66 results about "Object label" patented technology

An example apparatus disclosed includes interface circuitry, machine readable instructions, and programmable circuitry to at least one of execute or instantiate the machine readable instructions to identify a first source of object label representation and a second source of object label representation, the first source or the second source including an estimated label prototype vector associated with an input text-based object query, determine a first contextualized embedding for the first source and a second contextualized embedding for the second source, and combine the first contextualized embedding and the second contextualized embedding to generate a candidate object representation associated with the input text-based object query.

The invention discloses a timbre object separation method and device for audio data. The method comprises the following steps: acquiring original video and audio data; if the subtitle data is received, cutting the original video and audio data according to the timestamp of the subtitle data to obtain a plurality of audio coarse slice segments; respectively extracting at least one audio fine slice segment from each audio coarse slice segment by adopting a sliding window with a preset size; extracting audio features from each audio fine slice segment and clustering the audio features, and determining an object tag corresponding to each audio fine slice segment; according to the invention, each object label is backfilled to the audio coarse slice segment, and the tone object corresponding to each audio coarse slice segment is determined, so that different tones are effectively separated in a plurality of main speaker scenes through a mode of determining object label backfilling in audio coarse cutting and fine cutting processes, the requirements in complex scenes are met, and the audio separation accuracy is improved.

The application relates to an object representation model training method, an object representation method, an object representation device, computer equipment, a storage medium and a computer program product. The method comprises the following steps: inputting a first training image set into an initial object representation model to obtain object association features corresponding to training images, object features and object fusion features; obtaining an object association classification loss based on the object association features corresponding to the same training image and an object association label, obtaining an object classification loss based on the object features corresponding to the same training image and an object label, and obtaining a fusion loss based on each object fusion feature corresponding to the same training image group; adjusting model parameters of the initial object representation model based on various losses to obtain an intermediate object representation model; performing model iterative training on the intermediate object representation model as the initial object representation model until a first convergence condition is met, and obtaining a target object representation model. The method can improve the representation accuracy of features for objects.

Provided are a method and an apparatus for auto segmentation using a bounding box. A method for auto segmentation using a bounding box according to one embodiment of the present disclosure comprises receiving a first object image including an object labeled with a bounding box, which is a pre-learning target, learning a segmentation model by classifying an object and a background from the bounding box of the received first object image, and segmenting an object from a second object image, which is an identification target, using the learned segmentation model.

The application provides an object recognition method, device and equipment and a storage medium. The method comprises: obtaining target data; identifying an object to which the target data belongs based on a target recognition model; the target recognition model uses a first training sample of a newly added object labeled with an object identifier, so that the object identifier predicted for the first training sample is consistent with the object identifier labeled for the first training sample, and the probability distribution predicted for the first training sample approximates the probability distribution predicted for the first training sample by a basic recognition model; the basic recognition model is trained by using a second training sample of a historical object labeled with an object identifier; and an initial target recognition model is a model capable of recognizing data of the historical object. The application can train the target recognition model capable of accurately identifying the object to which the data of the newly added object belongs and accurately identifying the object to which the data of the historical object belongs by using only the training data of the newly added object.

The invention provides a game interaction control method and device and electronic equipment. The method comprises the following steps: in response to entering an object selection state, selecting a target object from virtual objects based on an aiming position of a front sight identifier; displaying object tags of at least part of the target objects on a graphical user interface; in response to a trigger operation for the object tag, setting an object state of a target object corresponding to the trigger operation; and in response to the ending of the object selection state, performing collection and settlement on at least part of the target objects based on the object states of the target objects. According to the mode, a decision space is provided in a rapid collection process, wrong collection is avoided, and game operation experience is improved.

The embodiment of the invention discloses a storage packaging operation identification method and device, electronic equipment and a storage medium, and relates to the field of storage packaging, and the method comprises the steps: collecting a to-be-identified packaging frame; detecting the to-be-identified packaging frame by using a pre-trained object detection model in the storage packaging operation identification model, and determining a target object label set and a target object bounding box set in the to-be-identified packaging frame; screening out a candidate operation description set from a pre-constructed standard operation description knowledge base according to the target object label set; and performing alignment processing on the target object bounding box set and the candidate operation description set by using a comparison language image pre-training model in a storage packaging operation identification model to obtain an identification result of the packaging operation in the to-be-identified packaging frame. By adopting the technical scheme of the embodiment of the invention, the recognition accuracy of operations with subtle differences in similar environments can be improved.

Provided are a method and an apparatus for auto segmentation using a bounding box. A method for auto segmentation using a bounding box according to one embodiment of the present disclosure comprises receiving a first object image including an object labeled with a bounding box, which is a pre-learning target, learning a segmentation model by classifying an object and a background from the bounding box of the received first object image, and segmenting an object from a second object image, which is an identification target, using the learned segmentation model.

The application provides a data sticking method, device, equipment and program product; the method comprises the following steps: in response to a data sticking request of a target object routed by a data sticking node, obtaining an object identifier of the target object, an object label of the target object, a first sticking data list corresponding to the object label, and a bitmap of each first to-be-stuck data in the first sticking data list; determining a second sticking data list from the first sticking data list according to the bitmap and the object identifier; the second sticking data list comprises a plurality of second to-be-stuck data; determining a sticking score of each second to-be-stuck data; determining target sticking data from the plurality of second to-be-stuck data according to the sticking score; and sending the target sticking data to the data sticking node. Through the application, the accuracy of the target sticking data can be improved while reducing the cost of data sticking.

This invention provides an image restoration method, apparatus, and electronic device, applicable to the field of image processing technology. The method includes: determining the location and object labels of different objects in an image to be restored; using a pre-set image restoration method for each object label, restoring the image regions corresponding to the locations of each object label in the image to be restored, thereby obtaining a restored image; wherein, the location corresponding to each object label is the location of the object to which that label belongs in the image to be restored. This solution can improve the restoration quality of image restoration.

The application relates to an attribute classification method and device, computer equipment, a storage medium and a computer program product. The attribute classification method comprises the following steps: obtaining historical data of a target object; calculating a first attribute value corresponding to the target object according to a preset interactive object label and historical information; calculating a second attribute value corresponding to the target object according to a referral identity label carried by the target object and a pre-stored label list; and determining a target classification attribute corresponding to the target object according to the first attribute value and the second attribute value. Through the arrangement, the attribute division of the target object can be more accurate, and the target classification attribute obtained finally can be more consistent with the actual attribute of the target object.

Embodiments of the present application disclose an object label generation method and device and a computer readable storage medium. After obtaining historical operation information of at least one target object in a preset time period, the embodiments of the present application filter target historical operation information corresponding to a target content of a preset content knowledge graph from the historical operation information. Then, based on the target historical operation information and the preset content knowledge graph, the historical object label of the target object is counted. Then, according to the target content and the target historical operation information, at least one predicted object label of the target object is determined from an object label set corresponding to the preset content knowledge graph. The historical object label and the predicted object label are fused to obtain the object label of the target object. The scheme can improve the accuracy of object label generation. The embodiments of the present application can be applied to various scenes such as cloud technology, artificial intelligence, intelligent transportation, and auxiliary driving.

The application discloses a sample data acquisition method and device, equipment and a storage medium, and belongs to the field of machine learning. In the embodiment of the application, in response to a sample acquisition request, an object identifier of a sample object and a content identifier of sample content recommended for the sample object are acquired, the sample content being a label of the sample object when a training content recommendation model is trained. Based on the object identifier, an object feature of the sample object is acquired, the object feature including an object behavior and an object label. In the case that the sample content is recommended by a target recommendation engine for the sample object, based on the content identifier, a content feature of the sample content is acquired from the target recommendation engine, the content feature being a content feature used by the target recommendation engine when the sample content is recommended for the sample object, thereby improving the consistency of the object feature and the content feature, and avoiding the problem that the content recommendation effect of the trained content recommendation model is poor due to feature crossing.

Embodiments of the present application disclose an object feature construction method and device and a computer readable storage medium, which can be applied to various scenes such as cloud technology, artificial intelligence, intelligent transportation and auxiliary driving. The object label information of a to-be-constructed object in a target system is obtained. The label similarity between object labels is calculated, and the basic object label is determined in the object label information based on the label similarity and the label weight. Semantic propagation is performed in a preset label relationship graph based on the semantic information of the basic object label, and the associated object label and the label association path are obtained. The number of nodes of the label association path is counted, and the label weight of the basic object label corresponding to the associated object label is adjusted according to the number of nodes to obtain the associated label weight. The object feature corresponding to the to-be-constructed object is constructed based on the associated object label and the associated label weight. In this way, the accuracy of object feature construction is improved, and the efficiency of object feature construction is improved.

The invention provides an analysis report generation method and device, electronic equipment and a readable storage medium, and the method comprises the steps: inputting a processed case-related image and / or a case-related video and metadata information into a pre-trained multi-modal large model, so as to enable the multi-modal large model to recognize a target object contained in the case-related image and / or the case-related video; extracting context and scene information based on text information in the case-related image and / or the case-related video, and outputting at least one object tag and description information for the case-related image and / or the case-related video; inputting the at least one object label and the description information into a pre-trained big language model, so that the big language model performs structured processing on the object label and the description information, and correspondingly filling obtained structured data into a report template matched with the selected case type, and outputting a target report corresponding to the case-related image and / or the case-related video. In this way, the comprehensiveness and efficiency of report generation can be improved.

A system and method and for automatically enhancing an input image includes detecting a genre for the input image using a genre identification machine-learning model and identifying one or more objects in the input image using an object identification machine-learning model. The identified genre and objects are then compared to a list of genre and object tags for images in an image library to identify a plurality of genre and object tags that are similar to the identified genre and objects. A list of edits corresponding to each of the identified similar genre and object tags is then to the input image to generate a plurality of enhanced images for the input image. An aesthetic value is measured for the plurality of enhanced images and at least one of the plurality of enhanced images is provided as a recommendation for enhancing the input image, based on the aesthetic value.

The application provides an object processing method, device and equipment in object storage and a storage medium. The method comprises the following steps: receiving an object storage request sent by an object storage client device; the object storage request comprises at least one target object; the target object comprises a target object identifier and a target object label; determining a target processing time and a target processing strategy corresponding to each target object according to a real-time time and each target object label; determining at least one object processing data according to the target object label, the target processing time and the target processing strategy of each target object; storing the object processing data to a metadata storage device, so that an object processing device acquires each object processing data from the metadata storage device according to a preset time period, and processes each target object stored in a data storage device according to a real-time time and each object processing data. The scheme of the application can save hardware resources and process expired data in time.

Embodiments of the present application provide an object recognition method and device, an object recognition model training method and device, equipment and a medium. A sample three-dimensional image, object label thereof, a plurality of sample three-dimensional voxel graphs and an object recognition model to be trained are obtained. The object recognition model comprises a two-dimensional global feature extraction network, a two-dimensional feature processing network, a three-dimensional encoding / decoding network and a multi-dimensional feature fusion network. The sample three-dimensional image and the sample three-dimensional voxel graph are input into the object recognition model to obtain a first object prediction result of the sample three-dimensional image and a second prediction result of the sample three-dimensional voxel graph. The parameters of the object recognition model are adjusted according to the first object prediction result, the second prediction result and the object label. In this way, a trained object recognition model is obtained. Subsequently, the object in a three-dimensional image to be recognized is recognized based on the trained object recognition model, and the accuracy of object recognition is improved.

A method, system and storage medium for object detection are presented. The method includes; creating object representation data based on outputs from a neural network and augmented reality (AR); constructing a semantic map comprising object superpoints; determining a set of object superpoints in the semantic map whose locations are within a certain distance of a new incoming object point; updating the semantic map in response to the new incoming object point; and modifying a probability of a class to which the new incoming object point belongs as identified by the neural network based on the updated semantic map. The semantic map is composed of object superpoints having a list of scores corresponding to detected labels, a list of viewing directions, and a list of sizes. Based on the semantic map, the accuracy of object detection is improved by modifying the probability of an object label or class from the neural network.

A mechanism for processing video data is disclosed. A value of an annotated region (AR) object label index of an i-th AR object index (ar_object_label_idx[ar_object_idx[i]]) syntax element is determined. The ar_object_label_idx[ar_object_idx[i]] syntax element is specified to be in a range of N to M, where N and M are integers and N is less than M. A conversion is performed between a visual media data and a bitstream based on the ar_object_label_idx[ar_object_idx[i]] syntax element.

Methods and systems for model calibration include training an object detection model to generate confidence scores using calibration that is based on confidence, correlation, and matching, with accuracy of a location of bounding boxes being used with accuracy of object labels to keep the confidence scores close to an actual probability of correctness. Object detection is performed on an image using the object detection model to generate a bounding box around an object, a label for the object, and a confidence score. An action is performed responsive to the object and the confidence score.

An image processing method is provided. The method includes obtaining a to-be-processed image comprising a target object, and inputting the to-be-processed image to a convolutional layer of an image processing model, to obtain an initial feature map of the to-be-processed image, wherein the image processing model comprises an encoder and a decoder; inputting the initial feature map to a self-attention mechanism layer of the encoder, and obtaining a target feature map corresponding to the initial feature map according to position information of each feature in the initial feature map and a position relationship between the each feature and other features; and inputting the target feature map to the decoder for processing, to obtain an object segmentation map and an object label of the target object in the to-be-processed image.

Apparatuses, systems, and techniques to generate labels of objects depicted with in an image. In at least one embodiment, one or more object labels are generated in association with an image frame based, at least in part, on one or more prior frames and one or more subsequent frames.

The embodiment of the invention discloses a content analysis method and device, electronic equipment and a storage medium, and the method comprises the steps: calling a first intelligent agent to obtain object information and historical operation information of a target object, and determining an object tag when the target object browses content according to the object information and the historical operation information; calling a second agent to obtain a plurality of content segments corresponding to the to-be-analyzed content and a content text corresponding to the to-be-analyzed content, and determining an abnormal risk level and a content risk description of the to-be-analyzed content according to the plurality of content segments and the content text; calling a third agent to determine an analysis result of the to-be-analyzed content according to the object label, the abnormal risk level and the content risk description, wherein the analysis result is used for indicating an abnormal type and an abnormal reason of the to-be-analyzed content; according to the embodiment of the invention, the exception type and the exception reason of the to-be-analyzed content can be accurately determined without manual intervention.

The present disclosure describes systems, non-transitory computer-readable media, and methods for generating object-specific-preset edits to be later applied to other digital images depicting a same object type or applying a previously generated object-specific-preset edit to an object of the same object type within a target digital image. For example, in some cases, the disclosed systems generate an object-specific-preset edit by determining a region of a particular localized edit in an edited digital image, identifying an edited object corresponding to the localized edit, and storing in a digital-image-editing document an object tag for the edited object and instructions for the localized edit. In certain implementations, the disclosed systems further apply such an object-specific-preset edit to a target object in a target digital image by determining transformed-positioning parameters for a localized edit from the object-specific-preset edit to the target object.

Embodiments of the present disclosure relate to dynamic throttling of workload-based video processing functions using machine learning. Systems and methods are disclosed for dynamically throttling video processing and / or streaming based on workload. Live video is captured from one or more sources (e.g., cameras) and stored. The video is then provided to a video processing engine and a video streaming engine. The video processing engine can perform one or more operations such as object detection, object tracking, and object classification to produce characterization data (e.g., bounding boxes, object tracks, alerts, object labels, object counts, boundary crossings, intersection highlights, etc.). System resource usage and performance of the video processing and streaming are monitored to produce workload data (e.g., metrics). Based on a policy and the workload data, the video streaming and / or processing is dynamically reconfigured by adjusting parameters provided to the video streaming and processing engines.

The application relates to an object annotation information presentation method and device, electronic equipment and a storage medium. The method relates to the field of computer vision. The method comprises the following steps: acquiring a target object in a specified scene, the specified scene being a scene presented at a target position; and presenting annotation information of the target object on an annotation facade of the target object presented on a display interface, the annotation facade being determined from at least two visible facades of the target object according to projection areas of the at least two visible facades presented on the display interface, the visible facade being a facade visible to the target position in an outer facade of the target object. The above method can select an annotation facade according to the projection of a visible facade on a display interface of a specified scene when displaying annotation of a target object in an AR or VR scene, so that a facade with a larger visible area can be dynamically selected to display annotation information in a virtual reality / augmented reality scene, and the display effect of the annotation information is improved.

The embodiment of the invention discloses a control method and device, a sofa and a computer readable storage medium, the method is applied to the sofa, attitude information of an object is determined based on pressure information of a target area, and the target area is an area corresponding to a key point of the object in the sofa; determining first adjustment information based on the attitude information and an object label of the object; and adjusting the sofa based on the first adjustment information. Therefore, the control precision of the sofa can be improved.

The application discloses a construction method, device, medium and equipment of object label, relates to the technical field of Internet, and the method comprises the following steps: acquiring basic attribute information of an object and interaction behavior information of the object, the interaction behavior information comprising interaction behavior sub-information corresponding to each business scene, the interaction behavior sub-information comprising interaction content of the object and behavior description sub-information for the interaction content; determining first object label information of the object based on the interaction behavior sub-information; performing semantic enhancement processing on the first object label information to determine second object label information of the object; performing extension processing on the second object label information based on the basic attribute information and the behavior description sub-information to determine third object label information of the object; and performing alignment processing on the third object label information to obtain fusion object label information in multiple business scenes. The application can improve the accuracy and richness of the object label and can better serve upper-layer applications.

The invention relates to the field of computers, and provides an information pushing method and device, electronic equipment and a storage medium. The method comprises the steps of obtaining bitmaps of a plurality of objects, wherein the bitmaps indicate resources and at least one object label of the objects, wherein interaction operation of the resources is triggered by the objects; according to a target resource limited by a directional configuration condition and at least one target object label, performing logical operation on the bitmaps of the plurality of objects to determine an object set meeting the directional configuration condition; registering a timed task for the object set based on the push time of the target resource; if the timed task reaches the corresponding triggering time, executing the timed task to push the prompt information for the target resource to the object in the object set, and registering the timed task by taking the object set as a unit, so that the number of registered timed tasks is greatly reduced, the workload of timed task maintenance is also reduced, and the user experience is improved. The problem of large resource occupation of the timed task caused by registering the timed task by taking an object as a unit is solved.