75 results about "Linear classifier" patented technology

The invention provides a landslide classification method and system based on a visual language model and a cross attention mechanism. The method and the system specifically comprise the following steps: data preprocessing: carrying out Canny edge detection on an RGB image, and calculating terrain attributes such as a gradient and a slope direction for a DEM (Digital Elevation Model); feature extraction: capturing local features by adopting a reflection filling convolution layer and multi-scale residual connection; a visual language model is introduced, wherein semantic enhancement features are extracted through image-text alignment by means of the visual language model; cross self-attention fusion: capturing a global context through self-attention, and focusing heterogenous data complementary information by cross attention; and classifying and outputting: outputting a result by using global average pooling and a linear classifier. Through the visual language model and the cross self-attention mechanism, the landslide recognition capability under the complex terrain is effectively improved, an efficient and reliable technical means is provided for geological disaster monitoring, and the method can be widely applied to the fields of landslide recognition, risk assessment and the like.

The invention provides a multi-modal large model mental ability improving method and device based on attention intervention, and relates to the technical field of artificial intelligence. The method comprises the following steps: generating a multi-modal psychological theory data set; inputting the data to a multi-modal large language model to be evaluated, and constructing an input sequence of a Transform network; extracting an activation value of each attention head in each layer of attention network for each input sequence, and defining a belief label of the activation value from the perspective of a leading angle; taking the trained linear classifier as a probe model to obtain a predicted belief label, and constructing a sensitivity score of each attention head; and extracting an intervention target direction vector of each high-sensitivity attention head, and executing directional activation intervention on each high-sensitivity attention head to obtain a multi-modal large language model after the psychological theoretical ability is improved. According to the method, innovation is systematically carried out from three dimensions of data construction, mechanism detection in the model and reasoning intervention enhancement, and the key blank in the prior art is filled.

The invention discloses an unmanned aerial vehicle detection method based on self-supervised learning. The method comprises the following steps: collecting CSI data in multiple scenes for a pre-training task of a Transform model; after knowledge in the pre-trained Transform model is migrated to a lightweight CNN model by using a knowledge distillation method, a linear classifier is added to the CNN model, and the CNN model is optimized by using a small amount of label data through quantitative perception fine tuning; and performing real-time unmanned aerial vehicle detection on the test data through the optimized CNN model. According to the method, channel state information in a scene where an unmanned aerial vehicle possibly exists is utilized, a Transform model is pre-trained through a two-dimensional reconstruction task, knowledge is migrated to a lightweight convolutional neural network through knowledge distillation, and then a quantitative perception fine tuning technology is used for training, so that efficient classification detection in multiple scenes is realized. The method has the advantages of high detection precision, low label dependence, light model weight, high cross-scene generalization capability and the like.

The invention discloses a handwritten text recognition method based on multi-stage enhancement. The handwritten text recognition method comprises the following steps: acquiring a handwritten text image; constructing a hierarchical dynamic multi-scale CNN backbone network to obtain a visual feature sequence; inputting the visual feature sequence into a time sequence multi-scale module to obtain a local enhanced feature sequence; performing global modeling on the local enhanced feature sequence by using a Transform encoder to obtain a global visual feature sequence; enhancing the global visual features to obtain a time sequence context feature sequence; dynamic weighted fusion is carried out on the global visual features and the time sequence context features through a gating fusion module; and sending the fused features into a linear classifier and a CTC decoder to obtain an identification result. According to the method, intelligent arbitration of global and time sequence features is realized through new technology application of a hierarchical multi-scale CNN trunk, time sequence context enhancement and a gating fusion mechanism, and the recognition accuracy and robustness are remarkably improved.

The application discloses a kind of light-weight human key point detection method and device based on model pruning, comprising the following steps: (1) the original image is convolved and continuously down-sampled, and different scale low-resolution images are obtained;(2) a multi-branch convolutional neural network is constructed, different resolution images are input into branch network respectively, and information fusion layer is set between different branches;(3) a linear classifier is added to each convolution block in the network, and the contribution of each convolution block is calculated using the accuracy of the linear classifier;(4) the contribution of the convolution block is sorted, and the convolution block with low contribution is deleted according to the pruning ratio to obtain a pruned network;(5) the pruned network and the original convolutional neural network are trained together using the knowledge distillation method;(6) the image to be detected is input into the trained pruned network, and the detection result of human key points is obtained.The application can effectively improve the accuracy and reduce the model parameters.

The invention discloses a network flow classification method and device based on multi-modal feature fusion, and the method comprises the steps: obtaining network flow data, extracting an IP address as a node, and constructing a communication graph; time modal features and event modal features are extracted for each node, and standardization processing is carried out on the time modal features and the event modal features; the time modal features and the event modal features are aligned; fusing the two types of aligned features; performing multi-layer neighbor sampling and hierarchical aggregation on the communication graph by using a graph neural network model to obtain structure-enhanced node features containing multi-hop neighbor information, inputting the node features into a linear classifier, outputting a category probability, selecting a category with the maximum probability as a prediction result, and performing iterative training to obtain a classification model; and mapping the classification model to a data plane of the programmable switch to realize online reasoning of the data packet. According to the method, high-precision identification of various network traffic types is realized by fusing the spatial modal, time and event modal characteristics.

The invention discloses a alumen ustum image clear area screening method based on superpixel segmentation and feature classification. The method comprises the following steps: generating a definition image of an original alumen ustum image by using a Laplace operator; enhancing the color contrast of the original alumen ustum image by using the definition image; segmenting the enhanced image into a plurality of small regions by using an SLIC superpixel segmentation method, and storing segmentation boundaries of all the small regions; applying the segmentation boundary to the original alumen ustum image, filling all small areas into a minimum enclosing rectangle, and storing the position and structure information of the minimum enclosing rectangle; using a ResNet feature extraction network to perform feature extraction on the small regions obtained by segmentation; and performing definition classification on all the small areas by using a linear classifier, and returning a classification result to an original image to obtain a clear alumen ustum image. According to the method, automatic identification and screening of clear alumen ustum structures in the image are realized through image small region division and region feature discrimination.

The present specification relates to the technical field of natural language processing and artificial intelligence, and specifically discloses a text multi-label classification method and device, wherein the method comprises: receiving a text multi-classification request; the target text data is carried in the multi-classification request; inputting the target text data into a pre-training model to obtain a target pre-training word vector matrix corresponding to the target text data; generating a prompt template matrix based on a pre-constructed adaptive prompt template; the adaptive prompt template is constructed through iterative training based on contrastive learning; splicing the prompt template matrix and the target pre-training word vector matrix to obtain a spliced target sentence vector representation; inputting the target sentence vector representation into an encoding model to obtain a target sentence representation corresponding to the target text data; and mapping and classifying the target sentence representation using a linear classifier to obtain a label set corresponding to the target text data. The above method can improve the accuracy and efficiency of multi-label classification.

Systems and methods for property guided molecular optimization using artificial intelligence diffusion models. An equivariant continuous denoising diffusion implicit model autoencoder framework (DDIM-AE) can be trained (510) on a conformational dataset to predict raw data from data corrupted by a time-dependent noise to obtain a trained DDIM-AE that ensures controlled generation of threedimensional (3D) molecules. Linear optimization of semantic embeddings of 3D molecules can be performed (520) with a linear classifier to achieve a target property value from desired properties and obtain an optimized embedding. An optimized 3D molecule that includes molecular conformation with the desired properties while preserving interactions with biochemical molecules can be generated (530) from the optimized embedding with the trained DDIM-AE.

The invention provides a cholecystectomy CVS evaluation system, method and device based on SMIL, and relates to the technical field of video processing. The image frame extraction module segments the cholecystectomy video to obtain image frames; the global feature extraction module inputs the image frame into a student Transform in a label-free self-supervised distillation architecture in the SMIL network architecture, and extracts global context features; the instance feature extraction module divides the image frame into a plurality of image blocks and inputs the image blocks into student Transformers in the multi-instance learning architecture to obtain an instance feature matrix; the local feature aggregation module inputs the instance feature matrix into an MIL Attention module to obtain local instance features; the feature fusion module performs feature fusion according to global and local features and dynamic weights to obtain specific fusion features corresponding to different standards; and the evaluation and prediction module inputs the fusion features into a linear classifier to obtain a CVS standard evaluation result. The system can be conveniently popularized to different hospitals and can adapt to operators of different styles or different devices.

The application discloses an interpretable image recognition method based on visual Transformer and prototype learning, and comprises the following steps: image preprocessing, normalizing and dividing a to-be-recognized image in training data into a plurality of non-overlapping image patches, then flattening each image patch into a one-dimensional vector; using different types of visual Transformer encoders to extract long-range dependence feature representation of the image patch; designing a prototype branch and composing a double-branch prototype layer and a single-branch prototype layer; according to the type of the visual Transformer encoder, calculating the similarity through the double-branch prototype layer or the single-branch prototype layer, obtaining an activation map through transformation, and processing the activation map; constructing a linear classifier to obtain an image recognition result; and interpreting the acquisition process of the image recognition result to complete the interpretable image recognition based on the visual Transformer and the prototype learning.

The invention relates to a time-space diagram neural network autism classification method based on dynamic function connection and dynamic effective connection feature fusion, which can realize accurate classification of autism by using resting state functional magnetic resonance imaging data. The method comprises the following steps: firstly, constructing a dynamic function connection matrix and a dynamic effective connection matrix to respectively extract brain network diagram characteristics, and under the guidance of a dynamic effective connection network, forming fused brain network space-time connection characteristics by adopting a space-time fusion position Transform based on a cross attention mechanism; introducing a multi-layer perceptron to extract high-order image features in the brain network, embedding the high-order image features as node representation of a population graph, constructing edges of the population graph by using demographic information, realizing fusion of the magnetic resonance image features and the demographic information, and finally learning node embedding through a graph convolutional network to obtain the demographic information of the population graph. And autism classification is realized based on a linear classifier. Experimental results show that the provided method has excellent performance in autism diagnosis tasks, and the accuracy and robustness of diagnosis are remarkably improved.

Methods for obtaining black-box representations of machine learning models are disclosed when information about the models' internal states or parameters is inaccessible. By using the model's outputs instead of its internal states, the black-box representation is model-agnostic and provides a reliable and robust representation of the model through an external lens. The black-box representation is generated using responses from the model to a series of initialization and information-gathering questions, quantifying the model's confidence in the responses it has just returned. The black-box representation is then used as a training dataset for a linear classifier to learn performance metrics about the model.

The application discloses an industrial information physical system cross-layer anomaly detection method based on information physical feature fusion, acquires a data packet sequence of each communication event; for each data packet sequence, extracts coarse-grained features of a sequence level thereof, analyzes all data packets to obtain fine-grained features of a data packet level, and then fuses the coarse-grained features and the fine-grained features to obtain an overall feature vector of the sequence; taking the overall feature vector of the data packet sequence as observation data, projecting the observation data to a high-dimensional feature space by using a dictionary, learning the dictionary and a linear classifier; in online testing, obtaining an overall feature vector of a data packet sequence corresponding to a current communication event, obtaining sparse coding based on the dictionary, and outputting a current state label of the industrial system by using the linear classifier. The application improves real-time performance and accuracy of anomaly detection by effectively fusing information physical heterogeneous data.

This invention provides a SMIL-based system, method, and device for evaluating CVS (Continuous Vision Loss) in cholecystectomy, relating to the field of video processing technology. The image frame extraction module segments the cholecystectomy video to obtain image frames; the global feature extraction module inputs the image frames into a student Transformer within the unlabeled self-supervised distillation architecture of the SMIL network to extract global contextual features; the instance feature extraction module divides the image frames into multiple image blocks and inputs them into a student Transformer within a multi-instance learning architecture to obtain an instance feature matrix; the local feature aggregation module inputs the instance feature matrix into the MIL Attention module to obtain local instance features; the feature fusion module fuses features based on global and local features and dynamic weights to obtain specific fused features corresponding to different standards; and the evaluation and prediction module inputs the fused features into a linear classifier to obtain the CVS standard evaluation result. This system is easily deployable to different hospitals and can adapt to surgeons with different styles or different equipment.

The invention provides a method and a system TGrKCMI for predicting interaction between circRNA (Ribonucleic Acid) and miRNA (Micro Ribonucleic Acid). According to the method, a pre-training model is used for extracting sequence features of circRNA and miRNA, and redundant information is reduced through PCA dimension reduction; then, a multi-head attention module with a gating mechanism is introduced to encode sequence features, and the expression ability of key information is enhanced; on the basis, a circRNA-miRNA interaction diagram is constructed, and robust diagram feature learning is realized in combination with diagram attention convolution of random feature masks. And finally, a kernel-based adaptive nonlinear classifier is adopted to carry out modeling on the fusion features, and high-precision and high-robustness interaction prediction is realized.

The application discloses a kind of generation image positive sample realizes the method, system, medium and equipment of image classification, belong to computer vision field.The application is first extracted and rewrites text semantics by multimodal model, enhances visual prompt, and then guide denoising diffusion model to generate the positive sample consistent with original image semantics, and uses contrast learning to carry out unsupervised training to image encoder, so that it accurately obtains semantic information;Using labeled feature vector trains linear classifier, to build image classification model for image classification task, finally can complete image class determination without a large number of labeled data.

The application relates to an end-to-end attribute-level sentiment analysis method based on a sentence component perception attention mechanism and belongs to the technical field of natural language processing. The application comprises the following steps: firstly, a pre-training language model is used to train a word vector. Secondly, a sentence component perception attention mechanism C-ATT is used to divide sentence components, words belonging to the same component in a sentence divided by the C-ATT pay attention to each other, and words of different components do not pay attention to each other, so that aspect words pay more attention to opinion words divided into the same component. Finally, a linear classifier is used to predict a joint label; experiments are conducted on four public sentiment analysis data sets, and the experimental results show that the end-to-end attribute-level sentiment analysis method based on the sentence component perception attention mechanism is effective.

This invention discloses an interpretable robotic arm bearing fault diagnosis method based on physical prior knowledge, belonging to the field of rolling bearing fault diagnosis technology. This method integrates the physical prior knowledge of the bearing with a neural network structure. It obtains fault frequency harmonic features with clear physical meaning and key frequency band broadband features through parallel physical feature extraction networks and broadband feature extraction networks, respectively. A multi-head attention mechanism with sparsity and diversity constraints is used to adaptively filter the fused features, highlighting key fault characterization information. Finally, a single-layer linear classifier achieves transparent fault category decision-making. This invention solves the "black box" problem of existing deep learning fault diagnosis models, providing end-to-end interpretability from feature contribution and attention allocation to classification decision-making while ensuring high diagnostic accuracy, significantly improving the model's reliability and engineering practicality.

This invention discloses a method, system, electronic device, and storage medium for processing chest images, relating to the field of image processing. The method includes: acquiring a multi-label real dataset and a single-label synthetic dataset; dynamically sampling and matching synthetic data batches based on the label distribution of the real data batches; inputting the two types of data batches into a dual-branch heterogeneous framework for cross-modal deep fusion to extract real and synthetic fused representations; using a category-specific parallel linear classifier for feature decoupling and outputting the prediction probabilities of each branch; constructing a mask based on multi-label and single-label vectors to calculate label-level collaborative distillation loss, and combining this with cross-entropy supervised loss to perform end-to-end training and optimization of the model; finally, using the trained model to output feature label prediction results for the image to be processed. This invention solves the algorithmic challenges of feature entanglement caused by the coexistence of multiple attributes and the scarcity of rare samples under long-tailed distributions, improving the accuracy of classification models in extracting pure features.

This invention relates to the field of artificial intelligence technology and discloses a speech extraction method, apparatus, device, and medium. The method includes: first, acquiring reference speech of the target speaker and mixed speech of all speakers; preprocessing and encoding the reference speech and mixed speech to generate two discrete token sequences; fusing the two discrete token sequences to form a fused discrete token sequence; using a language model to predict the fused discrete token sequence to generate candidate discrete token sequences for the target speaker; calculating the probability distribution of the candidate token sequences using a linear classifier and selecting the sequence with the highest probability as the target discrete token sequence; and then reconstructing the target discrete token sequence into a speech waveform to obtain the speech of the target speaker. This invention transforms the complex audio generation problem into a classification problem, simplifying model training; and utilizes the sequence modeling capability of a language model to capture long-term dependencies between speech tokens, achieving high-quality speech reconstruction.

Embodiments of the present disclosure provide functionality to tensor network framework designed for quantum kernel methods and demonstration of tensor network effectiveness at scaling this application. Quantum kernels capture the distance between data points in quantum feature space by evaluating the quantum state overlaps associated with each data point. It has been found that expressing data in quantum feature space may produce more separable data that improves the results of linear classifiers. The different kernel elements may be computed independently, and parallel processing may be exploited to significantly reduce computational time, enabling to train on more data. Thus, quantum kernels continue to improve classification metrics with the addition of more training data and more features.

The invention discloses an interpretable medical image intelligent diagnosis device, and the device comprises the steps: constructing a novel structured cue word template which is used for guiding a large language model to extract lesion features with a clinical identification value from massive world knowledge; a novel text feature extraction model is deployed, the model optimizes a feature extraction process by introducing learnable dynamic cue words, text information generated by the large language model is mined, and a text embedding vector with high characterization capacity is obtained; a novel visual feature extraction model is deployed, the model realizes multi-level visual feature fusion by designing a lightweight dynamic adapter, and a final visual embedding vector is generated; and calculating similarity scores based on the text embedding vectors and the visual embedding vectors, realizing disease prediction through a linear classifier, retrieving Top-K related text embedding vectors based on the similarity scores, matching corresponding text descriptions, and generating a visual diagnosis basis.

The application discloses a kind of electroencephalogram emotion signal recognition method based on graph regular non-negative matrix decomposition, comprising the following steps: step one, using nearest neighbor method for electroencephalogram emotion signal constructs an adjacent matrix;Step two, establish a graph regular non-negative matrix decomposition model, and the non-negative constraint of matrix after decomposition is carried out;Step three, introduce a projection matrix, further to matrix implement three decomposition;Step four, the model is optimized;Step five, using the new representation PX obtained after the multiplication of trained model parameter P and sample X replaces the representation matrix V of original model, joins corresponding label matrix and trains a classifier in linear SVM classifier;Step six, the class of test sample is predicted by inputting into classifier.The application can effectively identify electroencephalogram emotion signal, compared with other classic electroencephalogram emotion recognition method, the application effectively improves recognition rate.

The application provides an ESN log anomaly detection method based on low-rank linear subspace representation fine-tuning and a related device, and relates to the technical field of log anomaly detection. Raw log data is obtained and converted into target log sequences through log preprocessing; a preset base model is obtained, and the preset base model is adjusted through a low-rank linear subspace representation fine-tuning technology to generate a target model; the target log sequence is input into the target model to determine a feature vector; and a preset linear classifier is used to perform log anomaly detection according to the feature vector. By intervening in the representation of known encoding specific semantic information, the low-rank linear subspace representation fine-tuning method can deeply reveal the way in which the model processes and understands data, reduce the cost of model training, and improve the technical effect of model interpretability.

A system and method are provided for implementing transformer-based computer vision models on resource-constrained devices. An input image is divided into tokens, each corresponding to a patch. A background-aware vision transformer (BAViT) classifies tokens as foreground or background using a lightweight architecture without a class token and with a linear classifier for token-wise prediction. Training utilizes an accumulative cross entropy loss that aggregates token-level losses to improve accuracy. Tokens classified as background are pruned, thereby reducing computational complexity, runtime memory, and inference latency. Foreground tokens are processed in a downstream transformer-based object detection model, such as YOLOS, to generate detection outputs. The BAViT module operates as a pre-processing stage, facilitating integration with detection models without retraining. Configurations include BAViT-small with two transformer layers suitable for edge devices, supporting applications such as security and inventory tracking.

The invention relates to an overall guidance robust linear classification method for noise data, and belongs to the technical field of noise data classification and artificial intelligence data processing, and the method comprises the following steps: obtaining data to be processed; constructing a rescaled hinge loss function; constructing a target function based on the total statistical information of the training sample and the re-scaled hinge loss; converting the target function into an overall statistical information sub-problem and a re-scaling hinge loss sub-problem; and solving the overall statistical information sub-problem and the re-scaled hinge loss sub-problem to obtain an optimal solution of the target function, thereby obtaining a linear classifier RPC, and realizing classification of the to-be-processed data. The overall guided robust classification (RPC) method provided by the invention has obvious advantages in noise data, the classification accuracy and stability are obviously improved compared with other methods, and the requirements on the performance of the classifier in a noise data environment in practical application can be better met.

The application discloses a multi-period brain CTA collateral circulation scoring method based on feature fusion under an attention mechanism, and mainly relates to the technical field of computer-aided medical treatment; the method comprises the following steps: S1, converting an original CTA-dicom sequence into a pseudo RGB image; S2, designing four independent single-branch networks according to the data characteristics of multi-period data; S3, fusing multi-period CTA images and enriching data characteristics through the designed single-branch multi-feature fusion and multi-period multi-feature fusion modules; S4, adding a global attention mechanism module to the fused network to add a global feature extraction attention mechanism; and S5, inputting the screened fusion features into a linear classifier to classify the collateral circulation of a patient; the application effectively improves the prognosis judgment efficiency, avoids the limitations of manual extraction of image features in the traditional method, plays an auxiliary role in the diagnosis of doctors in the clinic, and has a guiding role for the clinical treatment decision of collateral circulation stroke.

This invention relates to a method, system, computer device, and storage medium for identifying foreign objects in greige fabric. The key technical points are as follows: The method includes: converting the RGB values ​​of a greige fabric image to HSV values; dividing the greige fabric image into a nine-grid layout, and calculating the LBP feature values ​​of the H, S, and V channels of the greige fabric image within each nine-grid layout in the HSV color space; rotating the LBP feature values ​​of the greige fabric image within each nine-grid layout to obtain corresponding rotation-invariant LBP feature values; drawing gradient histograms of the H, S, and V channels of the greige fabric image within each nine-grid layout based on the rotation-invariant LBP feature values ​​and performing normalization processing to obtain the feature vector of the greige fabric image; classifying the greige fabric image using an SVM linear classifier based on the feature vector to identify foreign objects in the greige fabric image; this application can identify foreign objects on greige fabric.

The application relates to a few-sample webpage fingerprint learning method, a terminal device and a storage medium, and the method comprises the following steps: assembling a pre-training data set and a few-sample data set; a comparison network is constructed, the comparison network comprises a data enhancement module, a feature extractor, a projection head module and a comparison loss module; the pre-training data set is used for training the comparison network, and a pre-training feature extractor is obtained; the original flow in the few-sample data set is subjected to the data enhancement module and the trained feature extractor, and the feature vectors of the enhanced flow of each sample in the few-sample data set are obtained; a linear classifier is constructed, the feature vectors are used as the input of the linear classifier, and the linear classifier is trained; the trained feature extractor and the trained linear classifier are combined to obtain a flow classification model, and the flow classification model is used for fingerprint identification of the flow of a webpage to be monitored. The application can quickly train a new classifier by collecting a small amount of flow, and the accuracy of webpage fingerprint identification is ensured.