223 results about "Search graph" patented technology

The invention relates to a single-target tracking method based on global perception and lightweight self-attention, and the method is characterized in that the method comprises the steps: respectively inputting an obtained template image and a search image into a weight sharing feature extraction module, and extracting a template feature map and a search feature map; performing attention enhancement of channel and space dimensions on the extracted feature map through a joint global perception module, and outputting an enhanced template feature map and an enhanced search feature map; an improved Transform module is adopted to carry out local feature enhancement and lightweight self-attention calculation on the enhanced feature map, and reconstructed template features and search features are obtained; and inputting the reconstructed template features and search features into the trained target model predictor to realize target tracking and positioning. Compared with the prior art, the method has the advantages of realizing balance between target tracking precision and real-time performance and the like.

Examples described herein relate to techniques for facilitating selection of stickers for inclusion in messages within the context of an interaction system. According to some examples, message content is detected and a set of candidate stickers is identified based on the message content. A search icon is dynamically replaced with a representation of respective ones of the set of candidate stickers. At a first point in time, the search icon represents a first candidate sticker of the set of candidate stickers. At a second point in time, the search icon represents a second candidate sticker of the set of candidate stickers.

The invention provides a target tracking method and system based on global and local two-way extraction and asynchronous enhancement, and the method comprises the steps: carrying out the initialization of a template image and a search image, and carrying out the sequence division through block embedding; extracting global features and local features of the template image and the search image at the same time, and performing feature fusion on the global features and the local features of the template image and the search image; the output of the global and local joint module is input into an asynchronous enhancement module for asynchronous interaction and feature enhancement; and inputting the finally enhanced output feature to a prediction head to obtain a tracking result. According to the method, global and local features are extracted at the same time through the feature combination module, and the features after interaction are enhanced by using asynchronous enhancement operation in the feature fusion stage, so that the robustness and the feature expression ability of the model are improved.

The invention discloses a multi-modal target tracking method guided by sparse space-time markers, which performs joint feature extraction, modal fusion and template search matching through a network model, and introduces sparse space-time markers at multiple levels to embed target appearance change information to complete accurate target tracking. In the first stage, search area images and template images of two modalities are cut according to a target frame of an initial frame, then the template search images are coded and projected to a sequence feature space, templates and search sequence features of the two modalities are spliced together, and the spliced templates and search sequence features are sent to a stacked sequence feature coding layer; in the second stage, sparse space-time marks from historical frames are introduced into a preset coding layer, and cross attention is carried out on the sparse space-time marks and search areas of the modes of the sparse space-time marks so as to embed target change information; search mark features of two modals extracted through multilayer coding are spliced together and sent to a tracking prediction head to obtain a response point coordinate with the highest score and a value of a regression frame, and finally a tracking result coordinate and a bounding box of an actual target are obtained through relative transformation.

The invention discloses a knowledge graph multi-hop retrieval method fusing semantic analysis and graph embedding, provides an innovative fusion strategy, and aims to improve the efficiency and accuracy of knowledge graph multi-hop questions and answers by integrating semantic analysis and graph embedding technologies, and effective multi-hop questions and answers should be a knowledge graph multi-hop retrieval method based on question semantic features. In the process of searching an entity closest to the meaning of a question in a graph embedding space in a relational semantic path, relational semantics is used for guiding path exploration, the number of jumps is dynamically determined according to semantic content of a question, probability evaluation between the question and the entity in the graph embedding space is introduced, so that the correlation and reliability of candidate answers are enhanced, and the probability evaluation efficiency is improved. The specially designed loss function gives consideration to the exploration efficiency of the semantic path and the accuracy of the answer, thereby promoting the model to continuously optimize the reasoning ability in the training process.

The invention provides a target tracking method and system based on a Mama visual hybrid module, and the method comprises the steps: inputting a search image and a template image into the Mama visual hybrid module, and extracting and superposing global context information and local detail information; inputting the output of the Mama visual mixing module into a feature fusion module for fusion; performing feature enhancement on the output of the feature fusion module by using a feature enhancement module to obtain enhanced features; pre-training the optimized tracking model by using a large-scale data set, and updating parameters of the tracking model; and inputting the search image and the template image into the parameter-updated tracking model to obtain a tracking result. According to the method, a feature aggregation network is designed, historical state information of a search area is integrated by using an image state space module, the historical state information and interacted features are spliced along a channel direction, and effective aggregation of the features is realized through convolution, activation, normalization and jump connection to filter irrelevant information.

The invention discloses a data search processing method and a host. A host processor obtains a query vector and a centroid of each to-be-searched graph; the to-be-searched graph is constructed for the query scene, so that the number of candidate vectors of the corresponding to-be-searched graph is determined according to the query vector and the centroid; a query request is sent to the hardware accelerator, the query request comprises query vectors and the number of candidate vectors, the hardware accelerator distributes the query request to different search units, and candidate vector search is conducted on the corresponding graphs to be searched in the external storage through the different search units.

A search data processing method includes: obtaining a media search graph including a query node, a media node, and an association node; obtaining first training sample pairs, including a positive node pair and a negative node pair, from the media search graph, sampling the media search graph based on meta-paths respectively corresponding to the query and the media data, to obtain sampling sub-graphs; inputting the sampling sub-graphs to an initial graph neural network to obtain respective initial semantic features and form a semantic feature pair corresponding to the first training sample pair; and training the initial graph neural network based on a difference between semantic feature pairs of the positive node pair and the negative node pair, to obtain a target graph neural network that is configured to determine a target semantic feature corresponding to a query node or a media node.

Disclosed are a method and an electronic device for image search. The method includes: obtaining a search string provided to search for images; determining whether the search string matches one of the classification tags, and while the search string matches one of the classification tags, to-be-searched images corresponding to the one of the classification tags are presented in a user interface; while the search string does not match the classification tags, obtaining a second comparison vector according to the search string based on a multi-modal artificial intelligence (AI) model, determining a correlation degree between the second comparison vector and the first comparison vector corresponding to the to-be-searched images to generate a search result; and, identifying whether the search string has a second location information, so that the search results generated based on the search results and the second location information are presented in the user interface.

The invention provides a target tracking method and system based on multi-kernel convolution and grouping residual self-attention, and the method comprises the steps: constructing a feature fusion network which comprises a multi-kernel convolution module and a grouping residual self-attention module under a twin network framework, and extracting the features of a template image and a search image, the template features are used as static tokens to be connected with search features in series, fusion is carried out through a self-attention mechanism, and a fusion feature sequence is formed; calculating a maximum similarity score by using a multi-head self-attention encoder to generate a center score graph, and planarizing a search region in the fusion feature sequence into a 2D feature graph; and inputting the data into the head network for processing to obtain a tracking result. According to the method, the advantages of deep convolution and a self-attention mechanism are absorbed, the global modeling capability is achieved, meanwhile, the capability of fully learning local details is achieved, feature fusion is conducted through an asynchronous structure, and the robustness of the model is enhanced.

The invention discloses a visual target tracking method based on natural language and target state information. The method comprises the following steps: (1) constructing a training sample set; (2) constructing a visual target tracking model based on a natural language and target state information; step (3), adjusting parameters of an image-text encoder and loading a pre-training weight to obtain a feature after the text and the first template are fused, a feature of the second template and a feature of the search image; (4) fusing the position information of the target in the sample set and the bounding box information of the target into the features of the second template; step (5), obtaining features after joint modeling; step (6), acquiring a token containing target position information after query; (7) obtaining a predicted target bounding box regression result; and (8) obtaining a final tracking result. According to the invention, the tracking accuracy of the visual tracker based on the natural language is effectively improved.

The invention discloses a network fault prediction method, and relates to the technical field of fault prediction. The method comprises the following steps: constructing a cross-level semantic topology model; screening a key link by using a node importance algorithm and extracting a structural fingerprint vector; calculating the deviation degree between the current link and the structure fingerprint vector, and judging that the structure is abnormal if the deviation degree is smaller than a threshold value; constructing an anomaly propagation rule base according to historical faults and operation and maintenance data; when a plurality of structure anomalies appear at the same time, the conflict resolution algorithm determines an anomaly dominant factor; potential signals are collected according to the abnormal dominant factors, weights are given according to a rule base, and weighting is carried out to obtain comprehensive potential signals; based on this, a fault propagation path is constructed, and a path weight is calculated by using a Pearch ranking algorithm; a fault chain is rehearsed through a depth-first search graph traversal algorithm; and verifying the rehearsal chain by using a preset test statistic circumstantial evidence to obtain a fault prediction result. According to the invention, on the basis of the fault prediction result, cross-level network fault prediction in network propagation is realized.

Embodiments described herein provide systems and methods for text generation using a natural language processing model. Natural language input is received and encoded by an encoder. A decoder generates a plurality of next-node probabilities for a set of candidate nodes that are next to at least one of K previously decoded paths of nodes on a directed search graph. Scores are computed for each of the candidate nodes and done in parallel among the K previously decoded paths. The highest scoring K nodes are selected for addition to the directed search graph. The search continues until a search budget is exhausted. Generated sequences are output. These sequences may represent a plurality of sentences or questions, depending on the task.

The invention provides a target tracking method and system based on a double-domain attention and wavelet enhancement module, and the method comprises the steps: carrying out the preprocessing of a template image and a search image, and inputting a parameter adjustment feature extraction network, respectively extracting features of the template image and the search image in a spatial domain and a channel domain through double-domain attention of a feature extraction network; splicing the template image sequence features and the search image sequence features along channel dimensions, adjusting a self-attention module and a wavelet enhancement module of a feature fusion network by using parameters after splicing, and performing interactive fusion and feature enhancement; and sending the fusion feature into a prediction head to obtain a tracking result. According to the method, the spatial domain features and the channel domain features of the input image sequence are extracted through the double-domain attention, so that the model not only can pay attention to the spatial position of a target, but also can model the importance of channel semantics, double-domain complementary modeling is realized, a target region is positioned more accurately, and target features and noise channels are effectively distinguished.

Disclosed is a network and method for creating and searching a graph in a massively parallel manner. The network includes a plurality of graph storage instances that collectively store a graph formed from a plurality of entities and comprising a vertex for each entity, edges connecting pairs of vertices, and adjacency relations. Each graph storage instance stores a subgraph of the graph and comprises a partition with a non-overlapping vertex set of all the vertices in the respective subgraph, an edge set of all the edges between vertices in the respective vertex set, and the adjacency relations for the edge set and to any edges outside the partition, to which the edge set connects. Each graph storage instance also includes an executor for executing one or more of the actions over the respective subgraph, and an action monitor for supplying actions to the respective executor.

1. The name of the design product: content search module of content display dynamic graphical user interface of electronic device. 2. The use of the design product: an electronic device. 3. The design points of the design product: the claimed part of the graphical user interface. 4. The picture or photo that best shows the design points: the front view. 5. The use of the graphical user interface: for displaying content; the use of the claimed part is to search for content. 6. The human-computer interaction mode of the graphical user interface: in the content search mode, in the order from the front view to the change state view 3, as the user taps, clicks or touches a certain area in the oval search bar at the top of the interface, the content window below the search bar gradually shrinks and blurs, and at the same time the search bar itself moves to the center of the interface and gradually enlarges. 7. Other circumstances that need to be explained: the part depicted by the solid line in the view is the claimed part of the design.

1. Name of the product in this design: Intelligent Question-Answering Search Graphical User Interface for Electronic Devices. 2. Intended use of this design: for use in an electronic device. 3. The key design feature of this product is its graphical user interface. 4. The picture or photo that best illustrates the key design points: Design 1 front view. 5. Design 1 is designated as the basic design. 6. Uses of the graphical user interface: for AI teaching assistant intelligent question answering and search interaction. 7. Description of the changing states of the graphical user interface: In the main view of Design 1, click on any covered area below "Referencing XX materials as references" in the middle of the interface to enter the changing state diagram of Design 1 and view the video content; the interaction method of Design 2 is the same as that of Design 1, so the description is omitted. 8. Other situations requiring explanation: Other explanation: The covered area is the content screen; X represents text content.

The large-scale image search method based on deep hash comprises the following steps: acquiring a query image uploaded by a user and preprocessing the query image to obtain a to-be-searched image; the to-be-searched image is input into a pre-trained image Hash coding model, the image Hash coding model comprises a feature extraction network and a Hash mapping network, and the feature extraction network is constructed based on Vision Transform and is used for carrying out feature extraction on the to-be-searched image to obtain image high-dimensional features; the Hash mapping network is used for performing Hash coding on the image high-dimensional features to obtain search Hash codes; calculating the similarity between the search hash code and the in-library hash code of the inventory image in the image library based on the Hamming distance; and screening the inventory images based on the similarity to obtain a target image and feeding back the target image to the user. The invention provides a deep hash-based large-scale image search method, an image recommendation method, a recommendation system and computer equipment, which have better search performance.

1. The name of the design product: the search for housing part of the house source graphical user interface of the electronic device. 2. The use of the design product: an electronic device. 3. The design points of the design product: in the local part of the graphical user interface. 4. The picture or photo that best indicates the design points: design 1 front view. 5. Design 1 is designated as the basic design. 6. The use of the graphical user interface: used as a house source search graphical user interface as a whole, and the search for housing part is used to search for house sources and display maps and house source related information. The user inputs the housing search information in the search box of design 1-design 4 front view, and the map and list below display the corresponding house source search results and related information according to the search information, such as displaying the search location, nearby communities and the corresponding number of house sources in the map. The user inputs the housing search information in the search box of design 5 front view, and the map and list below display the corresponding house source search results and related information according to the search information, such as displaying the search route and the corresponding number of house sources in the map. The housing search information input by the user in the search box can be a specific location, house source characteristics (such as business district, urban area, price), subway, etc. 7. The present design is a partial design, wherein the blue coated part is not claimed, and the map content displayed in design 1 to design 5 views does not belong to the claimed picture.

The invention relates to the technical field of motion trail tracking, in particular to an object motion trail tracking method and system based on visual features. The method comprises the steps of collecting continuous target object search image information and target object template image information based on an object motion video stream, and performing information interaction processing on template features and search features; obtaining a to-be-selected predicted target object bounding box based on the time sequence features and the search features subjected to information interaction processing, judging whether the to-be-selected predicted target object bounding box meets the selection accuracy requirement or not based on the average coverage of the to-be-selected predicted target object bounding box, and extracting three-dimensional point cloud data of a target object in the predicted target object bounding box. Inverse density sampling processing is carried out on the three-dimensional point cloud data to obtain a to-be-processed point set, and an object motion trajectory vector is generated based on the to-be-processed point set, so that the precision and robustness of object motion trajectory tracking can be improved.

The application discloses a target tracking method and device, electronic equipment and storage medium, comprising: acquiring a to-be-detected video sequence and determining an example image and a search image; inputting the example image and the search image into a trained feature extraction network for processing to obtain a first feature map set and a second feature map set; inputting the first feature map set and the second feature map set into a trained twin mutual attention module for processing to obtain a reinforced second feature map set; obtaining regression features according to the first feature map set and the reinforced second feature map set; performing anchor frame regression according to the regression features to obtain the position of a to-be-detected target in the search image, and further determining the position of the to-be-detected target in the to-be-detected video sequence. The application strengthens the features through the twin mutual attention module, applies the information of the to-be-detected target to the extracted features, makes the adaptability of the reinforced second feature map set stronger, and realizes robust, efficient and accurate target tracking.

The present invention relates to a method and storage medium for searching within an APP site, wherein the background server of the method sends the image to be searched and the corresponding page configuration information to an image comparison engine, so that the image comparison engine can obtain the corresponding long screenshot image of the page and the offset data of each business processing area in the long screenshot image according to the page configuration information, thereby calculating the first candidate image with the highest similarity to the image to be searched, and calculating the final moving distance based on the current offset distance of the first candidate image and sending it to the application end, so that the application end can automatically jump to the corresponding page and move to the corresponding business processing area according to the final moving distance and the page configuration information. Therefore, the present invention can not only automatically jump to the page corresponding to the image to be searched, but also automatically move to the business processing area of ​​the page, thereby improving the efficiency of searching within the APP site while simplifying user operations and optimizing user experience.

The application discloses a public transport line generation method and system based on road congestion avoidance, comprising the following steps: constructing a public transport search graph; obtaining a pickup point input by a user, obtaining a congestion index of the input pickup point, a first-order neighbor of the input pickup point or a second-order neighbor of the input pickup point based on the public transport search graph, and generating a recommended pickup point according to the congestion index; obtaining a starting point and a terminal point input by the user, constructing a candidate line set based on the public transport search graph, screening the candidate line set, and generating a recommended line. The application can provide a road congestion avoidance travel line for the user, save the travel time of the user, intelligently guide the user to go to a suitable station to take a bus during a peak period, enhance the willingness of the user to use public transport for travel, and realize green travel.

The system obtains emails sent to a user, an email among the emails, and a sender of the email and determines whether the sender provided an image representing the sender. Upon determining that the sender did not provide the image, the system obtains the image representing the sender. The system identifies a subset of emails, where each email in the subset of emails is from the sender. The system presents in an email interface the subset of emails along with the image representing the sender. The system obtains an indication from the user to change the image, and in response obtains images representing the sender by searching a database of images. The system presents the images to the user and obtains a selected image from the user. The system replaces in the email interface the image representing the sender with the selected image to obtain an updated email interface.

The present application discloses an object tracking method, device, equipment and medium based on pseudo multi-modal retrieval, which relates to the technical field of image processing. The method includes obtaining a search image and a template image; inputting the search image and the template image into an object tracking model, and outputting an object tracking result. Among them, the object tracking model is used to: respectively extract features from the search image and the template image to obtain search features and template features; map the template features to the codewords in the codebook, and the mapped codewords constitute the quantized template features; the codebook is constructed by using a vector quantization variational autoencoder; filter the search features through a progressive dynamic visual feature selection method; perform pseudo multi-modal retrieval and object prediction on the quantized template features and the filtered search features to obtain the object tracking result. The present application improves the accuracy and efficiency of object tracking.

1. The name of the design product: search box of browser graphical user interface for electronic equipment. 2. The use of the design product: for an electronic equipment. 3. The design points of the design product: the interface content expressed by non-dotted line in the graphical user interface. 4. The picture or photo that best shows the design points: design 1 front view. 5. Design 1 is designated as the basic design. 6. The use of the graphical user interface: the whole of the graphical user interface of the design is used for browser, and the part claimed is used for information search. In each design, the user can select the search icon in the interface shown in the front view by clicking or other methods to open the search box. The interface after opening the search box is shown in the change state diagram. The user can input keywords in the search box to search information. The position of the search icon includes but is not limited to the layout shown in the view. 7. Other circumstances that need to be explained: "X" in the interface represents a text, number or symbol area. The design claimed does not include the interface content and interface border expressed by dotted line in each design.

The present invention relates to the technical field of trajectory planning, and particularly to a heterogeneous UAV cluster collaborative search optimization method and system based on multi-situation map fusion. By constructing a target probability map for reflecting the probability of a UAV detecting a target in an environmental grid, a digital pheromone map for reflecting the search time interval of the UAV cluster for the environmental grid in the mission area, and an environmental search map for reflecting the search coverage of the UAV cluster for the environmental grid; according to a preset weight value and by combining the benefits of the target probability map, the digital pheromone map, and the environmental search, an optimization objective function for the collaborative search of the UAV cluster is generated, so as to use the optimization objective function for the collaborative search of the UAV cluster to obtain the optimal decision of the UAV cluster under the constraint conditions. The present invention improves the objective function in the collaborative search process of the heterogeneous UAV cluster, facilitating better obtaining of the optimal decision that maximizes the overall performance index under the predetermined constraint conditions, so as to improve the collaborative efficiency and task execution efficiency of the UAV cluster.

1. The name of the design product: AI helper question and answer graphical user interface for electronic equipment. 2. The use of the design product: an electronic device. 3. The design points of the design product: the graphical user interface content displayed on the screen. 4. The picture or photo that best indicates the design points: front view. 5. No design points, omit rear view, left view, right view, top view, bottom view. 6. The use of the graphical user interface: the figure is used for the user to enter the management page of the AI helper question and answer; the front view displays the homepage of the management page of the main webpage; in the front view, click the search pattern on the right side of the "unified search" field to enter the interface change state figure 1; in the interface change state figure 1, enter any question in the search field and click search to enter the interface change state figure 2; in the interface change state figure 2, click the pattern of the AI helper on the right side of the search field to enter the interface change state figure 3; in the question field below the interface change state figure 3, enter any question and send it to enter the interface change state figure 4; in the interface change state figure 4, click the "history conversation" button on the right side to enter the interface change state figure 5.

A cybersecurity service (“service”) obtains alerts indicating security policy violations for assets in a computing environment and the corresponding issue category(ies) associated with each violation. For each alert, the service determines which asset in the computing environment to target for remediation of the associated violation by searching a graph representation of the computing environment based on the affected asset's type and / or the issue category. The service identifies a target asset and other assets related to the affected asset and the target asset as a result of searching the graph and generates a remediation plan indicating actions to take on the target asset to remediate the security policy violation for the affected asset based on the target asset type and the corresponding issue category. The service indicates the remediation plan, the related assets identified due to the graph traversal, and their corresponding security policy violations within the same issue category.