1910 results about "Object based" patented technology

Systems and methods for generating contextualized data visualizations. A first graphical user interface (GUI) object based on first data is provided, along with a second GUI object based on the second data. A user may select a scenario to be analyzed and, in response to the single selection of the scenario, the system models a first impact of the scenario on the first data to generate predicted first data and separately models a second impact of the scenario on the second data to generate predicted second data. The predicted data may be generated by an artificial intelligence system including a prediction processor. The first and second GUI objects are updated using the predicted first and second data, respectively.

In some embodiments, a computer system performs virtual object manipulation operations using respective portions of the user's body and / or input device(s). In some embodiments, a computer system manipulates a virtual object based on input from a hand of a user and / or a handheld device. In some embodiments, a computer system manipulates a virtual object directly or indirectly.

A system for timing or zone alert determination from vehicle events and / or an accident database includes an interface and a processor. The interface is configured to receive a location of a translating object. The processor is configured to trigger a warning to the translating object based at least in part on its location and the existence of an associated hazard.

Embodiments of the disclosure provide for improved automation of software change management. Such embodiments enable automatically managing and testing software modifications. Some embodiments receive a change request data object indicative of a software modification, generate a risk level of the software modification based on the change request data object, and generate a test scheduling data object based on the change request data object and the risk level, where the test scheduling data object is indicative of a testing operation for the software modification. Some embodiments initiate performance of the testing operation at a testing environment and receive, from the testing environment, test performance data. Some embodiments determine a test failure based on the test performance data and, in response to determining the test failure, generate a communication bridge between a subset of a plurality of computing devices based on the test failure and a development flow data object.

The invention relates to the technical field of pressure control, in particular to a dynamic pressure control system and method of a strapping machine. The method comprises the following steps: acquiring information data of an object to be bundled; performing three-dimensional modeling on the to-be-bundled object information data to generate three-dimensional modeling data of the to-be-bundled object; binding mode confirmation is conducted on the binding machine according to the three-dimensional modeling data of the to-be-bound object, binding mode data are obtained, and the binding mode data comprise a single-layer binding mode and a multi-layer binding mode; initial strapping pressure setting is carried out on the information data of the to-be-strapped object based on the single-layer strapping mode, and single-layer strapping initial pressure data is obtained; and carrying out object deformation binding pressure adjustment on the single-layer binding initial pressure data to generate single-layer binding pressure adjustment data. Through three-dimensional modeling, dynamic pressure adjustment and a feedback control mechanism, the intelligence and precision of dynamic pressure control of the strapping machine are improved.

The invention provides a risk conduction prediction method and system based on combined deduction of a time sequence diagram and a large model, and relates to the technical field of artificial intelligence and knowledge engineering, and the method comprises the steps: extracting a prospective time sequence fact based on a hierarchical cue word and a structured constraint mechanism; extracting conflicts and generating a sequential relationship; the historical time sequence knowledge graph is updated based on the time sequence relation and a confidence coefficient weighted updating strategy; determining a central entity, and initiating a structured query to the updated historical time sequence knowledge graph by taking the central entity as a starting point to generate a context knowledge sub-graph; converting the context knowledge sub-graph into a natural language description with a logic relationship, injecting a risk hypothesis event, constructing a cue word as a new input of a large model, and outputting to obtain a structured JSON object containing a complete reasoning chain; and converting the structured JSON object based on a risk path extraction and visualization algorithm influencing weight attenuation to obtain risk early warning information and a visual conduction path diagram.

The invention discloses a retrieval response method and device, a storage medium, electronic equipment and a program product, and relates to the technical field of big data, and the retrieval response method comprises the following steps: under the condition that a prompt instruction for processing an original document is received, performing segmentation processing on the original document according to sequence termination probabilities corresponding to different data sub-blocks in the original document, n parent text blocks are obtained; paragraph splitting processing is conducted on each parent text block in the N parent text blocks, N * M sub text blocks are generated, and N and M are positive integers; vectorizing the N * M sub-text blocks to obtain N * M query reference vectors; according to the method, retrieval response is performed on the query problem issued by the target object based on the N * M query reference vectors, the technical problems of semantic fracture and low retrieval accuracy caused by traditional fixed blocking are solved, and the technical effect of improving the retrieval accuracy after document blocking is achieved.

The present application is directed towards systems and methods for cluster-based code analysis and transformation. Cluster-based analysis may group code objects based on their similarity across functional areas, such as where a code object is cloned in multiple areas (e.g. sort functions that are duplicated across areas, or reports or tables that are identical). In some implementations, objects may be grouped into clusters by type, or based on reading from or writing to a common table. In some implementations, clustering at different layers may be possible.

The embodiment of the invention provides a product recommendation method. The method comprises the steps of obtaining corresponding dialogue data when a target object selects a product; performing semantic recognition based on the dialogue data to determine dominant features corresponding to the target object; calling associated information corresponding to the target object, and performing feature extraction according to the associated information to obtain hidden features; and fusing the dominant features and the recessive features to obtain cross-domain information, so as to recommend a target product to the target object based on the cross-domain information. Therefore, a product recommendation process based on dominant features and recessive features is realized, dialogue data is fully mined, multi-dimensional information fusion is performed, the accuracy of dialogue understanding is improved, information supplementation is performed through the recessive features, the comprehensiveness of object representation is further improved, and the accuracy of product recommendation is improved.

The present disclosure provides a state estimation apparatus of estimating a current state of an object based on state information and observation information for dynamically replicating the object, the state estimation apparatus including an input unit that receives the observation information, and a current state estimation unit that generates current state estimation information through the observation information, wherein the current state estimation unit includes a prediction information generation unit that generates state prediction information through inputting past state estimation information obtained by delaying the current state estimation information by one time unit into a previously prepared state prediction model, and a prediction information correction unit that calculates a Kalman gain matrix associated with a degree of correction of the state prediction information through artificial neural network(s), and corrects the state prediction information through the observation information and the Kalman gain matrix to generate the current state estimation information.

The invention provides a digital human video generation method and device based on a large model, an intelligent agent, electronic equipment and a storage medium, relates to the technical field of artificial intelligence, in particular to the technical fields of deep learning, the large model, computer vision and the like, and can be applied to scenes such as video live broadcast, advertisement production, e-commerce sales and the like. The digital human video generation method based on the large model comprises the steps that demand information is acquired, the demand information comprises action description information used for describing a specified action video clip, and the action video clip represents a specified action of a target object; processing the demand information by utilizing a language large model to obtain a target script which comprises a target oral playing fragment text matched with the action description information; and processing the target script and the action video clip by using a visual large model to obtain a target video which is used for displaying oral playing of the target object based on the target oral playing clip text in the process of executing the specified action.

The invention provides an open vocabulary task guiding grabbing method and device, a medium and a program product. The open vocabulary task guiding grabbing method comprises the steps that a user language instruction and a complex scene image are input into a preset visual language model; identifying a target task corresponding to the user language instruction through a preset visual language model, matching a target object associated with the target task in the complex scene image, and determining an optimal grabbing component of the target object based on a plurality of functional components of the target object; constructing a plurality of capture candidate schemes by adopting the optimal capture component and the complex scene image; and performing optimization solution on each grabbing candidate scheme to determine the optimal grabbing pose, so as to solve the problem that the existing method is poor in effect when dealing with an unseen object and a new task in a scene with open vocabularies due to the limited scale of an operability memory bank in the related technology. The invention aims at solving the problem that the existing method is poor in effect when dealing with the unseen object and the new task in the scene with the open vocabularies.

Disclosed is an object recognition method and object recognition apparatus. The object recognition method may include detecting an object region comprising an object in an input image, determining scores corresponding to regions of the object region, determining weights based on the scores and detection information of the object region, determining a final score based on the scores and the determined weights, and determining a result of recognizing the object based on the final score.

Apparatuses, systems, and techniques to use one or more neural networks to perform one or more tasks. In at least one embodiment, said one or more neural networks use one or more textual descriptions to generate one or more three-dimensional (3D) models of one or more first objects based, at least in part, on two or more images of one or more second objects from two or more viewpoints.

The invention discloses a credit rating report generation method and device, and the method comprises the steps: obtaining and inputting the basic information of a to-be-rated object and the industry evaluation information of the to-be-rated object into a large language model, and obtaining an evaluation grade; obtaining an initial prompt word set corresponding to the to-be-evaluated object; optimizing the plurality of initial cue words; inputting each crossover variation cue word and scoring cue word evaluation level into a large language model to obtain a scoring result; and generating a credit rating report of the to-be-rated object based on the evaluation grade scoring result of the to-be-evaluated object. And according to a scoring result of the crossover variation cue words, screening out an optimal cue word, thereby generating a credit evaluation report according to the optimal cue word and the evaluation grade. According to the method, the problem that data updating is not timely due to manual information transmission is avoided. Meanwhile, the crossover variation cue word is optimized, so that the generation efficiency can be improved on the premise of ensuring the quality of the credit evaluation report.

Disclosed in some examples are methods, systems, and machine-readable mediums for utilizing context information to create decoding feedback information to improve decoder accuracy and / or performance. In some examples, the context information is from layers of a network stack above the layers in which the decoders are present. The context information may be or be based upon information about previously received and decoded data and / or information about the sender to provide decoding feedback information to the decoder that is used either to correct a previous decoding error or to inform the decoder on which of a plurality of decoding choices is more likely to be correct. This may increase decoding performance by decreasing errors and in some examples, reducing the complexity of choices by eliminating certain decoding possibilities and thus increasing decoder efficiency.

Systems and methods for generating simulation data based on real-world dynamic objects are provided. A method includes obtaining two- and three-dimensional data descriptive of a dynamic object in the real world. The two- and three-dimensional information can be provided as an input to a machine-learned model to receive object model parameters descriptive of a pose and shape modification with respect to a three-dimensional template object model. The parameters can represent a three-dimensional dynamic object model indicative of an object pose and an object shape for the dynamic object. The method can be repeated on sequential two- and three-dimensional information to generate a sequence of object model parameters over time. Portions of a sequence of parameters can be stored as simulation data descriptive of a simulated trajectory of a unique dynamic object. The parameters can be evaluated by an objective function to refine the parameters and train the machine-learned model.

The invention discloses a hoisting posture prediction method and device, electronic equipment and a storage medium, and relates to the technical field of hoisting postures and the like. The hoisting posture prediction method comprises the steps that a three-dimensional model of a target object is constructed based on region division of the target object; extracting a key mode based on the three-dimensional model and constructing an attitude reduced-order model; obtaining a gravity parameter based on the current hoisting path; and solving the attitude reduced-order model based on the gravity parameter and a first constraint condition to obtain a hoisting prediction attitude. According to the hoisting attitude prediction method disclosed by the invention, the three-dimensional model of the target object considering the precision and the calculated amount is adaptively established by dividing the region of the target object, and the attitude in the hoisting process is predicted in real time by solving the attitude reduced-order model, so that real-time risk early warning and active protection are realized.

Apparatuses, systems, and techniques are presented to generate digital models. In at least one embodiment, a neural network is used to generate motions of a first portion of an object based on motions of a second portion of the object and audio corresponding to the motions of the second portion of the object. For example, a neural network may use audio information to generate motions of facial and body features of a digital model.

The invention provides a training-free text-image generation method based on a diffusion model, and relates to the technical field of computer graphic processing and artificial intelligence. The method comprises the following steps: extracting semantic phrases and layout information in an input text by utilizing a natural language model, inputting the input text, the semantic phrases and the layout information as additional conditions into a diffusion model, and extracting cross attention maps of different time steps; a positive and negative sample concept and a foreground and background concept based on an object are constructed, a new loss function is calculated on a cross attention map for semantic information and layout information, the loss function combines semantic loss, regional loss and original loss of a diffusion model and is used for updating a potential space image, and the image is generated through iterative denoising and a decoder. According to the method, additional training is not needed, image generation output based on the diffusion model better meets text requirements, and a better text and image alignment effect is achieved.

An interactive orbital trajectory prediction system including a processor arranged to simultaneously display: i) a surface of the first primary body including the relative predicted trajectory of the object, ii) a surface of the second primary body including the relative predicted trajectory of the object, and iii) the barycenter of the two primary bodies, and the trajectory of the object with respect to the barycentric reference frame. The system includes an input device arranged to receive a user selection of a first sensor module of a plurality of sensor modules used to predict the trajectory of the object orbiting the primary body. The processor, in response to the user selection of the first sensor module, predicts the flight path of the object based on the first sensor module.

The invention relates to the technical field of three-dimensional reconstruction, and provides a data synthesis method and a training method for monocular three-dimensional reconstruction based on synthesized data, and the method comprises the steps: 1, rendering a picture containing a specific object into a plurality of pictures I corresponding to the object at different angles; step 2, obtaining a mask m of the object based on the picture I, and 3, guiding the picture generation model to generate a new picture which conforms to the text description and retains the structural information of the specific object by utilizing the picture I by utilizing the mask m and the additionally input text description. According to the method, a large number of 3D models and picture pairs are generated through different viewpoints, styles and background conditions, a rich training data set is provided, and the generalization ability of the monocular three-dimensional reconstruction model can be remarkably improved through the diversity. By randomly applying disturbance within a certain angle range of a plurality of viewpoints (R, T), the adaptive capacity of the model to different camera poses is improved, and the robustness of monocular three-dimensional reconstruction is enhanced.

This disclosure describes systems, methods, and devices related to object detection in images. A device may input an image, representing an object, to a manual labeling learner system; identify, using the system, first coordinates of an upper left corner of a bounding box representing the object based on a heatmap indicative of a probability of the first coordinates representing the upper left corner; identify, using the system, second coordinates of a bottom right corner of the bounding box based on the first coordinates and a first distance regression map indicative of coordinate differences between the second coordinates and ground truth coordinates input to the machine learning model as training data; generate, using the system, adjustments to the first coordinates and the second coordinates based on a second regression map; and generate, using the system, the adjusted first and second coordinates, the bounding box.

The invention discloses an action space determination method and device of an intelligent agent, equipment and a storage medium, and relates to the field of artificial intelligence and machine learning. The method comprises the steps of determining target high-resolution visual features of a target object according to a collected time sequence picture of the target object based on a high-resolution visual model; determining target global visual features of the target object according to the time sequence picture based on a global visual model; preprocessing the initial noise, the initial diffusion step length and the historical time sequence action sequence of the target agent to obtain a target noise variable, a target diffusion step length and a target time sequence action sequence; and based on a multi-modal diffusion model and a decision diffusion model, according to the target high-resolution visual feature, the target noise variable, the target global visual feature, the target diffusion step length and the target time sequence action sequence, determining a target action space of the target agent. According to the technical characteristics, the accuracy and the reliability of determining the action space can be improved.

Apparatuses, systems, and techniques are presented to introduce noise or variation into images. In at least one embodiment, one or more neural networks are used to determine noise to be added to one or more images of one or more objects based, at least in part, upon one or more features of the one or more objects.

The disclosure provides a sentence generation method based on a large language model including: obtaining a query sentence, in which the query sentence has at least one candidate description object; performing semantic recognition on the query sentence to obtain first semantic information and second semantic information corresponding to the query sentence, in which categories of the first semantic information and the second semantic information are different; inputting the at least one candidate description object and the first semantic information into the large model, to identify a target description object from the at least one candidate description object based on the large model; selecting target service data from at least one piece of service data corresponding to the target description object based on the second semantic information; and generating a reply sentence corresponding to the query sentence according to the target service data.

An approach for adapting volumetric objects in response to user prompts or commands may be presented. The approach may consist of training a 3-D Generative Adversarial Network to manipulate volumetric objects based on generated responses to prompts and user manipulations within a virtual reality environment. The approach may include identifying a volumetric object a user prompt is directed to and generating an appropriate auditory response to the prompt in a natural language format. The approach may also include adapting the identified object based on the response or mapping the correct manipulation to the volumetric object to provide movement synced to the auditory response.

One embodiment of a method for determining object poses includes receiving a first image of an object, sampling an initial pose of the object, performing one or more operations to update the initial pose to determine a first pose of the object based on the first image, a first rendered image of the object in the initial pose, and one or more transformer encoders.

The invention discloses an object three-dimensional reconstruction method, device and system based on deep learning. The reconstruction method comprises the following steps: acquiring a multi-view color image of an object through a controllable image acquisition device; reconstructing a sparse three-dimensional point cloud by using a motion recovery structure method and obtaining a camera pose; initializing parameters of the three-dimensional Gaussian sputtering model based on the sparse point cloud and performing training optimization; a target object semantic segmentation data set is constructed, and a low-rank adaptive technology is adopted to finely segment all models; generating prompts through an open vocabulary detection model at each view angle, obtaining an accurate segmentation mask, and optimizing a three-dimensional segmentation weight by adopting a joint loss function fusing color consistency loss and edge perception loss; and finally outputting the color three-dimensional point cloud of the target object. According to the method, the original image is segmented, so that the influence of the quality of the rendered image is avoided; the segmentation precision of the model in a specific scene is improved through field adaptive fine tuning; and the accuracy of the segmentation boundary is ensured by adopting a double-loss joint optimization mechanism.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes such as robot agent decision making, financial science and technology and medical health, and discloses an operation control method, device, equipment and medium based on an object flow sequence. The method comprises the following steps: exploring a mapping relation between an object flow sequence and an execution equipment action sequence in a simulation environment, generating a flow conditional action strategy, inputting a target object flow sequence to generate an initial execution equipment action sequence, tracking a real-time motion state of a target object to obtain a real-time object flow sequence, and comparing the real-time object flow sequence with the target object flow sequence. And dynamically adjusting the action sequence of the execution equipment based on the deviation. According to the method, the object flow and execution equipment action sequence mapping is constructed, and real-time object flow tracking and dynamic adjustment are combined, so that the influence of operator difference and environment uncertainty is reduced, and the operation accuracy and universality of the robot are improved.