132 results about "Object description" patented technology

The invention relates to the technical field of audio and video control, and discloses an audio and video player control method based on a voice instruction. The method comprises the steps that an original voice instruction stream of a user is collected, the instruction stream comprises a time domain audio signal sequence, an environment noise spectrum and user pronunciation characteristic parameters, and voice information can be comprehensively captured; multi-modal instruction analysis processing is carried out on the original voice instruction stream, a structured control instruction set containing acoustic control intention identification, semantic operation object description and context correlation parameters is generated, and the analysis precision is improved; then executing player state adaptation based on the set, generating a dynamic control response sequence containing an equipment state adjustment command, a media content positioning parameter and an interface interaction logic identifier, driving a player to execute a multi-dimensional control operation and generating real-time play control effect feedback data; and finally, multi-modal analysis parameters are optimized according to feedback data, a self-adaptive instruction analysis strategy is generated, and the control experience of a user on the audio and video player is optimized.

The invention discloses an intelligent SQL (Structured Query Language) generation method and system based on multistage intention recognition. The method comprises the following steps: receiving a natural language query of a user; vector matching: carrying out vector matching based on a knowledge base to obtain Top-K candidate query objects, the knowledge base containing multilayer structure description information; a large language model intention understanding step: determining a final query object in combination with the candidate query object description; loading a branch workflow, analyzing a field and a table structure, and dynamically configuring a database table and field information; sQL generation: generating SQL statements conforming to grammatical rules, including automatic field addition, time condition conversion and field priority matching operation; and executing the SQL statement and returning a result, and if the result fails, recording a log to prompt correction. By combining a natural language processing technology with a database knowledge base, an efficient, accurate and user-friendly database query solution is provided.

Embodiments of the present description provide an image object detection method. The method comprises: on the basis of an image to be retrieved, an object description text, and an object retrieval condition, determining, by means of an object detection model, a target position of an object to be retrieved in said image, wherein the object description text is used for describing said object, and the object retrieval condition comprises at least one of a mask image, a pose, and a texture corresponding to said object.

The invention discloses a mandatory access control method and device based on a process function context, and the method comprises the steps: collecting a security context associated with a system call initiated by a target process, so as to generate a standardized object description; mapping the object description into a target function classification identifier, so as to obtain a process function context view of the target process according to the target function classification identifier; constructing a target decision key for access decision based on the current policy era, the qualifier, the function classification identifier, the view identifier of the process function context view and the isolation domain abstract; and querying the multi-level cache according to the target decision key to determine a matched target access decision. Therefore, context-sensitive judgment and cross-component consistency taking the functional context as the center are realized.

A method of generating a virtual avatar based on a large model, an agent, an electronic device and a storage medium, which relate to a field of artificial intelligence technology, and to fields of computer vision technology, deep learning technology, large model technology, etc., and may be applied to scenarios such as AIGC, digital character, intelligent e-commerce, etc. The method includes: processing a target image including a target object by using a large model to obtain object description information, the target object having texture information; processing the target image and a to-be-processed image representing an object morphology of a three-dimensional object by using a texture-generative large model to obtain a target three-dimensional object with target texture information, the three-dimensional object being determined based on the object description information, the target texture information being matched with the texture information; and generating the virtual avatar based on the target three-dimensional object.

Object description information that is transmitted by a biometric recognition apparatus is received, the object description information includes a biometric feature of a target object and location information of the target object. Identity information of the target object is obtained according to the biometric feature. A service information set in association with the identity information is obtained. From the service information set, one or more pieces of candidate service information are selected. The one or more pieces of candidate service information are transmitted to a terminal device associated with the identity information. At least a first piece of target service information returned by the terminal device is received. At least a first service corresponding to the first piece of target service information is processed. Apparatus and non-transitory computer-readable storage medium counterpart embodiments are also contemplated.

Disclosed are apparatuses, systems, and techniques for automated iterative content detection and annotation of objects in media items. The techniques include performing a plurality of iterations to identify objects represented in a media item and referenced in a plurality of object descriptions of a prompt. An individual iteration includes identifying, using a content detection model, a subset of the objects represented in the media item and referenced in the plurality of object descriptions, or no objects represented in the media item and referenced in the plurality of object descriptions. Using the content detection model includes applying the content detection model to the media item and to the prompt or to an iteration prompt obtained from the prompt by eliminating descriptions of the subsets of the objects identified during previous iterations. The techniques further include generating, using the identified objects, a characterization of the media item.

A method, apparatus, non-transitory computer readable medium, and system for image processing include obtaining an input prompt including an image quality level and a description of an object, generating an image embedding based on the input prompt, where the image embedding represents the object and the image quality level in a vector space, and generating a synthetic image based on the image embedding, where the synthetic image depicts the object and has the image quality level.

The invention relates to an information pushing method and device, computer equipment, a storage medium and a computer program product. The method comprises the steps of obtaining candidate product features and object description features; the candidate product features and the object description features are combined to obtain shared features, at least two autocorrelation degrees of the shared features are calculated, the shared features are transformed based on the at least two autocorrelation degrees, and autocorrelation features corresponding to the at least two autocorrelation degrees are obtained; taking the candidate product features and the object description features as input features, and fusing the input features with the self-correlation features corresponding to the at least two self-correlation degrees in sequence to obtain target fusion features; and calculating an interaction degree of the to-be-pushed object to the product information based on the target fusion feature, and pushing the product information of the candidate product to a terminal of the to-be-pushed object when the interaction degree meets a preset pushing condition. By adopting the method, the information pushing accuracy can be improved.

The invention provides an industrial robot self-adaptive grabbing method based on multi-modal sensing, which comprises the following steps of: 1, acquiring multi-modal data of an object and an environment through a multi-modal sensing module; the multi-mode sensing module comprises at least two sensing modules of a visual sensing unit, a touch sensing unit, a force sensing unit and an auditory sensing unit; 2, preprocessing and feature extraction are carried out on the multi-modal data, feature information of different modals is fused through a multi-modal fusion algorithm, and comprehensive object description information is generated; 3, generating an optimal grabbing strategy based on the comprehensive object description information; wherein the grabbing strategy comprises the position and posture of a grabbing point, a grabbing path and grabbing force; and fourthly, the industrial robot executes the optimal grabbing strategy, the first step to the third step are repeated, and the grabbing strategy is adjusted in a self-adaptive mode till grabbing is completed. Stable and efficient grabbing can be achieved.

The embodiment of the invention provides a task processing method, a dialogue task processing method, a task planning model training method, an information processing method based on a task planning model and a model training platform. The task processing method comprises the steps of obtaining task data of a target task; the task data and the object description information of the candidate processing objects are input into a task planning model, multiple processing objects corresponding to the target task are determined, and the task planning model is used for planning the multiple processing objects of the target task; processing the task data by using the plurality of processing objects to obtain processing results respectively output by the plurality of processing objects; and determining a task processing result of the target task according to the processing results output by the plurality of processing objects. The task planning model is utilized to determine the plurality of processing objects for processing the target task, so that seamless integration between the model and the processing objects with different processing capabilities is realized, the task processing capability is expanded, and the task processing is more efficient and comprehensive.

The invention relates to the technical field of visual reasoning, and provides a safety monitoring multi-modal model reasoning method and device. According to a user problem and a user image in a security monitoring scene, an object position in a visual scene is converted into text information, and the text information, the user problem and the corresponding user image serve as input information; obtaining visual features according to the input information through a cross-modal semantic converter; constructing a visual scene of the user image into hierarchical description comprising scene description and object description; modeling the context of the user image according to the user question, and generating a text prompt of a visual scene; reasoning is carried out through a large language model according to the visual features, the hierarchical description and the text prompt, reasoning output is obtained, and the problems that in a multi-modal scene, the complex scene perception ability is insufficient, and the large language model reasoning ability is insufficient in utilization in the prior art are solved.

The invention relates to an interaction method and device, electronic equipment and a storage medium, and the method comprises the steps: receiving a first reference image, and displaying the identification information of the received first reference image in a guide language input region; the number of the first reference images is multiple; receiving a text cue word, and displaying the text cue word in the guide word input area; the text cue word comprises an object description word group, and the object description word group corresponds to the first reference image; the object description phrases are used for describing objects in the first reference image corresponding to the object description phrases; the identification information of the first reference image is interspersed in the text cue word; the number of characters between the position of the object description phrase in the text cue word and the position of the identification information of the first reference image corresponding to the object description phrase in the text cue word is less than a preset number of characters; a first video is generated based on the content in the guide language input area. The demand that a user hopes to generate a video based on a plurality of first reference images can be met.

The invention discloses a distributed operation-oriented multi-agent collaborative recommendation method and system, and relates to the technical field of intelligent recommendation, and the method comprises the steps: configuring a private domain operation agent in each independent private domain platform, deploying a big language model-based construction demand analysis agent at a user side to receive a natural language demand description input by a user, and constructing a multi-agent collaborative recommendation system; generating recommendation task instructions of different private domain platforms; the method comprises the following steps: receiving user preference description and recommendation object description, sending the description to a corresponding private domain operation agent, calling a recommendation model to generate a recommendation result list based on the received user preference description and recommendation object description, collecting recommendation result lists returned by all private domain operation agents by a demand analysis agent, and integrating and classifying the recommendation result lists to generate a comprehensive recommendation list. According to the method, an efficient, safe and extensible distributed recommendation architecture is constructed by introducing a user demand analysis agent, a private domain operation agent and a federal learning mechanism.

The invention discloses a cross-platform page code generation method based on a large model, and relates to the technical field of front-end development, and the method comprises the following steps: cooperatively analyzing a UI design drawing through a plurality of special large models, and carrying out real-time cross validation and dynamic compensation on an analysis result by adopting a multi-model mutual verification mechanism; based on a predefined standard specification, dynamic adjustment is carried out through a platform adaptation rule base; generating a page object description tree embedded with the input / output processing function; cross-platform page codes are generated through a code generation engine, and sandbox testing and automatic correction are executed; outputting a target platform code by utilizing a unified compiling engine; an adaptive optimization loop is constructed based on test feedback. According to the method, through integration of multi-model collaborative analysis, platform rule dynamic adaptation, sandbox verification and closed-loop optimization, the problems of large analysis deviation, poor cross-platform compatibility, uncontrollable code quality and the like are solved, and end-to-end high-quality automatic generation from a design drawing to multi-platform codes is realized.

Methods, systems, and apparatus, including computer-readable storage media for generating model-generated digital content from prompts built using a combination of a base object description and targeting parameters for an intended audience. The digital content, once generated, can be served to a target audience indicated by the targeting parameters. A system implementing the methods described herein can generate content for various different audiences, indicated by different combinations of targeting parameters available on a campaign management platform serving the content. When the content is no longer being served the system can cause the digital content to be deleted or otherwise discarded. Instead of storing the content, the system can save the prompt and re-process the prompt through the model to re-generate the content. The system can further index prompts for later querying, so that the system can avoid generating new prompts over using stored prompts for content generation.

The application discloses a kind of intelligent SQL generation method and system based on multistage intention recognition, comprising: receiving user natural language query;Vector matching step: vector matching is carried out based on knowledge base, obtain Top-K candidate query object, and knowledge base contains multi-layer structure description information;Large language model intention understanding step: determine final query object in combination with candidate query object description;Load branch workflow, parse field and table structure, dynamically configure database table and field information;SQL generation step: generate SQL sentence in accordance with syntax rule, including automatically adding field, time condition conversion, field priority matching operation;Execute SQL sentence and return result, and record log if it fails to prompt correction.The application provides an efficient, accurate and user-friendly database query solution by natural language processing technology combined with database knowledge base.

The invention discloses a printed text content display method and device based on cross-platform consistency, and the method comprises the steps: obtaining a to-be-printed character string and a defined text object description protocol, and obtaining a text attribute structure according to the defined text object description protocol; obtaining the resolution ratio of the ink-jet printer nozzle, and enabling the resolution ratio of the PC upper computer to be consistent with the resolution ratio of the ink-jet printer nozzle according to the resolution ratio of the ink-jet printer nozzle; adopting a text shaping engine to obtain corresponding font list information according to the text attribute structure; adopting a font rasterization engine to obtain corresponding font contour information according to the font list information and the text attribute structure; obtaining the pixel width and height of the whole line of text according to the font list information and the font contour information; according to the obtained font list information, font contour information and resolution parameters, drawing to a display screen by adopting a Skia rendering engine; therefore, cross-platform measurement and drawing consistency of the text content of the ink-jet printer is realized.

Methods, systems, and apparatus, including computer-readable storage media for generating model-generated digital content from prompts built using a combination of a base object description and targeting parameters for an intended audience. The digital content, once generated, can be served to a target audience indicated by the targeting parameters. A system implementing the methods described herein can generate content for various different audiences, indicated by different combinations of targeting parameters available on a campaign management platform serving the content. When the content is no longer being served the system can cause the digital content to be deleted or otherwise discarded. Instead of storing the content, the system can save the prompt and re-process the prompt through the model to re-generate the content. The system can further index prompts for later querying, so that the system can avoid generating new prompts over using stored prompts for content generation.

The embodiment of the invention provides a cochlea object description method and system, equipment, a storage medium and a program product, and the method comprises the steps: extracting a region-of-interest image of a cochlea object from a medical image comprising the cochlea object, carrying out the segmentation of the cochlea object, obtaining a segmentation result of the cochlea object, and carrying out the segmentation of the cochlea object according to a plurality of preset radiomics feature types, respectively extracting corresponding radiomics features from the region-of-interest image, obtaining voxels of the cochlea object from the segmentation result, calculating respectively corresponding morphological parameter values based on voxel coordinates of the voxels of the cochlea object according to a plurality of preset morphological parameters, and determining the cochlea object according to the plurality of preset morphological parameters and the plurality of morphological parameter values based on the plurality of radiomics features and the plurality of morphological parameter values. A description data set of the cochlea object is constructed to describe the cochlea object using the plurality of radiomics features and the plurality of morphological parameter values. According to the scheme provided by the embodiment of the invention, the comprehensiveness and accuracy of description aiming at the cochlea are improved.

The application relates to a symptom information determination method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring object description information to be processed; using a target symptom recognition network to obtain corresponding target symptom information by taking the object description information as input, wherein the target symptom recognition network is obtained by machine learning training of multiple sample pairs and adjusting parameters of a preset network during the training process, each sample pair indicates a pair of object description information samples and symptom information samples, the training process comprises learning a first type correlation degree and a second type correlation degree, the first type correlation degree represents a correlation degree between two heterogeneous samples in the sample pair, and the second type correlation degree represents a correlation degree between two heterogeneous samples from different sample pairs. The application improves the accuracy and efficiency of symptom recognition. The embodiments of the application can be applied to various scenes such as cloud technology, artificial intelligence, intelligent transportation and auxiliary driving.

A method of a pixel based with object based decision making for driving, the method includes receiving, at a first machine learning process of an artificial intelligence agent, a sensed information unit; receiving, at a second machine learning process of the artificial intelligence agent, object descriptive information regarding an object captured in the sensed information unit; generating, by the first machine learning process, a pixel-based path planning output related to a suggested pixel-based path segment of a vehicle; generating, by the second machine learning process, an object-based path planning output related to a suggested object-based path segment of the vehicle; and generating, by at least in part processing the pixel-based path planning output in correspondence with the object-based path planning output, a driving related output with respect to the vehicle.

Embodiments of the present application provide an image generation method, a reward model training method, an electronic device, a storage medium and a computer program product. The method comprises: obtaining object description text of a target object, original object image and scene information matched with a to-be-generated image carried by an image generation instruction; taking the scene information, the object description text and the original object image as input information, calling a pre-tuned visual language model to generate background image design description text adapted to the scene information, wherein when the scene information is a weak signal in the input information, the background image design description text can reflect scene preference characteristics matched with the scene information; calling a preset image generation model to generate an image of the target object matching the scene information under the constraint of the background image design description text, the image of the target object in the original object image and layout information. The image generated by the method is more matched with the scene preference corresponding to the scene information.

The application discloses a task execution method, device, equipment, computer storage medium and product. The method comprises the following steps: receiving a voice text of a user, processing the voice text into a semantic vector; based on the semantic vector, searching a corresponding object description vector in an environment vector database, and obtaining an object description text of the object description vector; based on the voice text and the object description text, generating an intent recognition prompt information, inputting the intent recognition prompt information into an intent recognition model, and obtaining an object information set recognized by the intent recognition model. The adaptability of the robot to the environment in the home service scene can be improved, the recognition accuracy of the user's intent can be improved, and the autonomy and flexibility of the robot task execution can be realized.

An artificial intelligence (AI) engine assists in the creation of product descriptions. For example, a system that includes the AI engine receives raw data including a description of a product and generates a search query based on the raw data to search the internet for possible product descriptions that satisfy the query. The system can parse the product descriptions into categories of content that map to sections of a template and creating the custom product description based on a selection and combination of the categories of content of the product descriptions matched to sections of the template. An AI engine is trained based on the selection and combination of the categories of content and ranks the content based on a frequency of being included in custom product descriptions such that the system can later recommend content in accordance with the ranking for creating a custom product description.

The application relates to the technical field of audio and video control, and discloses an audio and video player control method based on a voice instruction. The method comprises the following steps: collecting original voice instruction streams of a user, the instruction streams containing a time domain audio signal sequence, environmental noise spectrum and user pronunciation characteristic parameters, so that voice information can be comprehensively captured; performing multi-modal instruction analysis processing on the original voice instruction streams, generating a structured control instruction set containing an acoustic control intention identifier, a semantic operation object description and context association parameters, and improving analysis accuracy; then performing player state adaptation based on the set, generating a dynamic control response sequence containing device state adjustment commands, media content positioning parameters and interface interaction logic identifiers, driving the player to perform multi-dimensional control operations and generating real-time playing control effect feedback data; and finally optimizing multi-modal analysis parameters according to the feedback data, generating an adaptive instruction analysis strategy, and optimizing the control experience of the user on the audio and video player.

This disclosure proposes a video processing method, apparatus, computer device, and storage medium. The method includes: decomposing a video to obtain multiple video segments; determining multiple action event information corresponding to each of the multiple video segments; determining a target video segment from the multiple video segments based on the action event information; identifying object description information from the target video segment; and determining whether a target action event has occurred in the scene described by the video based on the object description information. Because the video is first decomposed, and a target video segment is determined based on the decomposed video segments, and target action events in the scene are identified and judged around the target video segment, the method achieves rapid identification and judgment of target action events around the target video segment, effectively reducing the computational resources consumed in target action event identification and judgment, and improving the identification and judgment effect of target action events.

A method for parsing a scene description document, including: determining an index value of a target haptic object description module; obtaining the target haptic object description module from a moving picture experts group (MPEG) haptic description module in the scene description document according to the index value of the target haptic object description module; and obtaining description information of a haptic media accessor according to the target haptic object description module, where the haptic media accessor is an accessor configured to access haptic rendering data of a haptic media file declared in an MPEG media description module.

The application relates to a conversation message processing method and device, computer equipment and a storage medium. The method comprises the following steps: in a first social application, a first conversation window of a conversation between a first conversation object and a second conversation object is displayed; in the first conversation window, a message card of a media conversation message generated by the second conversation object forwarding a target media object belonging to a media social platform into the conversation through a second social application is displayed; the message card displays thumbnail description information of the target media object; in the case that the media conversation message is archived after authorization of the second conversation object, in response to an archiving query instruction for the conversation, text format conversation archive information of the conversation is displayed; the conversation archive information comprises message archive information of the media conversation message, and the message archive information comprises object description information matched with the thumbnail description information and used for describing the target media object. The method can widen the archiving mode of the conversation message.

Some implementations relate to generating, based on processing captured vision data instances throughout an environment: regions of interest, and an estimated map location and region embedding(s) for each region of interest. Some implementations additionally or alternatively relate to determining, based on (1) a free form (FF) natural language (NL) instruction for a robot to perform a task and (2) generated region embedding(s) for identified regions of interest in an environment: object descriptors that describe objects that are relevant to performing the task and that are likely present in the environment. Some implementations additionally or alternatively relate to utilizing a subset of object descriptor(s), determined to be descriptive of object(s) that are relevant to performing the task of an FF NL instruction and likely included in the environment, in determining robotic skill(s) for robot(s) to implement in performing the task specified in the FF NL instruction.