147 results about "Prototype learning" patented technology

The invention relates to the technical field of medical image segmentation, in particular to a few-sample medical image segmentation method based on edge perception multi-prototype learning, and the method comprises the steps: inputting support and query images into a feature encoder, and extracting support and query feature maps of different sizes; inputting into a local attention fusion prototype generator to generate a support foreground prototype; processing the support mask through dynamic corrosion operation to generate an inner boundary prototype; generating a multi-foreground local prototype through a multi-layer perceptron; local and global information is optimized through multi-scale feature extraction, and a multi-scale prototype is obtained; fusing to obtain a multi-prototype foreground prototype; dynamic calculation weighting is carried out on the multi-prototype foreground prototype by using a double-stage prototype optimization network, and automatic calibration is carried out; then prediction is carried out through a prototype prediction module, and finally collaborative optimization is carried out through a loss calculation module; the method can effectively solve the problem of edge detail loss involved in the background technology.

The invention relates to the technical field of aircraft intelligent detection and maintenance systems, and discloses an aircraft defect intelligent evaluation system and method based on multi-modal fusion, and the system comprises a multi-modal data collection module, a tensor construction and decomposition module, a meta-prototype relation network module, a multi-target game optimization module, and a closed-loop feedback module. The method comprises the steps of constructing a five-order feature tensor through multi-modal data synchronous acquisition and space-time alignment, extracting low-rank features through hypergraph block item decomposition, dynamically generating a defect prototype set in combination with meta-learning, generating a maintenance decision by adopting a Nash equilibrium strategy and fusing multi-constraint conditions, and optimizing system parameters through closed-loop feedback. The whole-process intelligentization of aircraft defect detection and maintenance is realized; according to the method, high-precision defect detection is realized through multi-modal data fusion and hypergraph modeling, an intelligent decision is generated in combination with dynamic prototype learning and multi-target game optimization, and continuous self-optimization is performed by means of a closed-loop feedback mechanism, so that the operation and maintenance efficiency and safety of the aircraft are improved automatically in the whole process.

The invention relates to the technical field of machine learning and nuclear power safety assessment, and discloses a nuclear power safety assessment method and system based on machine learning, and the method comprises the steps: collecting and preprocessing the multi-source heterogeneous data of a nuclear power station; constructing a time convolution network model to extract multi-scale time sequence features of the multi-modal data and realize modal alignment; constructing a contrast learning model to optimize multi-modal feature representation; constructing a multi-granularity prototype learning model in the optimized feature representation space; constructing an adaptive attention fusion model to dynamically integrate multi-modal information, and continuously optimizing an evaluation model by a continuous learning technology; through multi-modal fusion and multi-scale time sequence representation learning, comprehensive assessment of the safety state of the nuclear power station is realized, and the comprehensiveness of safety assessment is improved.

The invention belongs to the technical field of artificial intelligence and medical diagnosis, and particularly relates to an interpretable Transform medical diagnosis method based on prototype learning, and the method comprises the following steps: data preprocessing; extracting prototype features; constructing a model; optimizing an attention mechanism; key value pair storage: storing the key value pair as a parameterized prototype; designing a loss function; an Adam algorithm is selected to train the model, and the learning rate and batch size hyper-parameters are adjusted according to the actual situation; and evaluating and optimizing the model. According to the method, a prototype learning mechanism is introduced, the attention mechanism of the Transform model is optimized, and the innovative method overcomes the defect of insufficient model interpretation in the prior art. By combining the prototype features and the attention weight, the model can more accurately position key information, and the accuracy of medical diagnosis is improved.

The invention relates to the technical field of artificial intelligence, and discloses an event prediction method and system based on multi-modal fusion, and the method comprises the steps: constructing a knowledge graph encoder, and converting domain expert knowledge into learnable vector representation; constructing a multi-granularity feature extraction network, and extracting features from different microcosmic, mesoscopic and macroscopic scales; realizing a knowledge-guided attention mechanism, and dynamically adjusting feature scale importance; constructing a prototype learning module, and establishing prototype representation of the abnormal category; a knowledge migration mechanism is constructed, and the generalization ability of the model to novel anomalies is enhanced; and multi-granularity abnormal event detection and early warning are realized, and a detection result and interpretable analysis are output. According to the method, through combination of knowledge guidance and multi-scale feature learning, efficient identification and early warning of social abnormal events under the condition of data scarcity are realized, and the method is suitable for the fields of public place safety monitoring, urban traffic safety management, large-scale activity safety guarantee and the like.

The invention relates to a cross-bearing single sample intelligent diagnosis method based on cognitive guidance and Riemannian manifold, and belongs to the technical field of rotating machinery fault diagnosis. Aiming at the problems of insufficient global task distribution learning ability, small sample over-fitting, Euclidean modeling limitation and the like of the existing meta learning method in a cross-domain single-sample scene, a cognitive guidance Riemannian meta learning framework is provided. According to the technical scheme, the method comprises the following steps: 1) constructing cognitive prototype learning global task distribution, and guiding a model to extract high-quality general meta-knowledge from multiple tasks; 2) designing a cognitive adaptive factor to dynamically adjust source domain memory, enhancing target domain adaptation and reducing single sample deviation; and 3) introducing a Riemann metric driving strategy, mapping the data to a Grassmann manifold space, and enhancing the non-linear feature discrimination ability by using geodesic distance. According to the method, the average diagnosis accuracy in a cross-bearing single sample task reaches 93.46% and is improved by 10.04% compared with an existing optimal method, and the accuracy and generalization ability under complex working conditions are improved.

The invention relates to the technical field of pathological image analysis and mining, and particularly discloses an ovarian cancer subtype classification method based on prototype learning and multi-view deep embedding clustering, and the method comprises the following steps: S1, collecting a tissue pathological image of an ovarian cancer patient and a corresponding full-view digital pathological image; and S2, generating a multi-view data set. According to the ovarian cancer subtype classification method based on prototype learning and multi-view deep embedding clustering, the problem that in the prior art, patch-level labels are generally lacked in the field of multi-instance pathological images, so that many natural image processing methods cannot be applied to the field of pathological images is solved. A ResNet backbone network is used for extracting features of pathological images under the maximum magnification, a small number of pathology prototypes are introduced to guide deep embedded clustering through pathology expert priori knowledge, a pathology image spectrogram is introduced to serve as a reference view, and the accuracy and stability of clustering are enhanced.

The invention belongs to the technical field of image segmentation, and relates to a semi-supervised pancreatic image segmentation method based on prototype estimation and prototype consistency. According to the method, based on a semi-supervised segmentation framework of an average teacher, V-Net is adopted as a deep learning segmentation model, and a projection head is inserted in the last but one stage of a decoder as a prototype branch; prototype learning design prototype estimation is introduced, unmarked data prediction is divided into determined areas of fuzzy areas, and different losses are designed for areas with different reliability of the unmarked data. The problems that in an existing semi-supervised learning segmentation method, unmarked data cannot be fully utilized, potential information of marked data is insufficient in utilization and the like are effectively solved, and a higher-performance solution is provided for an abdominal CT image pancreatic organ segmentation task.

The invention provides an image text description generation method, electronic equipment and a readable storage medium. According to the method, the object perception prototype learning module and the global context feature extraction module are introduced, so that fine-grained information and global semantic understanding in the image are effectively balanced. The visual backbone network module can extract multi-scale and multi-level image features and perform fusion, thereby enhancing the expression ability of the image features. The object perception prototype learning module further extracts an object prototype from the fusion features to ensure that the model can accurately capture key objects and attributes thereof in the image, and the global context feature extraction module ensures that the overall context of the image is fully understood. On the basis, the encoding and decoding module combines the global context and the object prototype to generate the text description, so that the semantic splitting phenomenon in the traditional method is avoided, and the detail information in the image is effectively reserved, thereby improving the accuracy and integrity of the image description.

The invention belongs to the computer vision technology, and particularly relates to a small sample multi-modal prototype learning-oriented remote sensing scene classification method, which comprises the following steps of: acquiring RGB (Red, Green and Blue) images with category labels and text prompts of the RGB images as a support set; establishing a text prototype, an RGB prototype and a hyperspectral prototype of each category according to the support set; and extracting to-be-classified query set image features by using a pre-trained CLIP image encoder, calculating cosine similarities between the query set image features and the text prototype, the RGB prototype and the hyperspectral prototype of each category of the support set, taking the cosine similarities as input of a multi-layer perceptron, and obtaining the category of the to-be-classified RGB image through classification of the multi-layer perceptron. High-precision and high-robustness remote sensing scene classification is realized under the small sample condition, only prototype and similarity calculation is needed in the reasoning stage, and deployment and expansion are easy.

The invention relates to a modal heterogeneous federated learning privacy protection method based on a cross-modal prototype, and belongs to the technical field of artificial intelligence and information security. The server firstly initializes a global model for each client, wherein the global model comprises a mapping module of a corresponding mode of the client; and the server sends the initialized model to the corresponding client. The multi-mode client initializes a private prototype learning model. And local model training is carried out by using the global model, the single-mode client and the multi-mode client. And after the local prototype and the local model sent by the client are received, the server executes prototype aggregation and model aggregation respectively. And after receiving the global prototype pair and the global mapping module, the client updates the local mapping module and starts a new round of training until a specific number of training rounds is reached. According to the method and the device, the communication overhead can be reduced, local models are not required to have the same structure, and a single-mode client can acquire information from a missing mode prototype in a targeted manner.

The invention discloses a water-cooled permanent magnet coupler heat dissipation fault diagnosis method based on a neural network, relates to the field of coupler heat dissipation fault diagnosis, and obtains a three-dimensional temperature field with a fault tag and a fault sensitive tensor through multi-physical field coupling and fault tag creation analysis based on an initial three-dimensional temperature distribution field and discrete electromagnetic field data. And performing multi-field fusion and space-time compression on the three-dimensional temperature field with the fault tag and the fault sensitive tensor to obtain a feature vector of concentrated fault key information, and performing physical constraint dimension reduction, fault prototype learning and parameter inversion on the three-dimensional temperature field with the fault tag and the feature vector of the concentrated fault key information to obtain fault parameter estimation. Bidirectional recursive fault analysis is performed based on fault parameter estimation to obtain a propagation state, gradient analysis and adversarial diagnosis are performed on the propagation state to obtain a fault type corresponding to the maximum probability, and misjudgment under complex working conditions can be greatly reduced.

The invention provides a fine-grained three-dimensional model classification method and system based on dynamic prototype learning, and belongs to the technical field of three-dimensional geometric analysis. Comprising the steps of performing multi-view orthogonal projection rendering on a three-dimensional model, performing feature extraction and projection operation on an image sequence of each view after projection rendering based on an encoder, and fusing multi-view features; a shared prototype pool is constructed, a prototype cost matrix is generated, a structured measurement space is constructed by calculating the cosine similarity of the multi-view features and the prototype cost matrix, and dynamic soft allocation is executed; sequentially executing a dynamic updating strategy and joint loss optimization on the shared prototype pool; and measuring the distance between the test sample features and the prototypes in the shared prototype pool to realize the probabilistic decision-making of the fine-grained category. Therefore, a dynamic prototype learning framework with geometric perception capability is constructed, and the precision bottleneck of a fine-grained classification task can be broken through by analyzing interpretable features and prototype mapping relations.

The invention provides a cancer survival prediction method based on a pathological image, and relates to the technical field of artificial intelligence and medical image analysis. The method comprises the following steps: firstly, dividing an acquired pathological image to obtain a plurality of image blocks; and obtaining the feature vector of each image block and identifying the tissue type to which each image block belongs. Calculating spatial proximity, feature similarity and tissue type compatibility between the image blocks according to the center coordinates of the image blocks, the feature vectors and the tissue type to which each image block belongs; and based on a calculation result, constructing a dynamic heterogeneous graph, and performing feature extraction to obtain a multi-scale feature graph of the dynamic heterogeneous graph. And performing multi-prototype learning on the basis of the belonging organization type of the image block and a cross-category attention mechanism to generate multiple prototypes. And finally, the multi-scale feature map and the multiple prototypes are aggregated to obtain final image representation, and cancer survival prediction is performed based on the final image representation, so that the prediction accuracy of the cancer survival state is improved.

The invention discloses a remote sensing target detection method for an open set scene, and belongs to the technical field of open set target detection. The open set remote sensing target detection network introduces a cascade target positioning network, a background feature enhancement module and reciprocal point prototype learning on the basis of a Faster-RCNN model; the cascade target positioning network can effectively adapt to multi-scale features, calculates the quality of a candidate box through centrality loss, and replaces a foreground-background classification mode in a traditional region suggestion network, so that the recall rate of objects is increased, and the possibility that unknown objects are misjudged as backgrounds is reduced; the background feature enhancement module fuses various background region information so as to enhance the adaptability to a complex environment and reduce the interference of background noise on target recognition; according to reciprocal point prototype learning, a reciprocal point prototype of a known category and a background is constructed, and an embedded network metric learning strategy is combined, so that background information and the known category jointly participate in candidate box classification, and a known target, an unknown target and the background are effectively distinguished.

The invention discloses a fine-grained feature matching vehicle re-identification method for a license plate shielding scene, and the method comprises a vehicle detection stage based on illumination perception and adaptive prediction, and a fine-grained Transform feature matching stage based on implicit semantic prototype perception. Comprising an illumination sensing module and an adaptive prediction module. The illumination sensing module is used for realizing image adaptive enhancement under different illumination conditions; the adaptive prediction module suppresses a redundant prediction frame through a self-attention weight mechanism; in the fine-grained Transform feature matching stage based on implicit semantic prototype perception, an implicit semantic prototype learning module and a fine-grained Transform matching module are included; the implicit semantic prototype learning module unsupervised decouples vehicle structural components through a group of learnable semantic prototypes to generate a soft semantic distribution diagram; and the fine-grained Transform matching module is used for introducing semantic affinity bias in cross attention. According to the method, the accuracy and stability of vehicle detection and identity matching in a complex environment can be improved.

The invention relates to a multi-degradation medical image unified fusion method based on degradation prototype learning, and belongs to the field of medical image fusion. The method comprises the steps that low-dose PET data, CT metal artifact data and MRI data with motion artifacts are generated through the imaging principle; learning a degradation prototype by using a feature selection mechanism; the basic fusion model is decomposed into a plurality of branches through a low-rank decomposition strategy, and processing can be performed through different branches when different degradation data are fused; a prompt module based on a learnable feature prototype is designed, and fusion is promoted by injecting degradation-related invariant features into different LoRa branches; and constructing a fused image through an output layer by integrating the degradation elimination fusion features in different scales. According to the method, the medical images containing degradation can be effectively fused, and the robustness and practicability in reality are improved.

The invention relates to a remote sensing image semantic segmentation method and system based on prototype learning, which dynamically optimizes feature representation through prototype learning, captures multi-scale context information in combination with a mask Transform, and significantly improves segmentation precision while keeping model efficiency, especially for a segmentation effect of small targets and complex scenes.

The invention relates to an interpretable cross-modal target re-identification method and system based on prototype learning. The method comprises the following steps: constructing a prototype cross-modal network for carrying out channel adaptation on image samples in a multi-modal image sample set, extracting multi-scale fusion features and obtaining a discriminative feature vector through channel weighting, traversing the spatial position of the discriminative feature vector and extracting local features, and carrying out multi-scale fusion on the image samples in the multi-modal image sample set; selecting a local feature with the highest similarity for each initial prototype, fusing the local feature with the initial prototype to obtain a new prototype, iteratively updating the prototype corresponding to each identity tag to obtain a dynamic prototype set, generating a prototype activation thermodynamic diagram about a discriminative feature vector and each prototype in the dynamic prototype set, and classifying and outputting a classification result of a corresponding image sample; and training the prototype cross-modal network, and carrying out cross-modal re-identification by using the trained prototype cross-modal network. By adopting the method, the cross-modal recognition precision, scene adaptability and interpretability of target re-recognition in a complex environment can be improved.

The invention discloses a hyperspectral image open set classification method and device based on fractional domain information enhancement and a hypersphere prototype learning strategy, and belongs to the technical field of hyperspectral image open set classification. In order to solve the problem that a high misclassification risk exists between a known category and an unknown category in an existing hyperspectral image classification method, the method comprises the following steps: firstly, obtaining fractional domain information of hyperspectral data based on weighted fractional Fourier transform, and then fusing the fractional domain information with spatial spectral domain information; deep feature extraction is carried out on the hyperspectral image through a double-branch network, a hypersphere prototype learning strategy is adopted, utilization of a measurement space is optimized, and features of known categories are restrained to be evenly distributed on a hypersphere; and carrying out identification based on a closed set classifier of a known category prototype, and meanwhile, realizing open set identification by utilizing a hypersphere prototype radius so as to obtain a final open set classification result.

The invention discloses a remote sensing change interpretation deep network combining distributed sampling and feature decoupling, and belongs to the technical field of remote sensing image processing. In order to solve the problem of inaccurate classification of remote sensing change pixels caused by fuzzy semantic boundaries easily caused by mixed feature extraction, the mixed features are decoupled into change and invariant features through joint distribution sampling so as to complete remote sensing change interpretation. In the training stage, the posterior distribution of the decoupled features is calibrated and learned through labels and is used for training a change prior generator; feature decoupling is realized by combining posterior distribution and a feature separator, and features are further gathered through prototype learning; a super-expectation push-pull loss regular term is provided, and the inter-class distance is increased by improving the prediction expectation push-pull positive and negative sample features to the farther end. In the test stage, remote sensing image change detection is completed through modules such as a feature separator and a change detection head without posterior distribution support. Experiments prove that the method has remarkable effects on qualitative and quantitative indexes.

The invention provides an image tampering positioning method, a computer device and a storage medium. The method comprises the following steps: inputting to-be-detected image data; processing the to-be-detected image data by using preset frequency adaptive Transform modules stacked in a preset number of stages to obtain an output feature mark of each stage; a preset prototype learning module is utilized between any two adjacent stages of a preset frequency self-adaption Transform module, a KNN-based local density peak value clustering algorithm is used for carrying out clustering on the output feature mark from the previous stage, then weighted aggregation is carried out, a tampered prototype mark is obtained, and the tampered prototype mark serves as input of the next stage; and fusing the output feature marks of all the stages and the tampered prototype marks of all the preset prototype learning modules by using a preset mark progressive fusion positioning head to generate a prediction image of tampered region positioning. The tampered image mark can be efficiently generated, and the tampered area can be accurately positioned.

The present invention discloses a remote sensing change interpretation deep network with joint distribution sampling and feature decoupling, which belongs to the field of remote sensing image processing technology. In view of the problem that mixed feature extraction easily leads to blurred semantic boundaries, which in turn causes inaccurate classification of remote sensing change pixels, the mixed features are decoupled into change and invariant features through joint distribution sampling to complete the remote sensing change interpretation. In the training stage, the posterior distribution of the decoupled features is used to train the change prior generator through label calibration learning; the posterior distribution and feature separator are combined to achieve feature decoupling, and the features are further aggregated through prototype learning; an over-expectation push-pull loss regularization term is proposed, which increases the distance between classes by improving the predicted expectation to push and pull the positive and negative sample features to the farther end. In the testing stage, remote sensing image change detection is completed through modules such as feature separator and change detection head without the support of posterior distribution. Experiments have shown that this paper has achieved significant results in both qualitative and quantitative indicators.

The invention provides a heterogeneous federated learning method based on dynamic global prototype aggregation and classifier selection, belongs to the technical field of machine learning, and aims to solve inherent conflicts between global classifier sharing and client personalized demands in federated learning. The method comprises the following steps of: S1, constructing a dynamic global prototype aggregation mechanism based on classification accuracy; s2, constructing a local prototype classification loss-based dynamic classifier selection mechanism of the client; s3, designing a target loss function of the client; compared with a traditional federal prototype learning method, the precision of the method is improved by 4.60% under isomorphic model setting and is improved by 10.71% under heterogeneous setting, and the superiority of the method in global generalization and client specific personalization is proved.

The present application provides a method for bearing fault diagnosis across working conditions by using a minimum entropy optimized prototype contrast network, and relates to the technical field of intelligent fault diagnosis. In the pre-training stage, an auxiliary domain discriminator is constructed to assist DA with the discriminant information of the classifier, the classification difficulty is evaluated by sample entropy, and the performance degradation in the DA process is inhibited. In the training stage, the learning vector quantization method is adopted to find the prototype. Through intra-domain prototype contrast learning, the sample features are closely gathered around the same prototype in the feature space, while being separated from the different prototypes. Then, the intra-class consistency of the features is enhanced, and the inter-class distinguishability is improved, so as to realize the precise alignment in the feature space. In addition, the cross-domain instance-prototype learning aligns the semantic structure in the shared embedding space, alleviates the negative transfer problem through the fine-grained alignment strategy, and improves the generalization ability of the model. Through the pseudo-label generation and the weighted loss function, the generalization performance of the model in the cross-working-condition small sample scene is improved.

This invention discloses a multimodal hash retrieval method, system, device, and medium based on a multi-viewpoint central structure, belonging to the fields of artificial intelligence and multimodal hash retrieval technology. The technical problem this invention aims to solve is achieving a balance between intra-class compactness and inter-class separability during multimodal hash retrieval. The technical solution adopted is as follows: constructing a multimodal dataset; constructing a multimodal hash retrieval model based on a multi-viewpoint central structure; and training the model. Specifically, constructing the multimodal hash retrieval model based on a multi-viewpoint central structure involves: modality-specific prototype learning: using an image modality deep multilayer perceptron and a text modality deep multilayer perceptron to extract refined features from the corresponding modalities, and calculating the average value of the refined features of the image modality and the text modality to obtain modality-specific prototypes, thereby obtaining unique features of the image modality and unique features of the text modality; multimodal ensemble class prototype learning; and multi-view semantic enhancement hash learning.

The application discloses a semi-supervised medical image segmentation method based on model self-distillation and prototype learning, and relates to the technical field of image signal processing. The semi-supervised medical image segmentation method based on model self-distillation and prototype learning comprises the following steps: S1, establishing a semi-supervised medical image segmentation dataset; S2, constructing a network architecture; S3, designing a semi-supervised medical image segmentation scheme, and building a semi-supervised medical image segmentation model according to the designed scheme; S4, training the semi-supervised medical image segmentation model by using a deep learning Pytorch framework; and S5, inputting a medical image to be segmented into the model to obtain a medical image segmentation result. The semi-supervised medical image segmentation performance is improved to a new height by using the proposed double-flow memory bank architecture, the self-distillation method based on an image block affinity matrix and the prototype synthesis method based on context matching.

The invention discloses an ultrasonic image prototype learning method based on causal invariant features, and the method comprises the steps: carrying out the sampling based on the prediction confidence degree of an ultrasonic image in a pre-training model, obtaining different sample distribution environments, generating a discrete prototype codebook of each environment through VQ-GAN, and capturing the specific main feature mode of the environment for cross-domain learning. And finally, extracting causal invariant features which do not change along with the environment by matching different environment prototype codebooks. According to the method, background interference is effectively reduced, the learning ability of the unusual sample is enhanced, and the method has wide medical image analysis application value.

This invention relates to the field of medical image segmentation technology, specifically a few-shot medical image segmentation method based on edge-aware multi-prototype learning. The method includes: inputting support and query images into a feature encoder to extract support and query feature maps of different sizes; inputting these into a local attention fusion prototype generator to generate support foreground prototypes; processing the support mask through dynamic erosion to generate inner boundary prototypes; generating multiple foreground local prototypes through a multilayer perceptron; optimizing local and global information through multi-scale feature extraction to obtain multi-scale prototypes; then fusing them to obtain multi-prototype foreground prototypes; dynamically calculating and weighting the multi-prototype foreground prototypes using a two-stage prototype optimization network and performing automatic calibration; then predicting using a prototype prediction module; and finally, performing collaborative optimization through a loss calculation module. This invention effectively solves the problem of edge detail loss in the background art.

The invention provides an image segmentation model training method fusing boundary perception and prototype cluster generation, and an image segmentation method and system, and belongs to the technical field of image processing. According to the invention, explicit modeling is carried out on boundary features through boundary prototype learning, pixels are clustered into boundary and non-boundary categories, and the problem of insufficient segmentation precision caused by mismatching of boundary region features in a traditional prototype method is overcome; the features are projected to a hidden space and further clustered into multiple subclasses, multi-prototype cluster representation is generated, the heterogeneity features of the medical image are effectively captured, and the limitation that segmentation is incomplete due to the fact that a single prototype can only recognize a core area is solved; on the basis of non-parametric characteristics of prototype learning, the method does not depend on a large number of learnable parameters, robust feature representation is achieved through intra-class compactness and inter-class separability, and compared with a mainstream deep learning method, the method has more excellent generalization ability; the clustering process enables the classification decision of each pixel to be traced back to the similarity calculation with a specific prototype, and good interpretability is provided.