38 results about "Object level" patented technology

The invention provides a multi-granularity open vocabulary query method based on an object-level lossless Gaussian field, and the method comprises the steps: introducing an object-level Gaussian field with a global consistency codebook, rendering a learnable semantic tag vector in the Gaussian field back to a corresponding object tag; the direct mapping between the label and the corresponding uncompressed high-dimensional feature is established through the code book, so that the semantic feature of any dimension is supported, additional compression is not needed, and the understanding capability on an object is remarkably improved; according to the method, wide quantitative and qualitative evaluation is carried out in a plurality of scenes, excellent performance in the aspects of object level zero sample segmentation and open vocabulary understanding is shown, the highest precision is particularly achieved in object-component hierarchical retrieval, and meanwhile multi-granularity scene editing is supported.

The invention provides a multi-target tracking method and device based on secondary graph matching, and belongs to the technical field of computer vision, and the method comprises the steps: employing an improved YOLOX target detection network, combining with a multi-task learning framework, and carrying out the target detection and feature extraction of an input video frame image; carrying out target classification, target association and target tracking processing by adopting a secondary graph matching strategy of target machine matching and region-level matching; after the second-level graph matching processing is completed, track prediction is carried out by adopting Kalman filtering, a multi-target tracking result is output after track updating and fusion processing are carried out in combination with background modeling based on a Gaussian mixture model, matching is carried out at a target level by adopting a GNN graph neural network, and the target identity retention capability is optimized; and then, in each target area, fine-grained matching is carried out by utilizing foreground point matching and adopting GCN graph convolution, so that the target association stability under the shielding condition is improved, and the stability of multi-target tracking is improved by adopting secondary graph matching and combining Kalman filtering and background modeling.

Methods, storage systems and computer program products implement embodiments of the present invention for identifying a vulnerability in a software application. In these embodiments, a specification is received that includes a plurality of application programming interface (API) endpoints in the software application, and a first API endpoint of the software application that exposes user information is identified. An execution path in the software application is identified that includes an ordered sequence of two or more of the API endpoints, the ordered sequence starting with a second API endpoint and ending with the first API endpoint. An attack on the software application that exploits the identified execution path is simulated, and finally, an alert is issued when the simulated attack is found to have been successful.

The invention provides an automatic object-level authorization vulnerability detection method based on a large language model, which comprises the following steps of: firstly, identifying a sensitive resource table through SQL (Structured Query Language) grammar analysis, and analyzing an operation path from a tracking request parameter to the sensitive resource table by utilizing a forward data stream; then, performing context sensing analysis on the database operation by adopting LLM to infer object-level sensitive operation; thirdly, recognizing program logic and a conditional protection mechanism in database query through static analysis, and recognizing custom verification logic such as method-level annotation and administrator permission in combination with an LLM and character string matching method; and finally, judging whether the sensitive operation lacks user-defined verification, ownership SQL constraint and access control conditions at the same time or not by integrating the mechanism, and generating a vulnerability report according to the judgment result. The invention further provides an automatic object-level authorization vulnerability detection system based on the large language model. Therefore, the detection precision can be effectively improved, and false alarm and missing alarm are remarkably reduced.

The invention discloses an object level change detection method based on semantic flow feature alignment and global attention fusion, and belongs to the field of change detection. The method comprises the following steps: inputting a double-time-phase image and carrying out preprocessing; constructing a twin feature encoder to extract multi-scale features; a self-global cross attention module is introduced in a coding stage to realize global semantic fusion and coarse registration of double-temporal image features; a semantic flow alignment module is further designed, and pixel-level alignment of a feature level is realized by generating a semantic flow field, so that view angle difference and structure offset are compensated; in the decoding stage, a feature optimization and jump connection fusion mechanism is adopted, shallow spatial information and deep semantic features are integrated, and boundary expression of a change region is enhanced; and finally, outputting an object level change detection result through a detection head. Experiments show that the method has object-level change detection capability in scenes with visual angle differences, weak textures and the like.

The invention discloses a high-standard farmland scene recognition method based on a deep neural network, and relates to the technical field of remote sensing image processing and agricultural information, and the method comprises the steps: 1, constructing a high-standard farmland scene sample library with prior significance, the high-standard farmland scene sample library comprises high-standard farmland sample features and high-standard farmland sample scale quantities; 2, constructing a high-standard farmland scene recognition model based on multi-source data fusion; 3, predicting a result, and outputting a scene category graph; according to the high-standard farmland scene recognition method based on the deep neural network provided by the invention, the problem that the prior art stays at a pixel level or an object level, depends on a single threshold value or shallow learning and has a semantic gap is solved.

A method for improving 3D object detection via object-level augmentations is described. The method includes recognizing, using an image recognition model of a differentiable data generation pipeline, an object in an image of a scene. The method also includes generating, using a 3D reconstruction model, a 3D reconstruction of the scene from the image including the recognized object. The method further includes manipulating, using an object level augmentation model, a random property of the object by a random magnitude at an object level to determine a set of properties and a set of magnitudes of an object manipulation that maximizes a loss function of the image recognition model. The method also includes training a downstream task network based on a set of training data generated based on the set of properties and the set of magnitudes of the object manipulation, such that the loss function is minimized.

The application discloses a search platform object-level data permission management method for a non-uniform permission system and multi-source data, which sequentially comprises classification of multi-source data collectable by a search platform, establishment of search platform permission data standard specification, multi-source data permission requirement analysis, data source permission data processing, search platform target permission data processing, search platform target permission data collection, search platform knowledge production, search platform user permission configuration and personnel authentication in a search request; the method solves the problem that a permission model based on RBAC in the prior art cannot adapt to object-level data permission management requirements in a non-uniform permission system and a multi-data-source enterprise search platform, realizes compatibility of data permission management of different data sources, different permission systems and differentiated organization mechanisms, and the granularity of search platform data permission management reaches the object level.

The application discloses a kind of text writing revision auxiliary method and system, it is related to artificial intelligence technical field, method includes: obtaining the current text description of user, extract entity text and carry out semantic sorting, generate entity text sequence;The specification score, sentiment score and writing object level of each entity text are identified by word vector model, generate first projection point;With each entity text as center text, the projection point of adjacent text in window is fused, to obtain second projection point, build current scene polyhedron;Each first projection point of entity text sequence and current scene polyhedron are input to writing detection model, model is encoded by multi-head self-attention and 3D grid convolution operation, generate text and scene vector and weighted fusion, decode output error prediction and position push to user.By implementing the application, the multidimensional characteristics of the scene are retained, thereby solving the problem that a single scene tag cannot reflect the real scene with multiple elements interwoven in the prior art.

The application discloses a kind of intelligent campus-oriented electromechanical installation management method and system, it is related to computer platform load balancing technical field, including constructing electromechanical installation object basis set and object level installation constraint set.Further generate electromechanical installation task, task branch regulation is executed in combination with construction state data, form three kinds of tasks of executable, to be made up and collaborative adjustment.For executable task, installation process data is collected, object level installation process record is constructed, object level installation verification and abnormal disposal path regulation are executed, and object level installation closed loop result is formed.Based on installation closed loop result, systematized debugging, acceptance collection, file construction and constraint update processing are implemented, and constraint optimization result is formed.The method described in the application realizes the fine flow of intelligent campus electromechanical installation task, abnormal closed loop disposal and result feedback optimization, significantly improves the accuracy, cooperativity and traceability of installation management.

A method for interactive controlling in a game includes: in response to an interactive behavior operation, controlling a controlled virtual object to execute an interactive behavior against a specified object; in response to an interaction condition being satisfied, updating an attribute parameter of the specified object, and increasing an experience parameter of the controlled virtual object according to a preset rule; in response to the experience parameter reaching a preset experience threshold, increasing an object level of the controlled virtual object in a game match, determining at least part of candidate items from a specified item set, and displaying the candidate items; in response to a selection operation on a first item from the candidate items, controlling the controlled virtual object to acquire the first item; and in response to a trigger operation on the first item, displaying a game behavior corresponding to the first item in the game scene.

The application relates to the field of new energy automobile power supply, and provides a charging pile permission management method and device based on dynamic attributes and a storage medium, the method comprises the following steps: collecting a set of dynamic attributes related to a business request; generating a real-time permission decision instruction based on the set of dynamic attributes and a predetermined permission policy; performing corresponding operations according to the target object level of the real-time permission decision instruction, wherein if the target object is a device-level resource, a charging pile control operation corresponding to the real-time permission decision instruction is performed, and if the target object is a management node, a permission token distribution operation is performed; in the permission decision and execution process, the involved data is subjected to security processing; the permission decision and execution process is monitored and recorded to generate monitoring data, and an abnormal index is generated based on monitoring data analysis; an operation log is generated based on the monitoring data and is stored. The application can realize dynamic, accurate, safe and controllable management of charging pile permissions and full-process auditability.

The application discloses a kind of multi-feature supported spillway remote sensing identification method and system, and the object level target is obtained by image segmentation algorithm;Spectrum, shape, geometric stereo feature of object level target is extracted, and preliminary extraction of spillway object is realized by multi-feature joint decision;The spatial direction and distance relationship between objects are comprehensively considered, and the fine spot around the preliminary identification result is identified and integrated again, contour modification is carried out simultaneously, and finally the relatively neat spillway boundary vector is output.This method specifically designs the geometric stereo feature parameters of spillway to highlight the unique features of spillway in three-dimensional space, effectively overcoming the difficulty of distinguishing spillway from general buildings.At the same time, the method also provides an automatic remote sensing spillway identification process to improve the accuracy of spillway identification, which has important practical application value in the fields of spillway remote sensing monitoring and water conservancy construction.

In a database system, wherein data is stored as objects within an object storage system, a system and method for estimating object cardinality, determining query execution plan costs, and selecting a query plan for execution by the database system. Multiple object cardinality estimation approaches for estimating the number of objects to be accessed for a given query condition on a column of a relation composed of a set of objects, where each object maintains the minimum value and the maximum value of individual columns are presented. A set of global statistics is also maintained, consisting of the total number of objects and the minimum and maximum values of individual columns. The object cardinality estimation is determined based on the global statistics without retrieving individual object-level statistics.

The invention discloses a small sample semantic segmentation method based on object query, and the method comprises the following steps: generating N object prototypes through mask pooling operation, the object prototypes comprising first N / 2 foreground prototypes and last N / 2 background prototypes; based on the object prototype, performing class correlation enhancement on the support set features and the query image features through a feature fusion module to generate enhanced features; inputting the enhanced features and the object prototype into a bidirectional Transform module, carrying out pixel-level and object-level bidirectional interaction through a foreground-background separated mask attention mechanism, and outputting segmentation features; generating a final semantic segmentation mask according to the segmentation features; according to the invention, the background interference problem is effectively reduced.

Embodiments of the present disclosure relate to application programming interface (API) billing based on an API object level. A method performed by an API open function (AEF) in a universal application programming interface (API) framework (CAPIF) is provided. The method includes receiving, from an API caller, a request to invoke a serving API for an API object lifecycle or access operation of an API object. The method includes determining that an API object lifecycle or access operation is associated with at least one API object-related trigger condition of a satisfied charging event. The method includes triggering a CAPIF core function to report charging information for a charging event to a charging function. And the method includes sending a response to the request to the API caller.

The embodiment of the invention provides a service asset dynamic allocation method and related equipment, and belongs to the technical field of computers. The method comprises the following steps: collecting first data of an object index according to a website registration object, and collecting second data of a domain name index according to a website domain name; performing data cleaning and grading processing on the first data and the second data to correspondingly obtain an object grade and a domain name grade; under the condition that the domain name level or the object level is changed, mapping the domain name level and the object level, and determining target service assets of the website; and changing an address value pointed by the access point of the website according to the address corresponding to the target service asset. According to the embodiment of the invention, the resource utilization rate and security of the server can be improved.

The application relates to a duct production information input method, computer equipment and a storage medium. The method comprises the following steps: in response to a trigger operation of a user, determining a target object level selected by the user; acquiring all duct fittings under the target object level; taking a first duct fitting in all the duct fittings as a target duct fitting, and extracting target information of the target duct fitting; obtaining target production information of the target duct fitting based on the target information and a preset tray configuration table; writing the target production information into an attribute of the target duct fitting, taking a next duct fitting of the target duct fitting as a new target duct fitting, and repeating the execution until the target duct fitting is the last duct fitting in all the duct fittings. The application can realize batch automatic input of duct production information, and manual discrimination and input of the user are not required, so that the efficiency and accuracy of duct production information input are improved.

A process for sensing a scene. The process includes receiving sensor data from a plurality of sensor modalities, where each sensor modality observes at least a portion of the scene containing at least one of the objects of interest and generates sensor data conveying information on the scene and of the object of interest. The process further includes processing the sensor data from each sensor modality to detect objects of interest and produce a plurality of primary detection results, each detection result being associated with a respective sensor modality. The process also includes fusing sensor data from a first sensor modality with sensor data from a second sensor modality to generate a fused 3D map of the scene, processing the fused 3D map to detect objects of interest and produce secondary detection results and performing object level fusion on the primary and the secondary detection results.

The invention discloses a video question-answering method and system based on a multi-modal multi-layer attention model, and the method comprises the steps: carrying out the segment division and key frame extraction of a video, obtaining object-level, frame-level and segment-level visual features, and carrying out the semantic coding of a question text and a candidate answer; modeling a cross-time object relationship by using a global object attention mechanism, fusing visual and text features layer by layer at an object level, a frame level and a fragment level in combination with layered cross-modal attention, and generating problem-aware video multi-modal feature representation; and finally completing answer matching and prediction based on comparative learning. According to the method, a lightweight layered fusion structure is adopted while the object-level semantic relationship is explicitly modeled, so that the video semantic understanding and question and answer accuracy are effectively improved, the model calculation complexity is remarkably reduced, and the method has good engineering practicability and deployability.

The invention discloses a project allocation method and device, a computer program product and electronic equipment. The method comprises the following steps: determining feature information of a target project, and determining a project grade and a project type of the target project according to the feature information; obtaining a working index data set of each first candidate object under the target mechanism, and determining an object level and an object type of each first candidate object according to the working index data set of each first candidate object; selecting N second candidate objects from the M first candidate objects according to the item level and the object level of each first candidate object; and selecting a target object according to the item type and the object type of each second candidate object, and allocating the target item to the target object. According to the method and the device, the problem of relatively low project task allocation accuracy and efficiency in related technologies is solved.

An object evaluation method and an object evaluation device. The object evaluation method includes: obtaining an image of a first object to be evaluated; determining first level information by means of a first object level classifier according to the image of the first object to be evaluated; determining adjustment weights of various object levels according to distribution proportions of the object images of the various object levels in a plurality of object images having the same object type as that of the image of first object to be evaluated and having known object levels, wherein an object level having a relatively high distribution proportion has a relatively low adjustment weight; and respectively generating second rating confidences of the various object levels according to first rating confidences and the adjustment weights of the various object levels to form second level information.

The application provides a visual analysis system and method for unified analysis of multiple computer vision tasks, which comprises the following steps: receiving multiple pictures containing at least one object, annotation results of the object in each picture and model prediction results of the object level in each picture, wherein the annotation results and the model prediction results both comprise detection classification results of the object level and detection position results of the object level or segmentation classification results of the object level and segmentation position results of the object level in each picture, the detection classification results and the segmentation classification results comprise discrete values of discrete attributes, and the detection position results and the segmentation position results comprise continuous values of continuous attributes; according to the annotation results of the object in each picture and the model prediction results of the object level in each picture, unified modeling of discrete attributes and continuous attributes in the results is performed by using a joint probability distribution, so that a multivariate probability distribution is obtained; and the multivariate probability distribution is displayed in multiple levels.

The invention provides an object evaluation model training method and an object evaluation method. The training method comprises the steps of obtaining a pre-trained object evaluation model; at least part of training samples in the training sample set are clustered according to output of a specified layer in the object evaluation model, so that second labels of sample object images of the at least part of training samples are generated, and the at least part of training samples are updated; the object evaluation model is trained according to the updated training sample set so as to update model parameters of the object evaluation model, and the object evaluation model can at least output first-level information of an object image under the condition that the object image is input. The first level information includes a first level confidence that objects in the object image are determined as respective first object levels.

This specification provides an image processing method and apparatus for 5S management. The image processing method for 5S management includes: acquiring an image to be processed taken of a target area; identifying and locating at least one target object from the image using target detection technology; extracting local features of each target object from the image area where each target object is located; determining the degree of satisfaction between each target object and the corresponding 5S management evaluation rule based on the local features of each target object; summarizing the obtained satisfaction levels to generate a first score at the object level; and generating a 5S management evaluation score for the image to be processed based on the first score.

The invention discloses a multi-target tracking method and system based on self-adaption during testing, and belongs to the field of multi-target tracking. The method comprises the following steps: constructing different types of enhanced views for an input video frame image; a teacher-twin student model double-branch module is established, a spatial entropy method and object-level KL divergence calculation are adopted to obtain a spatial entropy and object-level uncertainty corresponding to a student model, dynamic threshold determination of different layers of the student model is realized based on the object-level uncertainty, and then learning rate dynamic allocation is realized. The enhanced detection consistency loss between the enhanced views is obtained through calculation according to the spatial entropy, the enhanced detection consistency loss and the local patch comparison loss between the enhanced views are added, and a total loss function is obtained; and optimizing the student model according to the total loss function of all the images in the video frame sequence so as to reversely update the teacher model. According to the method, tracking precision, time consistency and calculation efficiency are effectively balanced, and compared with an existing method, all aspects are remarkably improved.

The application discloses a tampered image detection method based on object semantic consistency, and relates to the field of image passive forensics. Most of the existing splicing tampering detection methods based on deep learning are realized based on anomaly detection, and there are limitations in the receptive field due to convolution operation. In view of the problem, the application adopts an encoder constructed based on an attention mechanism to respectively extract and represent the semantic information of the object level and the tampering trace information of the pixel level in the image, generates object semantic representation and tampering trace representation, and fuses and decodes two different encodings through a decoder, so that splicing tampering detection and positioning of the image are realized. The application can combine object semantic consistency and suspicious tampering traces, effectively improve the detection accuracy of the tampered image, and has practical value.

Methods, apparatus and processor-readable storage media for object level space forecasting are provided herein. An example computer-implemented method includes determining a predicted growth of an object in a database using a non-linear regression model, wherein the non-linear regression model is trained using a set of data records indicative of a size of the object over time; identifying a threshold indicative of abnormal growth of the object using a classification model, wherein the classification model is trained at least in part on one or more of the data records that are labeled as anomalous based at least in part on one or more prior values of the predicted growth and actual growth of the object; and performing one or more automated operations in response to detecting that the predicted growth of the object exceeds the threshold.

Methods, systems, and devices for data management are described. A data management system (DMS) may receive a federated login request from a user associated with one or more tenants of the DMS. The DMS may direct the federated login request to a centralized management service. The DMS may receive a security assertion markup language (SAML) assertion that indicates an identity of the user, a set of object-level permissions assigned to the user, and an identifier of a first tenant associated with the user. The DMS may identify one or more computing objects in a cluster of storage nodes that correspond to the first tenant based on the identifier from the SAML assertion. The DMS may determine that the user is authorized to perform a set of actions on the one or more computing objects based on the set of object-level permissions indicated by the SAML assertion.