760results about "Metadata multimedia retrieval" patented technology

The invention discloses a cross-modal information fusion and retrieval method and system driven by semantic comprehension, and the method comprises the steps: obtaining text, image and audio original data, and extracting an initial feature set of each modal through a deep neural network; dynamically distributing each modal weight coefficient based on an attention mechanism, and performing weighted fusion on the initial feature set to obtain cross-modal fusion feature representation; through a cross-modal semantic association analysis model, high-dimensional semantic association features are extracted from the fusion feature representation, and semantic enhancement feature vectors are generated; constructing a cross-modal semantic graph network based on the vector, complementing missing modal features, and generating an optimized multi-modal feature set; and inputting the optimized feature set and the query sample into a contrast learning model, calculating a semantic similarity score, and generating a cross-modal retrieval result sorting list according to the score.

The invention discloses a collaborative personalized learning system and method based on a large model, and relates to the technical field of collaborative learning, and the system collects and fuses multi-modal data, generates a student state vector, plans a personalized learning path based on the state and a knowledge graph, and generates multi-modal explanation content according with the student style. The method comprises the following steps: extracting a student reasoning path, identifying thinking deviation, analyzing task performance, generating a path and content adjustment suggestion, identifying a motivation state, triggering an intervention strategy, receiving a teacher strategy, and realizing path and style co-construction. The multi-modal content of the matched style is generated, adaptive intervention is achieved in combination with inference analysis and emotion adjustment, and the learning efficiency and the teaching response intelligent level are improved.

The invention provides a wisdom education method and system based on student behavior sequence recommendation, and relates to the technical field of wisdom education. According to the method and the system, through an AI autonomous identification knowledge graph construction and innovative knowledge fusion method, a course knowledge system is structurally and hierarchically displayed, and a knowledge framework is clearly presented, so that knowledge graph framework construction of all-platform courses is facilitated, pre-repaired courses and subsequent courses are efficiently associated, and the construction efficiency is improved. And a clear and complete course learning path is provided for students. By adopting a recommendation algorithm based on a student behavior sequence, combining SASRec and introducing a self-attention mechanism of a sparse attention module, behavior data of students can be deeply analyzed, a learning path is adjusted according to real-time learning data, and personalized learning suggestions are provided for the students. According to the method and the system, multi-dimensional functions such as knowledge graph construction, a student behavior sequence recommendation algorithm and intelligent learning path optimization are integrated, and the method and the system have wide coverage and strong comprehensiveness.

The invention discloses a visual scene method, system and equipment based on digital twinning and a medium, and relates to the technical field of digital twinning and visual modeling, and the method comprises the steps: collecting multi-format source data, carrying out the field standard mapping, and carrying out the consistency verification of the multi-format source data after the field standard mapping; writing the multi-format source data after consistency verification into a to-be-fused buffer area, selecting model component data in the to-be-fused buffer area to execute coordinate reference conversion, and performing association binding on the converted model component data and the structured graphic and text information by constructing an identification field; and synchronously loading the associated and bound model component data and the structured graphic and text information to a predefined template container, generating a scene configuration file according to a container structure, and calling a release engine to register to a multi-terminal rendering service channel. According to the method, accurate alignment of multi-format model components in a unified space coordinate system is realized by constructing an affine transformation coordinate reference conversion scheme.

The invention discloses a multi-modal enhanced retrieval method and system, and the method comprises the steps: obtaining heterogeneous data, and executing data processing to obtain a meta-information set; encoding the meta-information set into a high-dimensional meta-information vector, and constructing a vector index; receiving a query statement of a user, calculating the similarity between the query statement and the meta-information vector, and obtaining a meta-information query result; and calling a large language model based on the obtained meta-information to generate a summary result, and merging and outputting the summary result and heterogeneous data corresponding to the meta-information. According to the method, multi-modal data is processed into unified semantic vector representation, efficient vectorization indexing and semantic generation and expansion capacity of a large-scale language model are combined, high-value information can be rapidly extracted from multi-source heterogeneous data, and real-time, cross-modal and high-precision retrieval requirements are supported. A reordering and traceability mechanism introduced in the scheme further enhances the correlation and reliability of the information, and provides comprehensive and accurate knowledge support for the generation of query results.

The invention belongs to but is not limited to the technical field of artificial intelligence, and particularly relates to a controllable content large model generation method and system based on multi-modal thinking chain reasoning, and the method comprises the steps: S1, improving the capability of multi-agent planning task dependence and execution sequence through employing a thinking chain method, enabling a complex task to be subdivided into a plurality of subtasks through the method, and enabling the subtasks to be subdivided into a plurality of subtasks; a resource transmission process between the sub-tasks is precisely planned and is executed in a chain mode; s2, complex tools are combined into sub-agents with different modal characteristics, so that the number of selectable sub-tasks during task planning is reduced, and the planning capability is improved; and S3, storing scene information of a previous dialogue by means of an external database, and when a new request is encountered, improving correlation and accuracy of multi-agent response by quoting historical interaction information and historical multi-modal resources which have context similarity with the current request, and realizing a more intelligent reaction mechanism.

The invention discloses a digital collection full-life-cycle traceability tracking system based on a block chain, and the system comprises a digital identification module, a verification module, a packaging transfer module, a verification module, an integration module, and a system construction module, and achieves the full-life-cycle traceability tracking of a digital collection through the technologies of multi-dimensional feature extraction, watermark embedding, distributed storage, and zero-knowledge proof. And tracking and verification of the whole process from creation to transaction of the digital collections are realized. According to the system, the transaction authorization model is encapsulated by adopting the intelligent contract, so that safe transfer of ownership is ensured; a tamper-proof verification mechanism is constructed through multi-level integrity verification; different platform data are integrated by using a cross-chain technology to form a comprehensive traceability graph, so that key problems of authenticity verification, copyright protection, value evaluation and the like faced by a digital collection market are effectively solved. In addition, the system also establishes a scientific authenticity evaluation and value dynamic evaluation system, provides an objective basis for market pricing, and significantly improves the credibility and safety of the digital collections.

The invention discloses a multi-modal information identifier generation method and system based on semantic hash. The method comprises the following steps: carrying out data preprocessing and multi-modal feature extraction on multi-modal data to obtain a unified representation containing rich semantic information; mapping the features to a shared semantic space by adopting an independent alignment projection network of each mode, and introducing cross-mode contrast learning and label supervision to realize semantic alignment among different modes; compressing the high-dimensional features after multi-modal data alignment to generate a Hash code with a fixed length; generating a unified semantic hash code containing semantic information of at least two modals; searching the number of times that the Hash code appears in a database through the unified semantic Hash code, generating a redundant code, and splicing the redundant code with the unified semantic Hash code to form an identifier of the sample; on the basis of the unified semantic hash codes, the hash codes related to the to-be-queried category in the test set are queried in the training set, and unified identification and efficient retrieval of different modes are achieved.

The invention discloses an active real-time interaction system based on LLM. The active real-time interaction system comprises a self-cognition module, a real-time perception module, an active planning module, a situation decision module, a dynamic execution module, an emotion engine module, an adaptive interaction module and a safety management and control module. The system adopts a multi-dimensional vector to represent role attributes, and constructs a hierarchical memory storage structure to record interaction experience; generating an environment state vector through a multi-modal data processing technology; executing the target task decomposition algorithm to generate a multi-path execution plan; making a real-time decision based on the multi-dimensional decision factor; mapping the abstract decision into an instruction sequence and monitoring an execution process; simulating a system emotional state and influencing decision expression; dynamically adjusting an interaction strategy according to the user characteristics; evaluating decision rationality and starting a corresponding intervention mechanism. All the modules form a closed-loop workflow through a standardized data interface, the problems that a traditional AI system is passive in response, lacks self-cognition, is limited in environmental perception, is single in planning capability, lacks flexibility in decision making and the like are solved, and active service, self-evolution and safety controllability of the system are achieved.

The invention provides an integrated sharing method and system for digital media content resources, and is applied to the technical field of data processing. The method comprises the following steps: preprocessing digital media content resources, cloud platform basic data and user demand data to generate a standardized media content sequence, cloud platform features, user demand features and resource classification features; processing the resource association relationship data to generate resource association features; processing a strategy in the shared strategy updating mechanism information based on the resource value evaluation system, and generating updated shared strategy data; processing the standardized media content sequence, the cloud platform feature and the resource association feature based on a target integrated sharing model to generate a target integrated sharing feature; and processing the target integration sharing feature based on the target integration sharing model and the updated sharing strategy data to generate a digital media content resource integration sharing result.

The invention provides an interaction method and device, a storage medium and a program product, and relates to the technical field of computers. The interaction method comprises the following steps: generating a first content in response to an instruction of generating the first content input by a user in a dialogue area; an editing area matched with the body of the first content is displayed, the first content is displayed in the editing area, and the editing area and the dialogue area are located on the same display interface; in response to an editing operation triggered by the user in the dialogue area or the editing area, determining an editing instruction for the first content based on the first content and the style of the first content; and based on the editing instruction, editing the first content in the editing area.

The invention discloses a personalized teaching content generation method and system based on a digital portrait, and the method comprises the steps: collecting the multi-dimensional learning data of a student in real time, analyzing the data keyword of the student, constructing the portrait of the student, generating a teaching target based on the portrait of the student, and enabling the teaching target to comprise a teaching theme, exercises related to the theme, and knowledge points related to the theme. Converting the teaching target into an executable instruction of a large language model through a structured prompt engineering technology; the large language model generates initial teaching content according to the teaching instruction; verifying and optimizing the generated initial teaching content to ensure that the generated teaching content conforms to a teaching target and can adapt to the current cognitive level and learning requirements of students; and outputting the verified and optimized teaching content to the students and automatically adapting to the content presentation form according to the learning style preference of the students.

An electronic device associated with a media-providing service stores, in a vector space, a plurality of respective vector representations for respective media content items. The electronic device receives a user input, including a text string. The electronic device generates, using a neural network, a structured query based on the text string. The electronic device determines, based on the structured query, whether to generate a vector representation of a portion of the text string. When the electronic device determines to generate the vector representation of the portion of the text string, it generates the vector representation of the portion of the text string, wherein the vector representation is embedded in the vector space, and identifies a set of media items using the vector representation of the portion of the text string. And the electronic device provides one or more select media items from the set of media items to a user.

The invention discloses a cross-modal image-text retrieval method based on pulse fusion, and belongs to the technical field of image-text retrieval. The method comprises the following steps: inputting a target image set into a target detection network to obtain an image floating point code, and inputting a target text set into a word segmentation device to obtain a word floating point code; inputting the image floating point code and the word floating point code into a pulse encoder to obtain a first image pulse code and a first text pulse code; inputting the first image pulse code and the first text pulse code into a pulse cross attention fusion module to obtain a second image pulse code and a second text pulse code; respectively carrying out weighted accumulation and average pooling on the second image pulse code and the second text pulse code to obtain an image floating point feature vector set and a text floating point feature vector set, and calculating cosine similarity to obtain an image-text alignment result; and the text retrieval result of each image in the target image set is obtained based on the image-text alignment result, so that the accuracy and efficiency of image-text retrieval are improved.

The invention discloses a conference content intelligent generation processing method and system based on a multi-modal large model, and the method comprises the steps: collecting the original data of a conference, and completing the standardization preprocessing; inputting a multi-modal large model, extracting multi-modal features and carrying out semantic alignment; executing cross-modal hash coding, generating binary codes and establishing an index database; performing hash retrieval on the related fragments, and constructing a conference content directed graph; based on a conference content directed graph structure, searching an optimized path by adopting a Monte Carlo tree; and generating structured conference content, and outputting a summary, an abstract and an action item. According to the method, efficient extraction, accurate retrieval and structured intelligent generation of the conference content are realized by fusing a multi-modal large model, cross-modal Hash coding and Monte Carlo tree search.

The invention provides an interaction method and device, a storage medium and a program product, and relates to the field of terminals. The interaction method comprises the following steps: displaying a session interface between a first user and a virtual object; receiving an input message on the session interface; and displaying image-text content related to the input message in a message body on the session interface, the image-text content comprising at least one image group and at least one text block, and the image-text content being a part of content in a media work published by a second user corresponding to the virtual object.

The invention discloses a model training method, electronic equipment and a computer readable storage medium, and relates to the technical field of data processing and large models. The method comprises the steps of obtaining a training data set, wherein the training data set comprises image annotation data of various types of concerned objects and description text annotation data associated with the image annotation data; training the initial multi-modal retrieval model by adopting the training data set to generate a learnable prompt; carrying out image coding training on the initial multi-modal retrieval model by adopting the training data set and the learnable prompt to generate an intermediate multi-modal retrieval model; the training data set is adopted to conduct text and image alignment training on the middle multi-modal retrieval model, a target multi-modal retrieval model is generated, and the target multi-modal retrieval model is used for conducting multi-modal retrieval on the target retrieval request to obtain a target retrieval result. According to the method and the device, the technical problems that a multi-modal retrieval model in related technologies cannot adapt to variability and large-scale data requirements, and the model performance is relatively poor are solved.

The invention provides a medical visual question answering method based on multi-scale feature fusion and question enhancement. The method comprises the steps of obtaining a to-be-recognized medical image and a to-be-answered text; inputting the to-be-recognized medical image and the to-be-answered text into a pre-trained medical question and answer model to obtain a detection result about the to-be-answered text; the detection result is a text result; wherein the pre-trained medical question and answer model is provided with a visual extraction model, a text extraction model and a classification model; the visual extraction model is a visual model based on a multi-scale visual feature extraction method, a multi-scale visual feature enhancement method and a multi-scale visual feature fusion method; and a multi-head cross attention feature interaction mechanism is arranged between the visual extraction model and the text extraction model. By combining the visual extraction model, the text extraction model and the classification model in the pre-trained medical question and answer model, the understanding ability of the medical image is improved, and finally the recognition precision and the answer quality of the medical visual question and answer task are integrally improved.

The invention provides an intelligent decision-making method and system for emergency allocation of first-aid materials. According to the method, by integrating a graph neural network, deep learning, reinforcement learning, Bayesian optimization and a genetic algorithm, the demand information of multiple emergency response points is efficiently processed, and by receiving the demand information including the number, the type and the severity of injuries and patients, the demand urgency degree is dynamically calculated through an intelligent evaluation system, and a preliminary deployment plan is generated. And a traffic prediction model is combined with real-time and historical data to pre-estimate the road traffic capacity, various logistics distribution strategies are simulated to optimize the preliminary scheme, and an optimal path plan is determined. And then, the material distribution effect is verified in different scenes by means of an AI simulation drilling system, the configuration scheme is adjusted, and finally a comprehensive allocation instruction is formed to guide actual operation. According to the technical scheme of the invention, intelligent decision support for the first-aid materials can be realized, and the efficiency and accuracy of first-aid material allocation are remarkably improved.

The invention discloses a data auditing method and device, electronic equipment and a nonvolatile storage medium. The method comprises the following steps: acquiring cross-modal data to be audited; a verification rule set corresponding to the multi-modal knowledge base is determined, first verification processing is carried out on the cross-modal data based on the verification rule set, a first verification result is obtained, and the first verification processing is used for verifying whether the cross-modal data has exceptions which do not conform to rules in the verification rule set or not; the large auditing model is adopted, second verification processing is conducted on the cross-modal data, a second verification result is obtained, and the second verification processing is used for detecting whether logic contradictions exist in the cross-modal data or not based on the semantic reasoning ability of the large model; and determining an exception report corresponding to the cross-modal data according to the first verification result and the second verification result. According to the method and the apparatus, the technical problem of poor content quality inspection effect of cross-modal data in related technologies is solved.

The invention discloses a key information extraction method and system based on a multi-modal large model, and belongs to the technical field of information processing.The key information extraction method and system based on the multi-modal large model comprise the following specific steps that firstly, input heterogeneous modal data are subjected to standardized preprocessing, and the preprocessed data are subjected to data preprocessing; comprising text vectorization, image feature coding and speech spectrum analysis; 2, establishing an inter-modal incidence matrix through a cross-modal attention mechanism, and dynamically adjusting the feature weight of each modal; and 3, sequentially executing context semantic understanding, entity relationship modeling and core information positioning by adopting a hierarchical feature extraction architecture. According to the method, intelligent selection of inter-modal features is realized through dynamic weight distribution, the key information recall rate is greatly improved in a noise interference scene, and after a feature distillation loss function is introduced and redundant features are eliminated, the error propagation rate is effectively reduced in a noisy voice and low-resolution image coexistence scene.

Embodiment of the disclosure relates to a method, apparatus, device, and storage medium for media item generation. The method provided herein includes: displaying a item search page comprising a search control, the item search page being configured to provide an item search result corresponding to received input information; receiving a search term inputted in the search control; providing, in the item search page, a generation entry in association with the search control; and based on a selection of the generation entry, displaying, in the item search page, a set of media items generated based on the search term. In this way, the embodiments of the disclosure may provide users with more diversified media items to meet user specific needs for media items.

The invention provides an ambiguity detection method and device for multi-modal and multi-language information, and the method comprises the steps: employing a ViT image encoder and an XLM-R text encoder to extract image and multi-language text features, achieving the depth interaction between modals through a cross attention mechanism, generating multi-modal fusion features, and carrying out the detection of the multi-modal fusion features. Comprehensively capturing fine-grained association between the image and the text; an image and a text encoder of a pre-training model are frozen by adopting a LoRA fine tuning technology, and only low-rank parameter optimization is performed on a classifier, so that the computing resource demand is remarkably reduced; a dynamic memory mechanism is introduced, the recognition capability of the model on complex discrimination signals is enhanced by retrieving historical sample information and dynamically fusing current features, and newly input data is accurately predicted. According to the method, the language limitation of the existing method is broken through, the accurate detection of the discrimination signal can be realized under the multi-language and multi-culture background, and the blank of the multi-modal discrimination detection in the multi-language environment is filled.

Embodiments of the present disclosure provide a method, apparatus, electronic device, and storage medium for collecting media content. The method includes: in response to a collection operation on media content, adding the media content to the media content collection list of the current user, and displaying a favorites list of the current user, where the favorites list is used to display first identifiers of at least some of the current user's favorites; in response to a trigger operation on the first identifier, adding the media content to the favorite corresponding to the first identifier on which the trigger operation acts. By adopting the above technical solutions, the embodiments of the present disclosure can enrich the collection methods of media content.

A method and system for automated routing of pictures taken on mobile electronic devices to a digital picture frame including a camera, microphone, and speaker integrated with the frame, and a network connection module allowing the frame for direct contact and upload of photos from electronic devices or from photo collections of community members. Clustering photos by content is used to improve display and to respond to photo viewer desires. Trends or patterns can be detected from the photo collections and that information used for various purposes beyond photo display. The frame includes a conversational intelligence that provides a verbal communication with a viewer, such as for determining an identity or preferences of the frame viewer, determining photos to display for the viewer, discussing displayed photos with the viewer, or telling stories or life histories to the viewer based upon photo content.

Systems and processes are disclosed for operating a digital assistant in a media environment. In an exemplary embodiment, a user can interact with a digital assistant of a media device while content is displayed by the media device. In one approach, a plurality of exemplary natural language requests can be displayed in response to detecting a user input of a first input type. The plurality of exemplary natural language requests can be contextually-related to the displayed content. In another approach, a user request can be received in response to detecting a user input of a second input type. A task that at least partially satisfies the user request can be performed. The performed task can depend on the nature of the user request and the content being displayed by the media device. In particular, the user request can be satisfied while reducing disruption to user consumption of media content.