172 results about "Relevant feature" patented technology

A method is provided for improving image classification accuracy in few-shot learning scenarios, where only a limited number of training examples are available. The method combines the use of Gabor filters and convolutional neural networks (CNNs) to extract detailed texture and orientation features from images. These features are then enhanced through global average pooling, aggregated into comprehensive feature vectors, and refined using an attention mechanism that identifies and emphasizes the most relevant features for classification. Masks generated from this attention process selectively enhance critical features, which, after optional re-encoding, are used to train a classifier via a metric learning approach. This method aims to increase feature separability and classification performance, facilitating more accurate classification of new images with minimal training data.

According to the movie and television play table book extraction method and device, the storage medium and the computer equipment provided by the invention, after an audio and video file of a movie and television play is split into a video file and an audio file, feature recognition is performed on the video file to obtain a subtitle text, speaker face information and a video understanding text; performing voice understanding on the audio file to obtain a voice transcription text and a voice understanding text; wherein the voice transcription text can be corrected into the standard transcription text with high accuracy through the subtitle text. Therefore, based on the face information of the speaker, the line segment of each speaker in the standard transcriptional text and the audio and video file is aligned, so that speaker information with accurate segmentation and semantic coherence can be obtained; and then, through combination with a character side-writing text generated by side-writing analysis on the speaker based on the video, the voice understanding text and the speaker information, table book information is constructed, and related feature description of the character can be covered on the basis of containing the line content, so that the content and depth of the table book are enriched.

The invention provides a metallurgical furnace working condition intelligent identification method fusing multi-modal data. A double-branch multi-level residual structure feature encoder is designed, and metallurgical furnace process variable data features and image data features are fully extracted. Aggregation and separation among modal features are carried out through a comparative learning method, feature semantic alignment is realized, and feature distribution is optimized. Under the constraint of orthogonal loss, a feature decomposer is utilized to effectively separate out inter-modal correlation features and modal private features, and an information bottleneck theory is utilized to promote redundancy removal of key correlation features of process variable data and image data. A multi-level and multi-dimensional feature fusion module is constructed, single-level feature interaction fusion and inter-level feature fusion are sequentially realized under the action of bidirectional cross attention, and fusion features rich in information content are obtained. And finally, the information is sent to the classifier, so that intelligent and accurate identification of the metallurgical furnace working condition under multi-modal data input is realized.

The invention discloses a positive and negative unmarked image classification method and system based on depth metric learning, and relates to the technical field of sample classification, and the method comprises the steps: obtaining a marked positive sample image and an unmarked image, inputting the marked positive sample image and the unmarked image into a depth metric learning model, adjusting the marked positive sample image in a depth feature space, and obtaining the unmarked image; minimizing the distance between the marked positive sample image and the center of the positive sample image, aligning the unmarked image and the corresponding enhanced view in a representation space through self-consistent metric learning, and outputting related feature representation; using related feature representation to select sample images which are in multiple proportion to the positive sample images as reliable negative samples; and taking the related feature representation as input, using a positive sample image and a reliable negative sample as supervision signals to train a binary classifier, and classifying positive and negative unmarked images to be detected. According to the method, dependence on heuristic negative sample selection is avoided, and high-quality feature representation with discrimination on positive and negative samples can be learned.

The invention relates to a dynamic concept cognitive anomaly detection method and system under an unbalanced condition, and the method comprises the steps: collecting the related feature data of a sample set in a scene facing a specific extreme event, and dividing an initial data set and a test set; constructing an initial learning model; performing dynamic event type prediction based on the test set, and classifying the event as an extreme event or a non-extreme event; constructing an active three-way concept learning module to form a positive concept space and a negative concept space based on a three-way decision idea and prediction probability values of extreme events and non-extreme events; according to the positive concept space and the negative concept space, introducing a concept center domain strategy for dynamic updating; and adopting a loss function minimization strategy to obtain an optimal learning model. The effectiveness of dynamic detection is improved by utilizing an active three-way concept learning method, a concept center field and a concept dynamic optimization strategy, and the capability of identifying extreme events under the extremely unbalanced scene condition is realized.

The invention provides a multi-modal document understanding method and device and electronic equipment, and relates to the technical field of document understanding. The method comprises the following steps: extracting an initial feature map from a document image, and performing down-sampling on the initial feature map to obtain a visual data unit; features of a text area are extracted from the initial feature map, the features of the text area and a text data unit of the document are processed based on a Q-Former structure model, a query vector is obtained, and the query vector is used for extracting features most relevant to the current task from the features of the text area and the text data unit; and inputting the visual data unit, the text data unit and the query vector into a large language model for processing, and understanding the document. The technical problems that an existing multi-mode large language model document understanding technology is poor in visual perception ability and high in computing resource requirement are solved.

The invention relates to a three-dimensional semantic scene completion method and device based on geometric and time sequence modeling hierarchical context alignment, and a medium. The method comprises the following steps: obtaining depth features and context features, sensing a cross attention mechanism through depth confidence, supplementing information of a low depth confidence region by using the context features, and generating related features of a current frame; historical frame related features are obtained from the attitude network, cross-frame feature affinity is calculated, the historical frame related features are dynamically optimized, and multi-layer historical frame aggregation features are obtained; and in the unified space, the depth hypothesis of the time feature voxels is adopted as a distance axis, the volume feature voxels are projected to the unified space, global alignment and combination are performed on the geometric feature voxels and the time feature voxels, and final aggregation features are obtained. Compared with the prior art, by introducing a hierarchical context alignment mechanism based on geometry and time sequence modeling, a complex 3D scene can be more accurately understood, and the method is especially suitable for environmental perception in an automatic driving system.

The invention relates to the technical field of man-machine interaction and voice processing, in particular to a gesture operation voice playing output method based on end-cloud collaboration, which comprises the following steps: S1, collecting gesture motion data of a user in real time; s2, extracting a bone point coordinate change rate, and generating a compression feature sequence; s3, segmenting the compressed feature sequence into a privacy related feature layer and a behavior representation feature layer; s4, dynamically matching the behavior representation feature layer with a preset semantic template; s5, selecting the target semantics with the highest weighted score and the associated voice template; and S6, encoding the voice template into streaming media data, and distributing the streaming media data to local equipment for voice broadcast. According to the method, multi-mode perception, dynamic semantic matching and edge streaming media coding are fused, high precision of gesture recognition and semantic analysis is achieved, meanwhile, user privacy protection and real-time voice interaction are ensured, and system response efficiency and application safety are remarkably improved.

The invention discloses an industrial equipment fault real-time monitoring method and system based on a neural network, and the method comprises the steps: carrying out the segmentation and classification of a monitoring video frame of target industrial equipment, obtaining recognition frames, selecting doubt recognition image blocks which are strongly correlated with other recognition frames, and reference recognition image blocks which are weakly correlated with other recognition frames, and carrying out the recognition of the doubt recognition image blocks. And performing brightness and texture feature analysis and processing on the doubtful recognition image block to enlarge the features of the doubtful recognition image block which do not belong to the equipment device, enhancing the reference image block to enhance the features of the reference image block which belong to the equipment device, then performing fault recognition based on the doubtful fusion feature map and the reference enhancement feature map, and outputting a fault monitoring result. In the scheme, the doubtful image block corresponding to the obstacle is weakened, and meanwhile, the related characteristics of the reference image block belonging to the equipment device are enhanced, so that the influence of obstacle shielding and the like on the fault monitoring result can be effectively avoided, and the fault monitoring accuracy of the industrial equipment is improved. The system also has the above technical effects.

A navigation path can be determined for an object using one or more neural networks. In various embodiments, image data is obtained that is representative of an environment in which the object is to be navigated. Relevant features are identified from the image, and a curve fit to those features. Loss values for the potential paths are scaled based at least in part upon the distance of those features in the real world. This can include, in at least some embodiments, performing the scaling as a function of the curvature of the curve fit to the features. Temporal smoothing can be performed with respect to prior path predictions in order to prevent sudden changes in the predicted path. The paths are analyzed to select a path with a highest confidence value that also at least satisfies a minimum confidence criterion. The path can be converted into three-dimensional navigation information.

The invention discloses an improved PointPill three-dimensional target detection method based on fusion sparse enhancement and geometric attention, and belongs to the field of deep learning and three-dimensional target detection. According to the method, an SP-Point Pill improved framework fusing a sparse Pill enhancement mechanism (SEPA) and a point awareness space attention mechanism (PASA) is provided. A sparse Pilllar enhancement mechanism is fused, and geometric information of sparse Pilllar is efficiently filled through neighborhood real point borrowing and a virtual point interpolation strategy; the point sensing space attention mechanism generates a dynamic weight based on the point cloud density and the average distance, and preferentially enhances target related features; the pedestrian detection method and the pedestrian detection device cooperate with each other to form a link closed loop of information completion and feature focusing, so that the pedestrian detection precision is remarkably improved on a KITTI data set, and meanwhile, the efficient reasoning speed is maintained.

The invention belongs to the technical field of computer vision and intelligent video analysis, and particularly relates to a multi-target tracking method based on trajectory recovery. The method comprises the following steps: firstly, performing target detection on an input video frame to obtain a bounding box, confidence and related feature information of a candidate target; then, a current detection result is matched with a historical track through Kalman filtering and appearance features, and track updating of the first stage is achieved; when an unmatched target exists, the position of the unmatched detection frame is dynamically corrected by calculating the average displacement of the center points of the front and back frame detection target, and whether the distance between the target and the camera is lower than the preset track recovery threshold value is judged by combining the distance information between the target and the camera. And when the conditions are met, the target is directly activated and recovered to be an effective tracking trajectory of the current frame, so that quick re-association of the lost target is realized.

The invention discloses an enterprise risk clue generation method and device, and the method comprises the steps: obtaining related feature data of an enterprise information system, inputting the related feature data into a trained risk clue prediction model, and outputting a risk clue prediction result of an enterprise; explaining the output risk clue prediction result through an SHAP method, and determining the contribution degree; and utilizing a causal forest model to predict the risk clues of the enterprise according to the related feature data and the corresponding contribution degrees, and obtaining an average value of output results of all the causal trees in the causal forest model as a final enterprise risk clue prediction result. Retrieving in an RAG knowledge database to obtain a corresponding text information retrieval result, and generating corresponding prompt word information; and inputting the prompt word information into the large language model to generate an answer to the enterprise risk clue. According to the invention, the accuracy and interpretability of risk clue prediction can be improved.

The invention discloses a depth image classification model evaluation method and system based on background pseudo-correlation measurement, and the method comprises the steps: obtaining a foreground target mask through a pre-training semantic segmentation model, and separating a foreground image; and generating various background images by using a text-to-image generation model in combination with random noise and semantic guidance weight. Then, background controllability constraint is adopted to adjust background change, meanwhile, the foreground is kept unchanged, and foreground and background images are fused to construct a composite image set; and then, inputting the synthesized images into a to-be-evaluated model, calculating category prediction probability difference, semantic representation of a feature extraction layer and an uncertainty metric value, and finally obtaining a correlation analysis error value to evaluate the dependency degree of the deep learning classification model on the pseudo-correlation features. Through combination of semantic segmentation, text-to-image generation and background controllability constraint technologies, quantification of the background pseudo-correlation dependency degree of the deep learning classification model is realized.

The invention relates to a food multi-modal detection data fusion analysis method and device, equipment and a medium. The method comprises the following steps: acquiring original multi-source heterogeneous data of a food spectrum, an image and a smell, and performing normalization and noise reduction preprocessing to obtain a standardized feature set; classifying and screening high-correlation features by using a support vector machine, and generating a structured description; dynamic parameters are extracted, a quality trend vector is generated through time sequence analysis, a safety score is calculated, and a qualified mark is output if the safety score reaches the standard; constructing an extension index set based on the score, inputting a dynamic model to generate an authentication update link, and combining the block chain time to obtain new-version authentication; and associating the historical records to generate an initial report, and verifying and correcting to obtain an optimized report. By adopting the method, the consistency and availability of food safety data can be improved, the credibility of safety certification is enhanced, and a systematic and efficient solution is provided for food quality safety detection.

The invention discloses an artificial intelligence model-based image classification robustness test enhancement method and system, and the method comprises the steps: carrying out the deep semantic analysis of the image content based on a multi-modal artificial intelligence model, automatically recognizing and extracting the class related features and non-class related features in an image, and generating a structured feature analysis report; the method comprises the following steps: generating a Keep Strategy and a Replace Strategy which are complementary to each other, and generating a Keep Strategy and a Replace Strategy which are complementary to each other; performing image editing operation based on the generated strategy to generate a corresponding image sample; performing automatic quality verification on the edited image sample, filtering out low-quality samples which do not conform to expectation, and generating a test sample; and comprehensively evaluating the target classification model based on the generated test samples, calculating performance indexes of the model on different types of test samples, generating a detailed robustness analysis report, and identifying weak links and improvement directions of the model. According to the scheme, a complete'generation-test-evaluation 'closed-loop system can be established, a standardized and quantifiable robustness evaluation index system is formed, and the model robustness can be scientifically and quantitatively evaluated.

This application provides a method and apparatus for auxiliary assessment of student quality based on a multimodal large model, belonging to the field of large model assessment technology. The method includes: acquiring student data of target personnel; using a multimodal large model to extract features from the student data in the following manner to obtain student quality features of the target personnel in multiple dimensions: converting structured data in the student data into corresponding student quality features according to preset mapping rules; extracting student quality features corresponding to unstructured text data in the student data using an attention mechanism based on preset prompts; and extracting student quality features corresponding to unstructured image data in the student data using a visual encoder. These multiple-dimensional student quality features are used to assist in assessing the student quality of the target personnel. This application can simultaneously process structured and unstructured data in the student data, extracting relevant features, facilitating the work of relevant personnel, and improving assessment efficiency.

The application provides a target tracking method and system fusing co-occurrence statistics and fhog gradient features, and the method comprises the following steps: determining an initial position according to a target region; calculating a target position p t and a scale factor s t‑1 of a previous frame of a current frame I t‑1 , so as to determine a target region and extract relevant features of the current frame; co-occurrence filter is used to obtain co-occurrence statistical information between pixel pairs in a tracked target image, fDSST algorithm is used to obtain fhog features of the tracking target, and the co-occurrence statistical information and the fhog features of the tracking target are fused, so as to be used as target image features; when the model is updated, the target region is determined according to a target center p t and a target scale s t of the current frame I t , and the target region is extracted; the target region extraction features are sent into a position filter and a scale filter, so as to obtain a target tracking result through iterative updating. The application solves the technical problems that the prior art cannot completely and effectively use feature expression of a target, has low robustness, and has poor tracking effect in a specific scene.

To provide an object identification method capable of monitoring an abnormal operation of a construction image in real time, saving labor costs required for safety monitoring at a construction site, improving an accuracy rate of the safety monitoring at the construction site, and reducing an occurrence rate of safety accidents.SOLUTION: The method includes: collecting a construction image; recognizing the construction image based on a target detection model to obtain a recognition result, the recognition result including a category of a construction object and location information of the construction object; and determining whether the construction image is associated with an abnormal action based on the recognition result. The ith feature segmentation module in the feature segmentation network performs convolution processing and segmentation processing on the ith type of first image feature, and outputs the jth type of sub-image feature to the jth feature fusion module connected to the ith feature segmentation module.SELECTED DRAWING: Figure 2

The application discloses a hyperspectral wetland image classification method based on a graph capsule neural network, which comprises the following steps: S1, learning feature transformation is performed on an adversarial domain self-adaptive framework, so that source domain samples and target domain samples of a hyperspectral wetland image are matched in features; S2, a graph capsule neural domain self-adaptive network structure is constructed, domain-invariant features and domain-related features are extracted, and transferable features are discovered and shared across domains; and S3, a coupling structure two-classifier is designed, the two-classifier is trained by using the source domain samples, classification differences of the target domain samples are maximized, and precise classification of the hyperspectral wetland image is realized by identifying a classification boundary. Meanwhile, the application discovers transferable knowledge and realizes cross-domain sharing, enhances effective discrimination of a class boundary, and finally realizes precise classification of the hyperspectral wetland image under conditions of unknown regions, complex scenes, and lack, deficiency and imbalance of data types.

The invention discloses a centrifugal fan running state intelligent diagnosis method and system based on deep learning, and relates to the technical field of centrifugal fan fault diagnosis and health management. According to the intelligent diagnosis method for the running state of the centrifugal fan based on deep learning, multi-working-condition vibration and multi-source data are collected and preprocessed, and weak fault features are enhanced through self-adaptive multi-scale time-frequency analysis; a parallel network is constructed, vibration deep features are extracted through physical prior attention, multi-source time sequence features are extracted through Transform, and the features are decomposed into fault sharing and working condition related features through decoupling loss and then fused; according to the method, field local feature distribution is aligned through local maximum mean value difference, and the data quality is improved through multi-source data synchronous acquisition and refined preprocessing. And by combining adaptive signal enhancement, physical prior guided feature extraction, feature decoupling fusion and local domain adaptation, working condition and domain difference interference is weakened, and accurate and stable diagnosis of the running state of the fan in a complex scene is realized.

The application relates to the computer field, in particular to the artificial intelligence field, and provides a living body detection method and device, equipment and a storage medium. The application embodiment can be applied to various scenes such as cloud technology, artificial intelligence, intelligent transportation and auxiliary driving. The method comprises the following steps: from a to-be-detected face image input into a target detection model, a target domain irrelevant feature map with a scene correlation degree lower than a set threshold value is extracted; and based on the target domain irrelevant feature map, a prediction classification result representing whether the to-be-detected face image is a real face image is obtained. The target detection model is obtained through training of at least one pair of contrast combinations based on initial detection model and each domain irrelevant feature map and each domain relevant feature map of an auxiliary detection model. The contrast combinations are constructed, and the model generalization ability and the model stability are improved. The prediction classification result is obtained based on the extracted target domain irrelevant feature map, and the data acquisition cost and the equipment deployment difficulty are reduced.

The invention provides a semi-supervised quality monitoring method based on physical information guidance in a laser metal deposition manufacturing process. According to the method, firstly, an image processing algorithm is designed to extract relevant features (such as length, width and area) of a molten pool; secondly, analyzing feature relevance by using a Spearman correlation coefficient and calculating a feature weight; further, constructing a physical information gain loss function in combination with internal defect physical knowledge and feature weights; and finally, based on the physical information gain loss function, developing a total loss function for model training and optimization, thereby realizing quality grade monitoring. According to the method, the feasibility of monitoring the model based on the molten pool characteristic quality in the LDED manufacturing process is proved, the model performance is remarkably improved by integrating physics knowledge input, and the robustness and generalization ability of the model are enhanced.

The present disclosure relates to a font recognition method, device, readable medium and electronic equipment. The font recognition method divides the image to be recognized into a plurality of sub-images through the predetermined font recognition model, and obtains the first image corresponding to each sub-image. Image features, determine the second image features corresponding to the image to be recognized based on the first image features corresponding to each sub-image of the image to be recognized, the second image features include each sub-image of the image to be recognized and the context-related features of other sub-images are used to determine the font type corresponding to the target text based on the second image features. In this way, the image to be recognized can be described more comprehensively and accurately based on the correlation between each word image and other sub-images. This can effectively improve the accuracy of font recognition results and also effectively improve the font recognition rate.

The embodiment of the application provides a safety helmet detection method, a model training method, a system, a device and a storage medium, and belongs to the technical field of artificial intelligence. The second image feature is obtained by inputting the training data into the backbone network layer of the model for feature extraction and then connecting the network layer for feature integration. The input feature is subjected to average pooling, high-dimensional convolution and global average pooling operations in parallel in the connection network layer, and then the feature fusion is performed to obtain the output feature. Thus, the important local information and global context information in the image can be focused on, the loss of safety helmet feature information is reduced, the safety helmet detection related features in the comprehensive training data are learned, the second image feature is input into the head network layer of the model for feature prediction to obtain a predicted detection frame, and the safety helmet detection model is reversely updated based on the predicted detection frame and the real detection frame. The safety helmet detection model trained by the application has high robustness, and can accurately detect the safety helmet wearing condition for a blurred monitoring picture.

The application discloses a CTR recommendation method based on feature interaction and score integration. The steps are as follows: 1. All features are divided into four categories: Q_U_I features, user behavior features, domain-related features and domain ID features; 2. The scene interaction refinement module is used to interact the Q_U_I features and the domain-related features to obtain combined features. The combined features are spliced with the user historical behavior features, the Q_U_I features and the domain-related features to finally obtain the input features of the backbone; 3. The expert projection module is used to extract differentiated public features; 4. The gating mechanism is used to weight and add the public features and the specific features to obtain the advanced fusion features of each view. Then the advanced fusion features are input into the corresponding MLP to obtain the scores of each view, and then the gating mechanism is used to integrate the scores, and the final prediction result is obtained through the activation function sigmoid; 5. The loss function is used for optimization. The application can capture the inherent correlation information in the feature space and the label space, and improve the correctness of multi-domain CTR prediction.

The invention relates to a method, a computer device, and a system for automatically detecting anomalies in machine operation. The machine operation in question is, in particular, that of machines for filling and packaging food and / or beverages. Anomaly detection comprises the acquisition of sensor data, the automatic categorization of this data according to operating states, and the extraction of relevant features for each category of operating states. Thresholds are determined using statistical methods to define precise operating limits. This model is monitored and adapted to react to anomalies at an early stage and to ensure operational safety. The invention provides a robust monitoring system that enables real-time monitoring of the machine system's condition and allows for early responses to deviations in operating conditions.This contributes to increased operational reliability, avoidance of downtime and optimization of maintenance processes.

Detecting application features within application portfolios. A processor may receive a request comprising an application. The processor may generate a first embedding vector based on the request. A model executed by the processor analyzes an embedding database that contains embedding vectors for multiple applications. Based on this analysis, the model identifies a first application within the plurality that features characteristics similar to those of the application in the request. Subsequently, the processor generates a report indicating that the first application includes these associated features and transmits the report to multiple recipients.

The application belongs to the field of electroencephalogram signal processing and classification, and particularly discloses an electroencephalogram transfer learning classification method and system based on an aligned reference source domain, which comprises the following steps: obtaining electroencephalogram signals of multiple subjects, calculating the Euclidean distance between each electroencephalogram data for each subject, taking the subject with high intra-class similarity and low inter-class similarity as a reference source domain, and taking the remaining subjects as a target domain to construct a training set; training a classification model based on the reference source domain through the training set, wherein the classification model comprises a feature extractor, a feature resolver and a classifier, the feature extractor extracts reference standard features and target domain features according to the electroencephalogram data of the reference source domain and the target domain; the feature resolver performs feature unwinding on the target domain features to obtain class-related features and domain-invariant features, so that the class-related features are aligned to the reference standard features; and the classifier determines the class of the electroencephalogram data of the target domain according to the class-related features. The application can effectively improve the classification accuracy of electroencephalogram signals.

Systems and methods that generate reports for assessment sessions are described. For example, an assessment system may automatically process audiovisual data (e.g., a voice command synced to captured video of an assessment session) in real- time, extract relevant features, and generate an assessment report or perform other actions. The systems and methods, therefore, may facilitate an efficient and accurate generation of diagnostic reports for an assessment session (e.g., for ASD), enabling remote diagnosis while incorporating human oversight for final approval, among other benefits.