1310 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.

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 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.

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 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.

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.

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.

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.

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.

Methods and apparatus for grasping an object by a suction-based gripper of a mobile robot are provided. The method comprises receiving, by a computing device, from a perception system of the mobile robot, perception information reflecting an object to be grasped by the suction-based gripper, determining, by the computing device, uncertainty information reflecting an unknown or uncertain extent and / or pose of the object, determining, by the computing device, a grasp strategy to grasp the object based, at least in part, on the uncertainty information, and controlling, by the computing device, the mobile robot to grasp the object using the grasp strategy.

An apparatus comprising an interface and a processor. The interface may be configured to receive pixel data. The processor may be configured to process the pixel data arranged as video frames, perform computer vision operations on the video frames to detect objects in the video frames, analyze characteristics of the objects detected, determine adaptive auto-exposure weightings in response to the characteristics of the objects detected, generate an auto-exposure weighting table based on the adaptive auto-exposure weightings and generate output video frames. Auto-exposure for the output video frames may be determined in response to the auto-exposure weighting table and the video frames. The auto-exposure weighting table may be generated for each of the video frames.

Various embodiments of the present disclosure provide prompt engineering and iterative, feedback-based generative techniques that improve traditional LLM technology, including extractive LLM techniques. The techniques may include generating, using a machine learning classification model, a resolution capability classification for an input data object that comprises an input question and an input document; generating using a large language model (LLM), an initial predictive output for the input data object based on an initial generative model prompt for the input data object; identifying a classification model output divergence based on a comparison between the resolution capability classification and the initial predictive output; and in response to the classification model output divergence: generating an augmented generative model prompt by modifying the initial generative model prompt with a representation of the resolution capability classification, and generating, using the LLM, an updated predictive output based on the augmented generative model prompt.

The invention discloses an unmanned simultaneous positioning and mapping method and device, a medium and equipment, and relates to the technical field of computers, and the method comprises the following steps: obtaining image data of the surrounding environment of unmanned equipment, carrying out semantic segmentation through employing an improved semantic segmentation model, and determining the semantic information of each pixel; acquiring multi-frame point cloud data, and performing distortion correction on the multi-frame point cloud data by performing pre-integration on the inertial measurement unit data; projecting each frame of point cloud data to the time-space synchronized image data, and determining a semantic tag of a three-dimensional point in each frame of point cloud data; removing three-dimensional points of the dynamic object according to the speed and the space overlapping degree; and optimizing a surrounding environment map and a pose map of the unmanned equipment by selecting a key frame and a loopback candidate frame. According to the method, the semantic segmentation technology and the laser radar inertial odometer are fused for positioning and mapping, so that the positioning and mapping precision in a dynamic similar environment is greatly improved.

A system and method for measuring the position of a moving object based on AI, which provides location and distance information of a moving object to a user using artificial intelligence-based image analysis and distance measurement. Accordingly, the system for measuring the position of an AI-based moving object includes a first camera that captures a first object, a second camera installed around the location where the first object has moved and captures the first object and a second object associated with the first object, and a rangefinder for measuring a first distance between the first object and a distance measurement reference position and a second distance between the second object and a distance measurement reference position; and an object monitoring device that calculates a position value of the first object and a position value of the second object using the first and the second distances measured by the rangefinder.

The invention provides a data processing method and a related device. The method comprises the steps of determining a first index value based on a first text and a second text; the first text is a session text of a first object and a second object generated by a first model, and the second text is a session text of a third object and the second object; the first index value is used for representing the session quality of the first object and the second object; determining a second index value based on the session response duration of the first object and the second object; the second index value is an index value used for representing the session response speed of the first object; determining an evaluation score for the first model based on the first index value and the second index value; and determining a text generation model matched with the first object based on the evaluation score. Through the method, the text generation effect of the text generation model corresponding to the first object can be improved.

The invention provides an information recommendation method and device, electronic equipment, a computer readable storage medium and a computer program product. The method comprises the steps of obtaining a vector combination; performing dimension reduction based on the vector combination to obtain a first output vector; performing attention coding based on the first multi-modal feature vector to obtain a first weight coefficient set corresponding to the first multi-modal vector, and determining a second output vector representing the individual interest of the target object based on the first weight coefficient set; performing feature extraction on the first multi-modal feature vector to obtain a third output vector representing the group interest of the group to which the target object belongs; determining recommendation parameters based on the first output vector, the second output vector and the third output vector; and determining a target recommendation object for the target object from the plurality of candidate objects based on the plurality of recommendation parameters corresponding to the plurality of candidate objects. According to the method and the device, the target recommendation object can be determined from multiple dimensions, and the recommendation accuracy is improved.

Disclosed herein are system, method, and computer program product aspects for semantic search in a model-based prompt builder system. A system generates a search retriever object based on a search index comprising unstructured data. The search retriever object includes metadata specifying one or more details of a vector search operation to be performed on the search index. The system obtains search results by performing the vector search on the search index based on the one or more details of the vector search operation provided by the search retriever object and a search query. The system provides the search results to a prompt generator configured to use a model to generate a reply to a prompt request requiring the search results.

The invention provides a shader dynamic modification method, a graph rendering method and device and related equipment, and the method comprises the steps: obtaining creation parameters in a resource creation interface when it is detected that a graph processing application calls the resource creation interface, and the resource creation interface is used for creating a pipeline state object, the creation parameter comprises a plurality of shaders, and the plurality of shaders are expressed in the form of a first intermediate language code; under the condition that a to-be-modified first shader exists in the creation parameters, in response to a modification operation on the first shader, determining a modified second shader; a second pipeline state object is created according to the second shader, and the second pipeline state object is used for calling a graphic interface event by the graphic processing application and executing a graphic rendering task of the graphic interface event. According to the embodiment of the invention, the method can achieve the dynamic modification of the shader, and improves the modification efficiency and flexibility of the shader.

The invention discloses an object pose estimation method based on deep learning and synthetic data and related equipment, and belongs to the technical field of computer vision. The method comprises the following steps: generating an instance segmentation and pose estimation synthesis data set by utilizing simulation modeling software and an automatic script; a YOLO11-seg model is trained, and a data set used for training a domain adaptation model is constructed in an auxiliary mode; training a simulation-to-reality domain adaptation model based on the improved CycleGAN and using the model to enhance the fidelity of the RGB image in the pose estimation synthesis data set; and training a pose estimation model based on multi-modal feature fusion and an attention mechanism by using an enhanced synthetic data set, extracting real scene target foreground RoI data by using YOLO11-seg, inputting the pose estimation model, and outputting a 6D pose of an object. According to the method, the object pose in the disordered scene can be accurately estimated, the grabbing success rate of the robot is improved, the inter-domain difference between synthetic data and real data can be effectively reduced, the data labeling cost is reduced, and the method can be suitable for industrial scenes.