408 results about "Multi layered perceptron" patented technology

A method and apparatus for reconstructing a three-dimensional (3D) scene from a two-dimensional (2D) input image of the scene using a fully-differentiable transformer-based encoder-decode. A 2D input image encoded into a set of image features using a pre-trained vision transformer model, wherein the vision transformer model is pre-trained with multi-view RGB image supervision and point cloud supervision. The set of image features is projected onto a 3D triplane representation using a transformer decoder to obtain output triplane tokens. A triplane representation is created from the tokens and queried. 3D point features of color and density for volumetric rendering re predicted using a multi-layer perceptron. The geometry of the generated 3D asset is represented with a surface mesh including vertices and triangular faces. A texture map by is created with a multichannel image in UV space. Multiple views of the 3D scene are simultaneously generated based on the surface mesh.

The invention discloses a double-flow time convolution enhanced interactive bearing life prediction method, which comprises the following steps of: acquiring original bearing vibration signal data, and processing and reconstructing the data; constructing a time flow feature extraction module by fusing the local time sequence modeling capability of the TCN and the multi-scale expansion attention, dynamically adjusting the expansion rate through the spectrum entropy, and capturing multi-scale local features by combining sparse multi-head attention; a spatial stream feature extraction module is constructed by fusing frequency sensing position coding and a layered sparse attention mechanism; designing a bidirectional cross-layer attention collaboration mechanism, performing feature interaction on the time flow feature extraction module and the spatial flow feature extraction module, enhancing the degradation characterization capability through hierarchical feature alignment and dynamic weight adjustment, and constructing a double-flow feature extraction layer; inputting the reconstructed signal into a double-flow feature extraction layer to extract features; inputting the extracted features into a multi-layer sensor to carry out RUL prediction; local-global feature complementation is realized; and prediction stability is improved.

The invention discloses a structure perception graph neural network physical field prediction method based on a geometric gating attention mechanism, and belongs to the technical field of artificial intelligence and computational physics crossing. The method comprises the following steps: extracting geometric features for standardization processing and organizing in a graph data structure; inputting the edge-level geometric features into a geometric gating function, generating a dynamic adjustment factor through a multi-layer perceptron, obtaining an attention weight after geometric modulation, and performing normalization processing to obtain output features of nodes; splicing the output features of the plurality of attention heads as final output features of the model; training the graph neural network model to obtain a physical field prediction model; and inputting the geometric features of the three-dimensional structure model to be predicted into the physical field prediction model. According to the method, the precision and physical consistency of complex physical field prediction are improved, and the problems of boundary blur, field intensity sudden change and the like caused by neglecting geometric dynamic change in a traditional static coding method are reduced.

The invention relates to the technical field of intelligent medical systems, and discloses a cerebral apoplexy onset risk assessment and reminding method and system.The method comprises the steps that continuous medical structured detection data are collected, and the data comprise carotid artery blood flow parameters, brain oxygen saturation, heart rate variability and metabolic indexes; inputting a bidirectional LSTM, a differential convolutional network, a wavelet residual network and a multi-layer perceptron to extract nonlinear features; constructing a neural function coupling structure diagram of four nodes of cerebral blood supply, oxygen supply, autonomous regulation and metabolic steady state; calculating inter-node time sequence offset correlation and a stable factor to obtain a coupling anomaly coefficient; and driving the embedded network by using a graph structure and a node feature input mechanism, and outputting a risk state assessment result. According to the method, the neural function coupling structure diagram is constructed and the mechanism is introduced to drive the embedded network, so that high-precision identification of the multi-system collaborative abnormal state and dynamic evaluation of the stroke risk level are realized.

The invention discloses a snake-shaped robot control method based on reinforcement learning. The snake-shaped robot control method comprises the steps that a complex terrain simulation scene comprising walls, cubic obstacles and low channels is built; modeling is conducted on the structure, joints and kinetic parameters of the snake-shaped robot, and a snake-shaped robot simulation model is obtained; defining a state space and an action space of the snakelike robot, and designing a diversified reward function; constructing an action network and a value network by adopting a multi-layer perceptron (MLP) structure; and performing multi-round simulation training on the snakelike robot by applying a reinforcement learning algorithm, so that the robot learns a parameter set for making an optimization decision under different scenes, thereby realizing optimization control of the snakelike robot and reaching a target point. According to the method, visual information does not need to be depended on, efficient and stable motion control of the snake-shaped robot in a complex environment can be achieved through autonomous learning, and the crossing capacity of the snake-shaped robot in a low and narrow space is improved.

The invention discloses a drug resistance prediction method and system based on comparative learning and multi-modal fusion, and the method comprises the steps: firstly generating a molecular map and a molecular fingerprint based on the SMILES of a target drug, and extracting the molecular features of the drug through a comparative learning model constructed through combining a map attention network and a map convolution network; and then, acquiring protein expression, gene expression and metabolic expression data from the target tissue cells, extracting modal features through a deep convolutional network, a Transform encoder and a multi-dimensional attention network, and realizing adaptive fusion of the multi-modal features through a heterogeneous interactive attention mechanism. And finally, jointly inputting the fused multi-modal features and drug molecular features into a multi-layer sensor to realize high-precision prediction of the drug resistance of cells to drugs. By introducing a contrast learning and multi-modal feature fusion mechanism, the characterization capability and prediction precision of the model are effectively improved, and efficient and reliable support can be provided for drug screening and clinical decision making.

The invention relates to the technical field of electronic detection, and discloses an electronic component dispensing detection method. The method comprises the following steps: acquiring dispensing image data of the surface of the electronic component, and generating a standardized dispensing data matrix containing glue position, thickness and uniformity characteristics through standardized preprocessing; constructing a dispensing correction matrix based on an adaptive window frame, and performing spatial reference dynamic correction on the standardized data matrix to obtain a spatial correction dispensing data matrix; inputting the data into a multi-layer sensor fusion network for feature fusion, and outputting a multi-source fusion dispensing data set; constructing a multi-dimensional abnormal feature incidence matrix based on the data set, and identifying abnormal dispensing data nodes by using a dynamic threshold detection algorithm; performing parameter optimization iteration on the multi-source fusion data set by using a gradient descent optimization algorithm to generate an optimized dispensing parameter set; and finally, constructing a three-dimensional visual dispensing quality model, and establishing a dynamic mapping relationship between model parameters and glue physical characteristics. The method can more comprehensively detect the glue quality.

The invention discloses a single-view three-dimensional dressed human body reconstruction method based on pixel and voxel feature fusion. The method comprises the steps of data set preprocessing, data set dividing, three-dimensional dressed human body reconstruction network construction, three-dimensional dressed human body reconstruction network training, three-dimensional dressed human body reconstruction network testing and reconstruction result displaying. In the pixel feature extraction network, the coordinate attention and the stacking hourglass network are combined, so that the pixel feature extraction precision is improved; in the voxel feature extraction network, a three-dimensional channel and space attention are introduced into the voxelization human body parameterization model network, and a multi-scale residual feature extraction module is combined to extract voxel features. And splicing the extracted pixels and voxel feature vectors, inputting the spliced pixels and voxel feature vectors into a multi-layer perceptron, and reconstructing a three-dimensional dressed human body model through Marking Cubes, thereby solving the technical problem of insufficient precision of the existing single-view three-dimensional dressed human body reconstruction method, and being applicable to the technical fields of artificial intelligence and computer vision.

The invention discloses an oil mist particle trajectory control method and system in oil mist separation, and relates to the technical field of intelligent control, and the method comprises the steps: collecting the three-dimensional position and velocity vector of oil mist particles in real time, and carrying out the three-dimensional modeling of an oil mist trajectory; taking the deviation between the actually measured track and the ideal track as a training sample, inputting the training sample into a multi-layer perceptron, and outputting a current error vector and a compensation increment; constructing a multi-target evaluation function based on the compensated position target and the safe energy consumption factor; optimizing the control parameters by adopting a gray wolf optimization algorithm with improved congestion degree; and issuing the optimized parameters to an execution system to automatically adjust corresponding values. By means of the method, high-precision online control over oil mist particle movement is achieved; the separation efficiency and the energy consumption are effectively balanced; the system has self-adaptive closed-loop capacity, parameters can be quickly adjusted when working conditions change, and separation stability and equipment safety are guaranteed; manual intervention is reduced through automatic execution, and operation reliability and production efficiency are improved.

The invention discloses an encrypted network traffic classification method based on a structured state space dual model in the technical field of network information security, and aims to solve the optimization problem of encrypted network traffic classification. The method comprises the following steps: acquiring a pcap data packet in encrypted network traffic; extracting hierarchical flow information in the flow packet and generating a stride sequence; inputting the stride sequence into a pre-trained encrypted network traffic classification model, and outputting a classification result; the training of the encrypted network traffic classification model comprises the following steps: carrying out self-supervised pre-training on unmarked data by using a mask auto-encoder, and learning universal representation of traffic data; and replacing a model decoder with a multi-layer perceptron head, and performing fine tuning optimization by using mark data. According to the method, the reasoning speed is improved while low GPU memory use is kept, and encrypted traffic classification can be more efficiently and effectively achieved.

The invention relates to the technical field of bioinformatics and computational biology, and provides an anti-cancer drug target screening method and system based on hypergraph-knowledge graph dual-channel, a hypergraph-knowledge graph dual-channel architecture is constructed through a feature extraction module, a self-adaptive multi-modal fusion module dynamically integrates multi-source heterogeneous data, and the anti-cancer drug target screening method and system based on the hypergraph-knowledge graph dual-channel architecture are obtained. The method comprises the following steps: adaptively fusing high-order interaction features and semantic association features of a gene, generating a high-quality negative sample by using a generative adversarial network to optimize a training process, finally optimizing feature representation through a discriminator, and calculating a synthetic lethal probability score between any gene pair. According to the application, firstly, the extracted multi-dimensional features are normalized through the feature extraction module, then the importance of different features is dynamically weighted based on the attention mechanism, finally, the synthesis lethal relationship prediction probability of the gene pair is output through the multi-layer sensor, and under the theoretical framework of the gene synthesis lethal effect, the synthesis lethal effect of the gene pair is predicted. And an innovative solution is provided for anti-cancer drug target screening.

The invention discloses a composite insulator defect detection method and system based on a multi-source signal and VMD decomposition, and the method is characterized in that the method belongs to the technical field of power system and equipment monitoring, and comprises the steps: synchronously collecting an ultrasonic echo signal, a mechanical vibration signal and an infrared thermal image signal of a composite insulator, and carrying out the timestamp alignment; performing CEEMDAN denoising and VMD decomposition on the ultrasonic echo signal in sequence to extract time-frequency domain features, and generating an ultrasonic feature vector; performing frequency domain transformation on the mechanical vibration signal to extract fundamental frequency and harmonic characteristics, and generating a vibration characteristic vector; performing surface temperature abnormal feature extraction on the infrared thermal image signal to generate an infrared auxiliary feature vector; the ultrasonic feature vector, the vibration feature vector and the infrared auxiliary feature vector are spliced into a fusion feature vector, and after dynamic weight distribution, the fusion feature vector is input into a multi-layer perceptron model for defect classification; and analyzing and positioning the internal defect position of the composite insulator by combining the ultrasonic signal propagation path time delay and the vibration mode, and generating a three-dimensional visual report.

The invention discloses a one-dimensional plasma simulation method based on deep learning optimization and related equipment, and the method comprises the steps: firstly constructing a one-dimensional basic model containing specific parameters, and then constructing and training a neural network of a multi-layer sensor (MLP) structure for predicting a potential function; then, a particle simulation method (DL-PIC) based on deep learning optimization is executed. Under the condition that 1000 grid points are included, compared with a traditional particle simulation method (PIC), the simulation time of the DL-PIC neural network is shortened by about 33.37%. According to the invention, complex physical phenomena such as reflection oscillation caused by heat effect and space charges can be effectively processed, and the method has good expansibility. The method is not only suitable for one-dimensional simulation, but also can be popularized to higher-dimension and electromagnetic particle simulation. Therefore, the invention provides an efficient and accurate solution for plasma simulation research, and can be widely applied to the technical field of plasma simulation.

A machine-learned architecture for determining whether an object is relevant to a vehicle's action planning may comprise a convolutional neural network, graph neural network, and / or multi-layer perceptron that may determine a relevance score associated with an object that indicates indicating whether an object is likely to impact operation(s) of a vehicle. In some examples, the machine-learned architecture may use scene information and / or an object track to determine the relevance score.

Described herein are systems, methods, and instrumentalities associated with using a multi-layer perceptron (MLP) neural network to process medical images of an anatomical structure. The processing may include padding an input image in accordance with the training of the MLP neural network, splitting the input image (e.g., the padded input image) into patches of a same size, and processing the patches through the MLP neural network over one or more iterations. During an iteration of the processing, the patches may be processed separately and re-combined into an intermediate image before the intermediate image is shifted to concatenate portions of the image that are derived from different patches. This way, global features of the anatomical structure may be learned and used to improve the quality of the image generated by the MLP neural network, without incurring significant computation or memory costs.

The invention discloses a multivariable time sequence prediction method and system based on an implicit neural network. The method comprises the following steps: 1) collecting data and preprocessing the data; 2) performing window division on the standardized or normalized multivariable time sequence and determining the length of a to-be-predicted window; 3) performing variable correlation coding on the input window to obtain a variable-level feature vector; 4) the implicit neural network based on time attention predicts target parameters by using the variable features in the step 3), and implicit neural representation of the target sequence is modeled through the parameters; 5) taking the output of the implicit nerve representation and the original input window as the input of the multi-head attention predictor, and obtaining a prediction result through cross-sequence cross attention calculation performed in the implicit space and multi-layer perceptron conversion output dimension; and 6) training and optimizing model parameters, calculating a mean square error of a prediction result and a real result, taking the mean square error as a loss function, carrying out back propagation to optimize trainable parameters of the variable correlation coding module, the implicit neural network module multi-head attention predictor and the multi-layer perceptron, and then repeating the steps 3) to 6) to obtain the multi-head attention predictor. Until the preset number of iterations is reached or the error of the model on the verification set meets the requirement of early stop; and 7) performing prediction by using a model of training convergence, and performing reverse normalization on a prediction result to obtain a final prediction result. The method has good generalization, and meanwhile, the interpretability of the attention mechanism is remarkably improved by generating the hidden space characteristics of the trend component and the season component.

The invention discloses a model training method and device and electronic equipment. The method comprises the steps that a training data set is acquired, the training data set is used for training a pre-training model, and the training data set comprises image data and corresponding text data; the training data set is sequentially input into the multiple multi-layer perceptron networks in the pre-training model for training, multiple target multi-layer perceptron networks are obtained, and the output result of the previous multi-layer perceptron network serves as the input of the next multi-layer perceptron network; fusing the plurality of target multi-layer perceptron networks to obtain a fused network; and training the fusion network according to the training data set to obtain a target fusion network, and determining a target pre-training model according to the target fusion network. According to the method and the device, the technical problem of poor accuracy due to the fact that a visual encoder model ignores many detail information in the encoding process in the prior art is solved.

The invention relates to the technical field of tumor prognosis prediction, in particular to a chest tumor prognosis prediction method and system based on data analysis, and the method comprises the following steps: S1, data collection: collecting multi-modal data of a chest tumor patient; s2, data preprocessing: preprocessing the collected multi-modal data; s3, prediction of tumor immune cell infiltration: predicting the infiltration degree of tumor immune cells, and evaluating the influence of the infiltration degree on patient prognosis; and S4, prognosis analysis and prediction: performing comprehensive analysis through a multi-layer perceptron algorithm to generate prognosis prediction results of the tumor patient, including tumor metastasis risk, recurrence probability and immunotherapy response. According to the invention, the complexity of the tumor immune microenvironment can be better captured, so that more accurate prognosis evaluation is provided, and clinical doctors are helped to understand the potential influence of immune response on tumor metastasis, recurrence and immunotherapy response.

The invention provides an atrial fibrillation postoperative recurrence prediction method fusing electrocardiosignals and clinical characteristics. The method comprises the following steps: acquiring data of a patient before an ablation operation, and carrying out resampling, denoising and normalization preprocessing and data segment segmentation on an electrocardiosignal; extracting spatio-temporal features by using a deep network containing a residual convolutional block and a long and short term memory module; screening high-discrimination clinical baseline features through statistical analysis and a machine learning model; extracting time-frequency domain and nonlinear features of short-time heart rate variability; designing a cross-modal attention fusion module to carry out feature adaptive weighted fusion; and outputting a recurrence probability through a multi-layer perceptron based on the fusion features. The method improves the prediction precision through feature complementarity, facilitates the recognition of high-recurrence-risk patients, is suitable for sinus heart rhythm signals or atrial flutter and atrial fibrillation signals, and has a certain application value in the field of cardiovascular precision medical treatment. The method can be popularized to all prediction researches based on the electrophysiological signals.

The invention discloses a point cloud data-based steel structure virtual assembly prediction method and system, and relates to the technical field of steel structure virtual assembly prediction, and the method comprises the steps: collecting point cloud data, carrying out the preprocessing, dividing the point cloud data into subsets, calculating the geometric characteristics of the subsets according to the divided subsets, and carrying out the optimization based on the geometric characteristics, and obtaining an optimal feature vector; performing category classification based on the optimal feature vector, further analyzing component features through a classification result, constructing a path optimization model, and taking the optimal feature vector as input to obtain a final assembled path sequence; according to the method, the particle swarm optimization algorithm is combined with the weighted fusion method, high-precision extraction of the geometric characteristics of the point cloud is achieved, the classification and fitting precision is remarkably improved through covariance matrix characteristic decomposition and linear, plane and spherical surface characteristic construction, and then a path optimization model with the combination of the fire-fisch algorithm and the multi-layer perceptron is utilized, so that the classification and fitting precision is improved. And the error, the length and the stability of the assembly path are comprehensively optimized.

The invention relates to the field of noninvasive prenatal diagnosis, and particularly discloses a preeclampsia noninvasive screening method based on deep sequencing 8bp oligonucleotide double-fragment characteristics, which comprises the following steps: collecting preeclampsia and healthy pregnant woman peripheral blood samples, and extracting free DNA for high-throughput sequencing; the method comprises the following steps: extracting core 8-mer sequences' GTGCGCCC 'and' GATGGGGT 'in a long fragment of 150-200bp through bioinformatics analysis; an integrated support vector machine, K-nearest neighbor, extreme gradient lifting, a random forest and a multi-layer perceptron are combined with a logistic regression element classifier to construct a stacking model, the frequency of a core sequence is normalized, machine learning analysis is carried out, and the preeclampsia risk is predicted. According to the invention, two 8bp oligonucleotide characteristic fragments are specifically screened, and a deep learning architecture of multi-model fusion is combined, so that the limitations of low specificity and invasive detection of a traditional screening method are effectively broken through.

The invention provides a DDQN unmanned aerial vehicle interruption scene path planning method based on an attention mechanism, and relates to the technical field of path planning. The method comprises the following steps: constructing a comprehensive objective function and a distribution network diagram according to node position information, energy consumption of a unit distance of the unmanned aerial vehicle and a comprehensive interruption risk between nodes; a graph attention network and a multi-layer perceptron are adopted to carry out feature extraction on the distribution network graph and the current state, current graph embedding features and current state features are obtained, the extracted features are spliced and input into a main network, and a predicted Q value, a reward value and an optimal action are obtained; the optimal action is sent to the unmanned aerial vehicle, the next state fed back by the unmanned aerial vehicle is received, the main network is circularly utilized to continuously obtain the optimal action, and finally an optimal path planning strategy is formed; and the path planning success rate and adaptability of the unmanned aerial vehicle under the interference of the multi-source dynamic environmental factors are remarkably improved.

The invention discloses a frequency domain-spatial domain multi-scale feature fusion complex marine environment ship fine-grained identification method, which comprises the following steps: acquiring a ship image to be identified and preprocessing the ship image to obtain a preprocessed ship image; constructing a frequency domain-spatial domain multi-scale feature fusion ship identification model; inputting the preprocessed ship image into a frequency domain-spatial domain multi-scale feature fusion ship identification model for processing, and outputting a ship fine-grained identification result; wherein the frequency domain-spatial domain multi-scale feature fusion ship identification model is obtained by extracting low-frequency features and high-frequency features of ship images through multistage wavelet transform, generating regional feature distribution and aggregation weights by using a learnable multilayer perceptron, optimizing extraction of ship fine-grained features, and training and verifying through a public data set. The public data set is ship image data and public data used for ship target fine granularity identification. According to the invention, the accuracy and robustness of ship identification are improved.

The invention discloses a microwave digestion-liquid flash spectrometer strontium isotope rapid detection system for multiple types of samples. The automatic sample injection module automatically identifies, grabs and conveys a sample by means of an image and radio frequency identification technology. The microwave digestion module is provided with a self-adaptive program, temperature and pressure are dynamically regulated and controlled through a PID algorithm according to sample characteristics, and efficient and safe digestion is ensured. The automatic separation and purification module intelligently selects a solid-phase extraction column or an ion exchange resin column to purify a sample by matching spectral analysis with a database. The liquid flash spectrometer detection module accurately collects signals by means of a high-sensitivity element and an algorithm. The data processing and control system adopts a multi-layer perceptron neural network, so that the strontium isotope content can be intelligently judged, and the source of abnormal data can be traced. In addition, the system has the functions of automatic cleaning and maintenance, emergency detection flow and remote monitoring, all the modules are connected through standardized interfaces, and the expansibility is high. The system is high in automation degree and accurate in detection, and an efficient and reliable scheme is provided for strontium isotope detection.

The invention relates to the technical field of image recognition. The invention discloses a human body posture estimation method and device, equipment and a storage medium. The continuity and accuracy of three-dimensional human body posture estimation can be improved. The method comprises the following steps: acquiring a video sequence; inputting the video sequence into a video segmentation module for segmentation processing to obtain a plurality of video clips; the target video clip is input into a two-dimensional human body posture estimator for feature extraction processing, two-dimensional human body posture information and image features of multiple resolutions are obtained, and the target video clip is any one of the multiple video clips; the two-dimensional human body posture information and the image features are input into a human body posture estimation model for compensation processing, and target fusion features are obtained; inputting the target fusion feature into a first multi-layer perceptron for attitude estimation processing to obtain a three-dimensional human body attitude estimation result corresponding to the target video clip; and combining all three-dimensional human body posture estimation results to form a three-dimensional human body posture estimation sequence.

The invention relates to the technical field of artificial intelligence and audio processing, can be applied to the field of intelligent medical treatment and financial science and technology, and discloses an audio watermark processing method and device, equipment and a medium, and the method comprises the steps: carrying out the coding of continuously inputted audio blocks, and obtaining an audio feature vector; acquiring a digital watermark, and performing time modulation on the digital watermark according to the audio lengths of the blocks by using a multi-layer perceptron to obtain a watermark implicit vector; embedding the watermark implicit vector into the audio feature vector to obtain a target audio vector; performing quantization processing on the target audio vector to generate a target audio spectrum embedded with a digital watermark; and decoding the target audio frequency spectrum to obtain a target audio. According to the method, the continuously input audio blocks are coded, the method can adapt to a streaming scene, the audio data are processed segment by segment, the watermark embedding requirement of the continuous audio stream is met, the audio can be compressed, the data volume is effectively reduced, the transmission time consumption is reduced, and the low-delay communication requirement is met.

The invention relates to the technical field of new energy power generation prediction, and particularly provides an ultra-short-term solar irradiance prediction method and device based on a satellite cloud atlas, and the method comprises the steps: obtaining the prediction time period of a prediction result and an error evaluation index of a parameter type relative to each pre-trained multi-layer perceptron model; selecting a pre-trained multi-layer perceptron model with the minimum error evaluation index to predict the prediction result; wherein the parameter types comprise global level irradiance, direct normal irradiance and diffusion level irradiance. According to the technical scheme provided by the invention, model optimization selection is carried out for different irradiance types, and the adaptability and accuracy of prediction are greatly improved.

Systems and methods for optimizing a trained neural network model are provided herein. A trained neural network model is obtained. The trained neural network model comprises a plurality of neurons. A plurality of output information of the hidden layers is received by performing a simulation of the trained neural network using a dataset. A plurality of mean activation values corresponding to neurons of the plurality of hidden layers is calculated, based on using a hyperbolic tangent (tanh) activation function. A subset of mean values corresponding to a plurality of ranges is calculated. A plurality of neurons and a plurality of associated layers corresponding to the subset of mean values is modified. An optimized neural network model that implements the modified plurality of neurons and the modified plurality of associated layers is generated.

The invention discloses an AI generated picture blind box detection method and device based on semantic consistency difference. The method comprises the steps that a corresponding descriptive text is generated for a to-be-detected picture based on a pre-training multi-modal large model BLIP of freezing parameters; and embedding an image-text pair composed of a to-be-detected image and a descriptive text corresponding to the to-be-detected image into the joint representation space by using a multi-modal model CLIP, and extracting image-text features. A detection model is constructed through two-stage rumination training: in the first stage, image-text features are spliced and then input into a first multilayer perceptron MLP1, and AI generation confidence is output; and in the second stage, the confidence coefficient is used as a regulation coefficient, the image-text feature cosine similarity is adaptively adjusted in combination with a loss function proposed by the scheme, iterative optimization is carried out in a submerged space, and regulated joint features are generated and input into a second multilayer perceptron MLP2 for final judgment. The method does not need to predict a picture source model, has high universality and detection precision, and is suitable for AI generated image recognition in a complex scene.

The invention relates to the technical field of medical image segmentation, and discloses a meibomian gland segmentation method based on a meibomian gland infrared image, and the method comprises the steps: inputting a to-be-segmented meibomian gland image into an encoder of a trained infrared image segmentation model, enabling the to-be-segmented meibomian gland image to pass through M encoding modules in sequence, obtaining encoding feature maps extracted by the M encoding modules, and obtaining a meibomian gland segmentation model; a global feature aggregation module is input, a deep feature map and a shallow feature map are extracted, after an attention feature map is obtained through a multi-head cross attention mechanism, the attention feature map and the deep feature map are added, and a deep attention feature map is obtained; performing layer normalization and multi-layer perceptron processing on the deep attention feature map in sequence, adding the processed deep attention feature map with the deep attention feature map to obtain a global feature map, and inputting the global feature map into a decoder; in each decoding module, performing jump connection on the input feature map of the module and the encoding feature map output by the corresponding encoding module, and outputting a decoding feature map; and carrying out convolution on the decoding feature map output by the last decoding module to obtain a segmentation result map.