154 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 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.

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 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 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.

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.

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.

1. The name of the design product: electronic device operation and maintenance monitoring management graphical user interface. 2. The use of the design product: the design product is used for an electronic device. 3. The design points of the design product: the graphical user interface. 4. The picture or photo that best indicates the design points: design 1 front view. 5. The carrier of the design product is the usual design, and other views are omitted. 6. Design 1 is designated as the basic design. 7. The use of the graphical user interface: the interface is mainly used for the interactive interface of operation and maintenance monitoring management, and mainly presents the ping test relationship between services through horizontal and vertical coordinates. Clicking on a "ping test unit (rectangle)" in the middle of the design 1 front view interface, the interface changes to design 1 interface change state diagram 1. Clicking on a "details" function key in the pop-up window of design 1 interface change state diagram 1, the interface changes to design 1 interface change state diagram 2. Entering the same information in the search box in the middle of the design 2 front view interface and clicking the search icon to the right of the search box, the interface changes to design 2 interface change state diagram.

To provide a video desired by a user to the user even if the user cannot verbalize a target to be searched.SOLUTION: An information processing apparatus includes: a receiving unit which receives a search text input by a user for searching for a video; a generation unit which inputs the search text to an image generation model, which is a machine learning model configured to generate, from a text, an image corresponding to the text, to generate a search image corresponding to the search text; and a video search unit which searches for a video corresponding to the search image and the search text based on the search image and the search text.SELECTED DRAWING: Figure 6

The present disclosure provides a kind of adaptive twin network target tracking method, device, medium and equipment, it is related to target tracking technical field, the method comprises: first, obtain the image group to be tracked, secondly, search image and the template image including preset target object are enhanced processing;Then, using the twin network feature extraction module in the target tracking model trained, the template image and search image after enhancement are carried out feature extraction, and the similarity between target object feature vector and search image feature vector is calculated, to determine whether the target object to be tracked exists in search image;If the target is not found, the adjacent image will be used as search image to repeat the above processing steps, until successfully find the target object to be tracked, and the position information is determined using anchor frameless module, to determine the predicted frame position of the target object to be tracked in search image.The present disclosure embodiment improves the accuracy and real-time performance of target tracking.

1. Name of the product in this design: Electronic device with graphical user interface for deploying smart campus map beacons. 2. Intended use of this design: for use in an electronic device. 3. The key design features of this product are its graphical user interface. 4. The image or photograph that best illustrates the design's key points: the front view. 5. Electronic devices are designed in a conventional way, so the rear view, left and right views and the top and bottom views are omitted. 6. Purpose of the graphical user interface: It is used by park managers to manage maps and bind beacons via mobile devices. 7. Human-computer interaction method of graphical user interface: The main view is the initial login interface. After selecting any item in the list in the main view, it jumps to the interface change state diagram 1; after selecting any building in the list in the interface change state diagram 1, it jumps to the interface change state diagram 2, which displays the floor map of that building. Different floor maps can be switched. The map can be zoomed in and out using gestures. The device icon is displayed on the map; after clicking the device icon (i.e., the teardrop icon) on the map in the interface change state diagram 2, it jumps to the interface change state diagram 3, where beacon information can be popped up and modified or deleted; after clicking the add beacon button "⊕", clicking on a map area will show a gray icon, as shown in the interface change state diagram 4. You can click on other map areas again, and the gray icon will follow until you confirm and the beacon position will be fixed, completing the addition; clicking the search icon in the lower right corner is shown in the interface change state diagram 5.

The application relates to a Siamese network video single-target tracking method based on feature fusion, and particularly relates to a Siamese network monitoring video single-target tracking method based on feature fusion. In order to solve the problems that the Siamese network single-target tracking algorithm has low tracking capability when facing complex environments and obvious background interference near a tracked target, cannot accurately track the target, and the output tracking area is not accurate when tracking some specific targets, a model is trained by using a template region image set and a search region image set, feature maps of the template image and the search image are respectively output, the model sequentially comprises a ResNet-50 network based on a mixed attention mechanism and a twin feature fusion network, the feature map of the template image and the feature map of the search image are input into an RPN network for similarity comparison, a prediction region with the highest similarity to the template image in the search image is output, and single-target tracking is realized. The application belongs to the field of target tracking.

The present disclosure relates to selectively analyzing digital content items from a social networking system to generate a searchable graph object that facilitates visualization of correlations between entities of interest (e.g., brands, products, services, companies), concepts (e.g., domain-specific and / or general terms), hashtags, and other output classes. For example, systems described herein involve collecting images and / or videos that are publicly available via a social networking platform and evaluating content and metadata thereof to identify entities of interest therein and predict various concepts therein to generate a graph object that provides a searchable matrix. The systems herein provide a mechanism for processing a graph query that presents a relational graph showing correlations between the query and the various output classes.

This enables the automatic search for alternative products that are "similar in appearance" and "cheaper." [Solution] When the control unit receives a command to search for a substitute for the target object, it acquires information about the target object. The information about the target object includes an image of the target object and the price of the target object. Based on the information about the target object, the control unit searches for a substitute object whose image is similar to the image of the target object and whose price is less than or equal to the price of the target object. The control unit outputs the search results.

The invention provides a target tracking method and system based on multi-stage feature fusion and residual enhancement, and the method comprises the steps: extracting features of a template and a search image, and adding a position code, so as to obtain a template image feature and a search image feature; the search features are enhanced through a double-stage enhancement module, and a confidence score is generated; thirdly, utilizing a learnable sparse modulation and dynamic gating bottleneck module to carry out further screening and differential enhancement on the features; then, training the model by using a large-scale data set, and optimizing parameters through joint classification and IoU loss; and finally, splicing the enhanced search features with the template features, interacting through an encoder, and outputting a tracking result by a prediction head. According to the method, through multi-stage enhancement, residual fusion and a dynamic adjustment mechanism, difficult scenes such as target shielding, complex backgrounds, scale change and rapid motion can be effectively handled, and the robustness and accuracy of target tracking are improved.

The present application relates to a kind of target tracking methods based on cross-modal feature fusion and refining of Transformer, including obtaining template image pair and search image pair to given RGB and infrared video sequence;The two-way target tracking network comprising asymmetric periodic insertion token adaptive fusion and refining module is constructed;Template image pair and search image pair are respectively input into two-way target tracking network, cross-modal feature fusion and refining are carried out by token adaptive fusion and refining module, and the feature matrix after fusion is sent into prediction head, and finally the position information of the tracking target is output.The present application solves the problem that cross-modal feature fusion is insufficient, redundant calculation and the feature of template and search area is not differentiated in the existing RGBT tracker, by introducing an asymmetric applied token adaptive fusion and refining module, while effectively fusing multi-modal information, the calculation efficiency and the robustness of tracking are improved.

The present invention relates to the field of computer vision technology, and particularly to a target tracking method combining a hierarchical encoder and a parallel attention mechanism. The method comprises the following steps: preprocessing a video sequence to obtain an initial template image and an initial search image; extracting features from the initial template image and the initial search image using an improved feature extraction backbone network VGG16 to obtain template features and search features; processing the template features and the search features through a parallel attention mechanism to obtain two features; fusing the obtained two features and inputting them into the last two convolution blocks of the improved feature extraction backbone network VGG16 and a DWConv‑Transformer encoder for processing to obtain final template features and final search features; performing a cross-correlation convolution on the final template features after processing through the parallel attention mechanism and the final search features to obtain a similarity score map of the two, and mapping the coordinates of the maximum point in the similarity score map back to the original image to obtain a tracking result.

Embodiments of the present application disclose a traffic monitoring service processing method and an electronic device. The method comprises: acquiring a target image and related identification information, wherein the related identification information comprises identification information of a target commodity and / or a first user; generating an information code according to the related identification information and adding the information code to the target image, so as to publish content in a content publishing system by using the image to which the information code is added; after receiving a search request initiated by a second user in a commodity information service system by means of image search, judging whether there is a valid information code in the input search image; if there is, adding a traffic source identification related to a content publisher recommendation to the current search behavior, so as to parse the related identification information according to the information code and provide traffic monitoring information for the first user. Through the embodiments of the present application, a service of monitoring an effect of a "talent recommendation" operation strategy can be provided for a merchant user.

The image of the present design is an image for searching processing procedures for wafers, etc. For example, as shown in Reference Image Figure 2, by operating the search button (7), a search is performed based on the search criteria selected in association with the search icon (3) indicating the search item, and the search results are displayed in the search result display field (6) and the search result count field (11). The search criteria may be, for example, the type of wafer chamber, the ratio of etching rates for two materials, the etching rate, or processing procedure parameters. To add search criteria for a search item, for example, click icon (4A) corresponding to the search icon (3) indicating the search item to display search option block (1), select search option (9), and click add search criteria button (8). Click icon (4B) to close search option block (1). To delete search criteria for a search item, for example, click icon (2). In search option block (1), for example, select the minimum and maximum search option values ​​by clicking slider bar (5).