectively avoided, and the drawing generation efficiency and the multi-specialty collaborative robustness are improved.

The invention discloses a cross-modal Hash retrieval method based on prompt learning and adaptive Mama gating selection fusion, and mainly relates to the field of deep cross-modal Hash retrieval. Comprising the following steps: S1, respectively and correspondingly dividing an image data set and category label information corresponding to the image data set into a training set and a retrieval set; s2, preprocessing the image information; s3, constructing a cross-modal Hash retrieval framework based on prompt learning and adaptive Mamba gating selection fusion; s4, obtaining a model through training by using the training set; s5, inputting the images of the retrieval set into the trained model; s6, storing the best model weight of the test set, and storing the hash code with the best performance effect on the test set; according to the method, the occurrence of semantic truncation can be relieved, the deficiency of context semantics can be supplemented, and different modal features can be reselected and fused, so that the semantic feature weight beneficial to a retrieval result is improved, and the weight of negative semantic information is reduced.

The invention discloses a safe and rapid storage method and system for image data of an imaging department, and relates to the technical field of medical image data processing. The method comprises the following steps: 1, designing a distributed storage system, and selecting and configuring storage nodes; 2, selecting and updating an encryption algorithm, and implementing file-level and block-level encryption; 3, realizing accurate data deduplication by utilizing a machine learning technology, and selecting a proper compression algorithm; 4, constructing an index system, and establishing a data warehouse and a data lake by using big data analysis and machine learning technologies; 5, establishing an image data backup and recovery mechanism; and 6, constructing a data integrity checking and monitoring system. According to the invention, by adopting the technologies of distributed storage, encryption, data deduplication, compression, big data analysis, machine learning, backup and recovery, data integrity verification, monitoring and early warning and the like, efficient storage and safety protection of image data are realized.

The invention provides an intelligent retrieval matching method and system for multidimensional data of image-text features, and relates to the technical field of icon image retrieval. Comprising the following steps: extracting image features, text content and semantic features of an icon image by using a convolutional neural network, an image segmentation attention mechanism network, a converter optical character recognition model and a bidirectional semantic understanding model, constructing the extracted features into heterogeneous feature tensors, and performing singular value decomposition to obtain icon feature fingerprint vectors; and constructing a multi-level index based on locality sensitive hashing, realizing rapid retrieval, calculating visual, text and semantic similarities in combination with a deep metric learning model, weighting according to variances and discrimination coefficients of similarity features to obtain a comprehensive similarity score, and outputting a retrieval result with the highest similarity.

An online concierge system trains a fine-tuned generative image model for distinct categories of items based on a generative image model that takes a textual query as input and outputs and an associated image. Training of the fine-tuned generative image model is additionally based on a small set of representative images associated with the various categories, as well as textual tokens associated with the categories. Once trained, the fine-tuned generative image model can be used to generate realistic representative images for items in a database of the online concierge system that are lacking associated images. The fine-tuned model permits the generation of different variants of an item, such as different quantities or amounts, different packaging or packing density, and the like.

The invention relates to the technical field of electric digital data processing, in particular to a traditional Chinese medicine data management system and method for data retrieval. The method comprises the following steps: acquiring morphology data of a traditional Chinese medicinal material sample; performing grade evaluation based on the traditional Chinese medicinal material morphology data to generate traditional Chinese medicinal material grade label data; acquiring a to-be-detected traditional Chinese medicinal material image; preprocessing the to-be-detected traditional Chinese medicinal material image, and performing entity recognition and relation extraction to obtain traditional Chinese medicinal material semantic annotation data; and fusing the traditional Chinese medicinal material grade label data and the traditional Chinese medicinal material semantic annotation data to obtain traditional Chinese medicinal material semantic annotation enhanced data. According to the method, the information of the traditional Chinese medicinal materials is expressed through structured processing and semantic enhancement, the knowledge graph containing dominant and implicit semantic relationships is constructed, and the retrieval depth, flexibility and intelligent application of knowledge of the traditional Chinese medicinal materials are improved.

The invention relates to a hospital database management method and system based on adaptive data desensitization. The method comprises the following steps: S1, uniformly integrating multi-source data of a hospital into a database; s2, performing sensitivity grading on the data of the database through a data classification algorithm, and automatically generating corresponding labels; s3, establishing a desensitization model based on role permission, access content and data features; s4, the user inquires data and inputs related parameters through the front-end system, whether fields or data in the request contain high-sensitivity fields or not is judged, and if the fields or the data contain the high-sensitivity fields, a proper desensitization strategy is dynamically generated based on a desensitization model according to the user role, the data sensitivity and the access context; and S5, monitoring access behaviors in combination with data using log records, carrying out anomaly detection based on a GANopen model, and if an abnormal behavior is detected, triggering an alarm or stopping access. According to the invention, efficient utilization and sharing of data are realized, and the intelligent level of hospital data management is improved.

A method of automated generation of contextually-relevant images for a music segment includes receiving at least one of basic metadata information and lyric information for the music segment, generating a first prompt for a computer-implemented machine-learning language model based on the at least one of the basic metadata information and the lyric information, receiving context information from the computer-implemented machine-learning language model in response to the first prompt, generating a second prompt for the computer-implemented machine-learning language model based on the context information, generating a third prompt by providing the second prompt as an input to the computer-implemented machine-learning language model, and generating an image descriptive of the music segment by providing the third prompt as an input to a computer-implemented machine-learning image generation model.

A method includes obtaining, by one or more processors, a plurality of images, executing, by the one or more processors, a domain-specific target audience machine learning model to generate domain performance scores for the plurality of images, ranking, by the one or more processors, the plurality of images according to the domain performance scores for the plurality of images, and generating, by the one or more processors, a record comprising one or more images of the plurality of images based on the rankings of the plurality of images.

The invention discloses an image cross-modal retrieval method and device based on a large language model and a medium, and the method comprises the steps: generating a text description corresponding to an original image through a BLIP model, and converting the text description into a corresponding image vector; establishing a mapping relationship between the image vector and the original image, and storing the mapping relationship and the image vector to a preset vector database; obtaining an image query description submitted by a user, optimizing the image query description through a preset language large model, and converting the optimized image query description into a corresponding query vector; determining a plurality of to-be-selected images corresponding to the image query description according to a first similarity between each image vector and the query vector in a vector database; and calculating a matching degree between the image query description and the plurality of to-be-selected images, and screening out a target image meeting the image query description from the plurality of to-be-selected images according to the matching degree.

The invention discloses a defect file and image number association method based on multi-format analysis, and relates to the technical field of semiconductor detection and data analysis, and the method comprises the following steps: constructing a multi-format rule base, receiving batch Klarf files and corresponding defect image folders, and extracting image numbers in defect image file names; reading field names of a preset number of Defect data of the Klarf file, matching the field names with the multi-format rule base, and selecting an analysis strategy according to a matching result by adopting a self-adaptive analysis algorithm; the analyzed Defect data are preprocessed, the association confidence coefficient is calculated through a three-layer verification algorithm, and an association result table is generated; according to the invention, automation, standardization and high reliability of semiconductor defect detection data processing are realized.

The invention provides a similar picture retrieval method, which comprises the following steps of: constructing a hierarchical semantic-visual representation framework, and decomposing query and images into three levels, namely a core concept, attribute modification and relation description; a context-aware professional term understanding system is achieved, the professional field background and purpose of query are analyzed, context feature vectors are generated, and term ambiguity is eliminated; establishing a semantic-vision bidirectional mapping and optimization framework, realizing forward mapping from semantics to vision and reverse mapping from vision to semantics, and generating an optimization mapping matrix and a candidate set through iterative optimization; and performing multi-modal similarity calculation, converting the context feature vector into a hierarchical weight vector, fusing the hierarchical weight vector with the multi-level similarity into a comprehensive similarity score, and sorting and outputting a retrieval result set according to the comprehensive similarity score. According to the method, the accuracy of similar picture retrieval in the professional field is improved.

The present disclosure presents a generative model configured to receive input regarding an item in two different modalities, such as text data and non-text data (including, for example, image or audio data), in order to generate output regarding the item that is determined based on a combination of both modalities' input. Specific relative positional and token type embeddings may be employed in an encoder portion of an encoder-decoder arrangement. An associated decoder may be trained to generate new text corresponding to diverse tasks based on the encoded representation of the two inputs as generated within the encoder. For example, the decoder may be utilized to generate attributes regarding the input item, auto-complete or auto-correct a title or description of the item, among other uses.

The present technology provides for AI-assisted video editing tools that implement a novel method of processing video which includes transcribing segments of a video and labeling the segments based on the contents of the segments. A smart edit can be generated based on the labelled segments. The smart edit can serve as a rough cut for a user to make further edits.

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.

The invention provides a multi-modal geographic positioning method based on a knowledge graph. The method comprises the following steps: graph construction: constructing a geographic knowledge graph; image positioning: calculating the entity similarity between the to-be-queried image and each sub-image so as to select an image positioning candidate sub-image, calculating the image matching similarity between the to-be-queried image and each image positioning candidate sub-image so as to determine an image positioning target sub-image, and taking the latitude and longitude coordinates of each image positioning target sub-image as a positioning result; text positioning: converting a to-be-queried text into map representation, calculating the map similarity between a text query sub-graph and each sub-graph to select a text positioning candidate sub-graph, and calculating the semantic similarity between the to-be-queried text and each text positioning candidate sub-graph, and calculating a text matching similarity corresponding to each text positioning candidate sub-graph based on the graph similarity and the semantic similarity corresponding to each text positioning candidate sub-graph so as to determine a text positioning target sub-graph, and taking latitude and longitude coordinates of each text positioning target sub-graph as a positioning result.

The invention discloses a data enhancement method and device based on multi-modal language alignment, and the method comprises the steps: collecting basic information of a patient in an electronic medical record system, a diagnosis report during an observation window, and various types of image data, and carrying out the preprocessing of the basic information, the diagnosis report and the various types of image data; respectively carrying out image data coding and text data coding on the preprocessed image and the preprocessed diagnosis report to obtain coded image embedding representation and text embedding representation; performing modal projection fusion on the encoded image embedding representation and text embedding representation, constructing a loss function through a comparative learning method, and aligning the image embedding representation and the text embedding representation through the loss function of minimizing comparative learning; image-text pair data is generated through a decoding method based on language similarity image-text pair generation. According to the invention, images, texts and images of different modalities can be distinguished; and data of a specified mode can be generated, so that the problem of lack of data volume in multi-mode large model research is solved.

Described herein are machine learning (ML) based on systems, methods, and instrumentalities associated with image search and / or retrieval. An apparatus as described herein may obtain a query image and a textual description associated with the query image, and generate, using an artificial neural network (ANN), a feature representation that may represent the image and the textual description as an associated pair. Based on the feature representation, the apparatus may identify one or more images from an image repository and provide an indication regarding the one or more identified images, for example, as a ranked list.

The embodiment of the invention provides a closed set domain adaptive remote sensing image classification retrieval method fusing a space domain and a frequency domain, which belongs to the technical field of data processing, and specifically comprises the following steps: establishing a closed set domain adaptive model fusing the space domain and the frequency domain of a remote sensing image; establishing a model training strategy; dividing the sample data set into a training set, a test set and a verification set according to a preset proportion, training a closed set domain adaptation model through the training set and a model training strategy, and testing and verifying the closed set domain adaptation model through the test set and the verification set to obtain a trained classification retrieval model; and inputting the target remote sensing image into the trained classification retrieval model to obtain a classification result and a retrieval result. By means of the scheme, the problem that due to the influence of remote sensing image background complexity and multi-source data distribution difference, a complex remote sensing image cross-domain classification retrieval task only focuses on spatial domain information, and a good generalization result cannot be obtained is solved, and classification retrieval accuracy and adaptability are improved.

The invention discloses a digital reading system based on a knowledge graph and a text image recognition technology. The system comprises a collection module used for collecting financial data and a first financial document from a plurality of financial data sources; the construction module is used for identifying a target entity in the financial field in the financial data and constructing a graph structure of a financial knowledge graph; the first analysis module is used for receiving a second financial document uploaded by the user, identifying a target keyword in the second financial document and generating a mind map corresponding to the target keyword according to a preset hierarchical structure; the second analysis module is used for identifying document elements in the first financial document; the typesetting module is used for acquiring screen information of the mobile terminal equipment; the watermark adding module is used for generating a digital watermark according to the copyright information and the custom information; and the question processing module is used for receiving the query question. The technical problem that financial knowledge processing efficiency of a related digital reading system is low is solved.

The invention relates to the technical field of cross-view image positioning, in particular to a cross-view image geographic positioning method and system for an unmanned aerial vehicle and a satellite image, and the method comprises the steps: employing a Dinov2 large model after Conv-LoRA fine adjustment as a feature encoder, and extracting an unmanned aerial vehicle image feature vector and a satellite image feature vector; designing a spatial relation perception feature aggregator MSRA based on a Mama module for aggregating image features, and embedding spatial configuration features into a global descriptor; an InfoNCE loss function is adopted to train the model; and after feature vectors of a to-be-queried image and each image in the reference image library are extracted by using the trained model, a similarity score is calculated by using cosine similarity, and the unmanned aerial vehicle-satellite image pair with a high matching degree is selected to realize geographic positioning of the to-be-queried image. According to the method, Conv-LoRA is adopted to carry out fine tuning on a Dinov2 large model, so that the quantity of parameters needing to be trained is greatly reduced; a spatial configuration relationship between features is fully mined through a spatial relationship perception feature aggregator based on a Mama module.

Described are systems and methods for providing a content generation service that may be configured to generate content, such as digital images and the like, based on both a textual input and supplemental information. The content generation service may include one or more trained machine learning models that may be configured to generate an image based on a textual input and one or more supplemental information input(s). The supplemental information may include, for example, user information, content information, etc. and can provide context, preferences, or any other additional information beyond the textual input that may be used to generate the content. Further, the content generation service and / or the user may be able to assign weights to each of the textual input and the supplemental information input in connection with the generation of the content.

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 technical field of image retrieval, in particular to an image retrieval method based on big data mining, which comprises the following steps: collecting images and metadata, screening metadata fields, determining a candidate image set and obtaining a metadata screening result, according to the method, in the image retrieval process, image metadata screening, geographic information clustering, visual feature fusion, index construction and matching strategy optimization are combined, and the retrieval accuracy and response efficiency are improved. In the metadata screening link, data is screened by using information such as time, places and equipment types, so that interference of irrelevant data is reduced, and a candidate image set is more targeted. Geographic information and timestamps are combined for classification, and clustering analysis is performed by utilizing a geographic information system, so that spatial distribution information plays a greater role in a retrieval process, and a more accurate result can be obtained for query of a specific region.

The invention discloses a multi-modal agricultural question and answer method and system for generating RAG based on retrieval enhancement, and the method comprises the steps: collecting and constructing crop disease image data containing a farmland complex background and corresponding text description, and forming an agricultural image-text knowledge database; based on the agricultural image-text knowledge database, multi-modal features of an input image and a query text are extracted, image-text joint similarity retrieval is carried out, and knowledge image-text candidates are obtained; inputting the knowledge image-text candidates into an image-text rearrangement module for rearrangement; taking the reordered image-text knowledge as condition input, accessing a large language model, and generating diagnosis description and prevention and treatment suggestions for the current crop diseases; and outputting a multi-modal question and answer result according to the image and question input by the user. According to the method, the agricultural image-text database is constructed, and multi-modal retrieval, rearrangement and large language model generation technologies are combined, so that accurate and efficient crop disease multi-modal question answering is realized, and the reliability of diagnosis and prevention suggestions is improved.

The invention discloses a customs intelligent specimen cabinet monitoring system, relates to the technical field of specimen cabinet monitoring, and aims to solve the problems that the monitoring strength of an existing specimen cabinet is reduced, and abnormality checking cannot be carried out in time. According to the invention, the most suitable transmission channel is flexibly selected through different transmission requirements and environmental conditions, the adaptability and flexibility of the system are enhanced, the feature extraction of the environmental monitoring data covers the monitoring parameters of each sensor, and the comprehensive monitoring of environmental factors is ensured. The monitoring of real-time video data provides intuitive visual information, so that workers can intuitively know the real-time condition in the specimen cabinet, manual intervention is reduced through automatic detection, recognition and alarming, the automation and intelligence level of the monitoring system is improved, and the monitoring efficiency is improved through automatic data classification, visual display and anomaly analysis. And the intelligent level of the customs intelligent specimen cabinet monitoring system is improved.