30 results about "Learning by example" patented technology

The invention provides a weak supervision video anomaly detection method and system based on prototype orthogonality, and the method comprises the steps: inputting a visual feature sequence into a selective state space model, so as to filter redundant information in a time sequence, capture a key dynamic state, and output a high-value time sequence feature; then, a learnable prototype codebook containing a normal category prototype codebook and an abnormal category prototype codebook is given, end-to-end training is carried out by adopting a multi-instance learning framework of a video-level label, prototype orthogonality constraint is applied in the training process, and after training is completed, the normal category prototype codebook and the abnormal category prototype codebook are subjected to end-to-end training; according to the method, only the distance between the high-value time sequence feature and the nearest prototype in the abnormal category prototype codebook is needed, and the video anomaly score is obtained according to the distance, so that video anomaly detection is realized. According to the method, the global geometric constraint of prototype orthogonality is introduced, and the selective state space model (S3M) with efficient calculation is combined, so that the extremely light weight of the model is realized while the high detection precision is ensured.

The application relates to the field of computer vision processing, in particular to a video retrieval method based on a deep neural network model and multi-example learning, which comprises the following steps: obtaining initial features by pre-training a query text, extracting I 3D-RGB features, ROI features and connection features from a video; updating frame-level visual features and word-level text features; constructing a graph for training, learning word-level text features by using a graph attention network; calculating the residual error of the word-level text features and the word-level text features, and taking the mean value of the residual error as a sentence-level text feature; performing segment dimension average operation on the frame-level visual features to obtain pipeline-level visual features; calculating the alignment score of the sentence-level text features and the pipeline-level visual features, constructing positive sample pairs and negative sample pairs, and training a video retrieval network; and the application constructs a graph neural network by acquiring discriminative features in multiple query texts through deep learning features, so as to provide text features with more representation meanings and multi-modal alignment supervision signals under weak supervision settings.

Techniques and architecture are described for converting tree structured data such as, for example, JavaScript Object Notation (JSON) data, into multiple feature vectors to train multiple instance learning (MIL) models for providing cybersecurity in networks. In particular, a data set is provided, wherein the data set comprises a sample configured as a hierarchal tree. The sample is converted into a set of path and value pairs, e.g., flattened into a set of path and value pairs, where the path is a sequence of field names and array indices encoding a position of a value. Each path and value pair of the set of path and value pairs is converted into a respective feature vector to form a set of feature vectors. The set of feature vectors is used to train a multiple instance learning (MIL) model, wherein each feature vector has a same, fixed length.

This invention discloses a method and system for identifying encrypted traffic based on spatiotemporal features and semantic alignment. The method first extracts the spatial and temporal feature sequences of the network flow, and uses a byte-pair encoding algorithm to convert the spatial packet length into discrete symbols. Then, the discrete symbols and temporal features are mapped to a high-dimensional space and fused together. A global spatiotemporal feature vector is extracted through a network using a concatenated one-dimensional convolution and multi-head self-attention mechanism. Next, the text semantic bullseye matrix of fine-grained behaviors of various known applications is obtained offline from a large language model. Finally, the similarity between the spatiotemporal features and the text bullseye is calculated, and a multi-instance learning max-pooling mechanism is introduced for dynamic routing. Based on this, a contrastive learning loss function optimization model is constructed or cross-modal inference is performed. This invention completely overcomes the conceptual drift problem caused by changes in encrypted features, achieving extremely high generalization accuracy and feature interpretability across generations.

This invention provides an online monitoring method and system for airborne flight runaway precursors of civil aircraft based on flight test data. The method includes: constructing a set of input flight parameters for identifying airborne flight runaway precursors; acquiring daily operational data and flight test data of the target aircraft model; and extracting physical feature information of airborne flight runaway by referencing the aerodynamic mechanism model and extreme flight envelope boundary of the target aircraft model. Based on the domain adaptation concept, an offline precursor recognition model is constructed that integrates physical feature information embedding, meta-learning, and multi-instance learning. Based on knowledge distillation technology, the recognition capability of the offline precursor recognition model is transferred to a lightweight network constructed from gated recurrent units to generate an online precursor monitoring model, enabling real-time precursor probability calculation and early warning. Finally, an intelligent agent model based on a dual-delay deep deterministic policy gradient algorithm is constructed to verify the effectiveness of the online precursor warning. This invention overcomes the cross-domain data gap and meets the requirements of online lightweight computation.

The invention discloses an open-pit mine slope stability index construction method based on dynamic and static data fusion. The method comprises the following steps: acquiring slope dynamic monitoring data of a site, and inputting the slope dynamic monitoring data into a convolutional neural network for processing to obtain a feature map; a data set of the multi-instance learning model is a dynamic feature instance package, the multi-instance learning model outputs a prediction result of the dynamic feature instance package and converts the prediction result into a softmax probability, and a stability level is obtained; obtaining slope static monitoring data, inputting the slope static monitoring data into the GBDT model to obtain a prediction result, and converting the prediction result into a softmax probability to obtain a static stability index of a corresponding station; and the multi-layer perceptron MLP combines the stability level and the static stability index into an input vector to carry out stability level prediction processing so as to obtain a final comprehensive prediction result. According to the slope stability evaluation method, the multi-example learning model, the GBDT model and the multi-layer perceptron MLP are combined, the efficient and accurate slope stability evaluation method is formed, and high-timeliness and high-precision slope stability evaluation can be provided through fusion of dynamic and static data.

Disclosed embodiments of the present invention provide a computer-implemented method for predicting peptide binding and presentation by MHC molecules, the method comprising: collecting or generating training data comprising a set of MHC molecules present in a biological sample and a set of observed peptide sequences presented by at least one of the MHC molecules present in the biological sample, where it is unknown to which particular MHC molecules the peptide sequences bind, and the training data is organized as a plurality of bags, each bag having a set of training instances, where labels are known for the bags but unknown for the training instances; θ We train the MIL classifier f at the instance level and then apply the labels of multiple new instances to the MIL classifier f θ and / or predict the labels of multiple new bags by directly applying the MIL classifier f θ and making predictions by applying the criterion 1 to each instance in each bag and aggregating the results across all instances in each bag.

The application belongs to the technical field of image recognition, and discloses a weakly supervised interstitial lung disease lesion recognition method based on multiple example learning, which comprises the following steps: step 1: acquiring CT image samples; step 2: selecting part of the CT images, and manually labeling the lung parenchyma in the images; step 3: establishing a lung parenchyma segmentation model through a saliency segmentation algorithm, inputting the manually labeled CT image samples to perform training and testing, and obtaining a trained lung parenchyma segmentation model; step 4: training a lesion recognition model using a multiple example learning algorithm; step 5: acquiring a to-be-recognized CT image sample, inputting the lung parenchyma segmentation model to perform segmentation, and then inputting the segmented data sample into the lesion recognition model to obtain a lesion position. The application can realize the visual labeling of interstitial lung disease through a small amount of labeling, and greatly improves the recognition efficiency.

This invention relates to a method and system for detecting unbalanced malicious traffic based on coarse-grained data labels. The method includes: S1: collecting malicious traffic data and normal traffic data at different time periods to construct a dataset, labeling each dataset with a coarse-grained label y, and labeling each data point x... i S2: Inherit the label y of its data set; i Multiple clustering algorithms were used to cluster malicious traffic data according to different sub-features, and the estimated score s was calculated. i S3: x i Dense vector representation of e i Input a multi-instance learning model and output each x. i The predicted value p i , using s i Construct a loss function to train a multi-instance learning model; S4: Based on p i Updates i When p i If the traffic is malicious, then increase the value by s. i The value is s, and vice versa. i The method provided by this invention only requires coarse-grained data labeling and utilizes estimated malicious data scores to achieve a malicious data prediction accuracy of the multi-instance learning model that approaches the performance of the supervised learning model with fine-grained data labeling.

The invention discloses a full-section pathological image classification method based on diffusion model feature transformation, and relates to the field of computational pathology. The method comprises the following steps: acquiring a paired HE dyeing image and IHC dyeing image, and extracting image features thereof; building a dynamic feature conversion network model, wherein the model comprises a feature encoder and a cross-modal dynamic diffusion module; the feature encoder comprises an HE stream and an IHC stream which are respectively used for extracting HE features and IHC features; and the cross-modal dynamic diffusion module takes the HE features as conditions, ensures the consistency of diagnosis semantics by comparing semantic bridging strategies, adaptively processes cross-modal distribution differences by using the frequency domain expert hybrid module, and finally generates target IHC features through a conditional denoising diffusion process. According to the feature conversion method, the IHC features with high quality and consistent semantics can be generated, the classification performance of a multi-instance learning framework is remarkably improved, an intermediate pixel image does not need to be generated, the calculation efficiency is high, and a new normal form is provided for calculation of biomarker prediction.

This invention discloses a multimodal data scene recognition method based on multi-level interactive fusion. Using video data and vehicle data collected by onboard sensors in autonomous driving scenarios, three single-modal features are extracted: 2D-level features from the video are obtained through multi-instance learning based on a two-stage attention mechanism; 3D spatiotemporal features from the scene video are extracted through a multi-layer spatiotemporal attention network, and onboard information feature vectors are added for training and interaction; and the onboard information feature vectors are trained. After feature extraction of the three modalities, similarity loss is calculated. During training, the similarity of the three modalities is maximized, and the features of the three modalities are interacted based on a multi-layer self-attention network. Finally, classification is performed. This method can utilize existing video and onboard information interaction to supplement information and improve the speed and accuracy of scene recognition.

The invention provides a hierarchical MIL (Multi-Example Learning) WSI (Whole Slide Image) classification method based on pseudo-bag hybrid enhancement, which can solve the problems of high calculation complexity and insufficient model generalization ability in the existing WSI classification. According to the method, firstly, WSI is divided into a plurality of pseudo bags through phenotypic clustering and layered sampling, then enhanced data is generated based on a pseudo bag mixing strategy, and finally learning and classification are carried out by adopting a multi-level architecture. A pseudo bag mixing strategy is combined with a multi-level framework, and through cross-sample semantic alignment, label smoothing and level compatibility, the problems of small samples, high noise and calculation complexity in WSI data can be effectively solved, and the accuracy of a result is improved.

The application discloses a long video retrieval method and device based on multi-scale multi-example similarity learning. The method acquires video and text preliminary features; uses coarse-to-fine coding mode to extract information of different time granularities from video segment scale and frame scale; based on video representation of two scales, uses segment scale similarity learning branch to filter out video segments most relevant to the text and obtain segment scale similarity; uses frame scale similarity learning branch to aggregate video features guided by the filtered most relevant video segments to obtain more detailed video information, and after similarity calculation with the text, frame scale similarity is obtained; a common space learning algorithm is used to learn multi-scale similarity between long videos and texts, and a model is trained in an end-to-end manner to realize text-to-long video retrieval. The application uses the idea of multi-scale multi-example learning, and can effectively solve the text-to-long video retrieval task.

This invention discloses a pathological image classification method based on self-motivated multi-instance learning. This method promotes better and more reliable decision-making by exploring the potential relationships between instances and the reciprocal relationship between feature representation and label prediction. The invention captures and aggregates tumor feature representations by introducing label-related category priors provided by pseudo-packet prediction to achieve high-accuracy pathological image label prediction. Conversely, the network is optimized based on the prediction results to further improve feature representation and thus improve pseudo-packet label prediction, forming a self-motivated learning mechanism. This invention introduces a multi-level feature fusion strategy to explore knowledge of current instances and global historical instances, while constructing a time comparison module to improve the robustness of feature representation and alleviate representation bias and overfitting problems. Furthermore, the self-motivated feature fusion module utilizes the mutual refinement mechanism between pseudo-packet prediction and feature representation to enhance the accuracy and reliability of pathological image classification.

The application discloses a low-resolution face recognition system based on deep learning and monitoring equipment, and relates to the field of deep learning and monitoring equipment fusion. The system mainly comprises: collecting face images under monitoring videos to construct a data set, including face images under clear videos and face images under low resolution. The method is a multi-example learning method, adopts a self-supervised contrast learning mode to compare the feature differences between positive and negative samples, adopts a multi-scale patch embedding to facilitate improvement of model performance, fuses multi-scale information, adds a Dropout layer in a multi-layer perceptron of a Swin Transformer, increases the generalization ability of data, prevents a certain neuron of the Swin Transformer from dominating the final result, so as to neglect the results of other neurons, and facilitates classification and prediction of the whole image.

A method, apparatus and system to train a MIL text classification model for classifying a text content bag includes determining a first classification estimate for text content instances of the text content bags using bag-level information, determining a second classification estimate for the text content instances of the text content bags using the first classification estimates by applying a contrastive learning technique, determining a pseudo classification label for each of the text content instances of the text content bags using the second classification estimates, determining a combined loss including a first loss associated with a bag constraint loss determined from a bag index of each text content instance, a second loss associated with the contrastive learning technique, and a third loss associated with the determination of the pseudo classification label, and guiding the training of the MIL classifier using the combined loss.

This invention discloses a multi-gene mutation prediction method based on a combination of multi-task and multi-instance learning. Belonging to the fields of digital image analysis, pathology, and machine learning, the specific steps are as follows: Preprocessing existing pathological image data by staining normalization; constructing a feature matrix for each pathological image, and further using a two-layer multi-instance learning method to construct a package for each pathological image; constructing a multi-task deep learning network based on transformer and MobileNet; applying the model to a test set and outputting pathological image analysis results. This invention employs a multi-task deep neural network and applies it to the task of predicting multiple gene mutations based on pathological images. Compared to traditional single-task networks, this invention can simultaneously predict the results of multiple tasks, saving computational resources while improving accuracy; furthermore, combining the transformer module with traditional convolution considers both local and global features.

The application discloses an event template induction method and system based on a large-scale language model. The method mainly comprises three modules: context-based text conceptualization, confidence-based event template structuring and graph-based event template integration. Specifically, the context-based text conceptualization fully utilizes the generation ability and analogy ability of a large-scale generative pre-training language model through example learning, and converts diversified event natural language expressions into unified conceptualized event template language; the confidence-based event template structuring filters the conceptualized event categories and event argument roles through saliency, reliability and consistency, and thus structures the event template language; and the graph-based event template integration integrates the scattered event templates of the same event through a graph partition clustering algorithm. The application can effectively discover high-quality and high-coverage event templates in an open scene.

Systems, apparatuses, and methods are provided for generating patch-level predictions in whole slide images (WSIs) using classification models trained with attention and self-attention mechanisms. A WSI is divided into patches, and each patch is processed by a feature extraction model to obtain features. Attention-based aggregation assigns weights to patch features during training, enabling the classification model to generate patch-level predictions for disease-associated or non-disease-associated target classes during inference, including artifacts. A patch is classified as positive for a target class if its predicted probability exceeds a threshold, and negative otherwise. Training uses multiple instance learning and attention-derived data to optimize model performance. This approach supports granular and interpretable outputs for diagnostic and quality assurance applications.

Systems and methods relate to predicting disease progression by processing digital pathology images using neural networks. A digital pathology image that depicts a specimen stained with one or more stains is accessed. The specimen may have been collected from a subject. A set of patches are defined for the digital pathology image. Each patch of the set of patches depicts a portion of the digital pathology image. For each patch of the set of patches and using an attention-score neural network, an attention score is generated. The attention-score neural network may have been trained using a loss function that penalized attention-score variability across patches in training digital pathology images labeled to indicate no or low subsequent disease progression. Using a result-prediction neural network and the attention scores, a result is generated that represents a prediction of whether or an extent to which a disease of the subject will progress.

The embodiment of the invention provides a recommendation model training method for alternative traffic tickets, a recommendation method and a program, and the training method comprises the steps: constructing a training package based on historical recommendation data of the alternative traffic tickets; the training package comprises a plurality of examples, and the examples are alternative traffic ticket business search schemes; determining example-level target feature information corresponding to the training packet; determining packet-level target feature information corresponding to the training packet; obtaining multi-example learning feature information corresponding to the training packet in combination with example-level target feature information and packet-level target feature information corresponding to the training packet; and training a recommendation model at least based on the multi-instance learning feature information corresponding to the training package by taking improvement of the prediction accuracy of the user click probability of the training package and the prediction accuracy of the ordering result of the instances as a training target to obtain a trained recommendation model. According to the embodiment of the invention, the accuracy of the recommendation result for replacing traffic tickets and the user experience can be improved.

The invention discloses a speech emotion recognition method based on a two-stage multi-instance learning network, and relates to the technical field of speech emotion recognition. According to the speech emotion recognition method based on the multi-instance learning network, accurate emotion classification can be performed according to audio segments when speech audio is not equal in length and emotion expressions in the speech are not uniformly distributed. Firstly, an utterance-level audio is regarded as a packet, multiple features are extracted from audio clips obtained through segmentation, and two-stage processing is carried out. 1) a feature representation of each example is extracted on a local scale, and correlation enhancement is performed on the feature representations by using cross attention. And 2) designing a feature distillation module to filter redundant examples with weak emotion information, and obtaining a pseudo packet prediction result through a sequence weighted aggregation module and a multi-layer perceptron. And finally, performing secondary aggregation in combination with example-level and pseudo packet prediction results to obtain a final emotion tag. The method can be applied to voice emotion recognition, and the emotion of a voice audio speaker is accurately classified.

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This application relates to an automated detection method and system for latent cracks in poultry eggs based on view AI perception and multi-example learning, comprising: (S1) acquiring multi-view, multi-site images of each poultry egg using an image acquisition device, and assigning an egg-level binary classification label to each egg; (S2) extracting image-level representations using a frozen backbone network, and then adaptively weighting and aggregating all image-level representations through a single-layer gated attention module, and performing end-to-end training using a standard cross-entropy loss function; (S3) obtaining the detection result of whether the current poultry egg to be detected has latent cracks. The accuracy of this method reaches 98.66±0.78%. This method achieves clear imaging of the entire surface of the eggshell while achieving high classification accuracy with low manual annotation input, and can provide an efficient weakly supervised detection scheme for high-throughput online eggshell latent crack detection.

The present application relates to a method for predicting EGFR gene mutations in lung cancer based on multi-instance learning and Transformer technology. The method extracts instance-level features by segmenting lung CT images into image blocks, and uses soft pseudo-labeling technology in a self-generated manner to provide additional supervisory signals for instance-level features, guiding the multi-instance learning model to more effectively distinguish instances and enhance the identification ability of instance-level features. The unique relative spatial position information is integrated into the self-attention mechanism, which can flexibly and clearly mine the dependencies between instances and greatly improve the representation ability of heterogeneous tumors. The importance scores of different instance features are automatically learned through the adaptive gated instance aggregation module, which facilitates the dynamic weighting of feature aggregation instances. This method improves the generalization of the model on different data sets. The prediction method provided by the present application can delicately capture and analyze the heterogeneous features within the tumor, ensuring that the prediction of the EGFR mutation status is more accurate and targeted.

The invention relates to the technical field of malicious traffic detection, in particular to a malicious traffic detection method based on multi-instance learning, which comprises the following steps of: 1, a burst segmentation stage: firstly, carrying out time dimension structured segmentation on original encrypted network traffic, dynamically dividing a continuous network flow sequence into a plurality of short burst segments with time locality through a sliding window mechanism; step 2, a feature extraction stage: firstly, inputting a data packet size sequence subjected to burst segmentation and normalization processing into a convolutional neural network, and extracting high-level features layer by layer through multi-layer convolution operation; and step 3, a flow aggregation and attention pooling stage: the model identifies behavior characteristics of sensitive data transmitted through an abnormal tunnel by analyzing a space-time pattern of encrypted flow through a multi-stream characteristic aggregation and dynamic weight distribution mechanism, so that the accuracy and robustness of detection are improved, and the detection speed is improved. And a more effective means is provided for network security protection and sensitive data exit channel detection.

This application discloses a training method, usage method, apparatus, device, and medium for an image classification model, belonging to the field of artificial intelligence. The image classification model includes a feature extraction network and a multiple instance learning model. The method includes: acquiring a sample image set, wherein each sample image in the sample image set includes at least two instances; training the feature extraction network using the sample images in the sample image set through self-supervised learning based on contrastive learning, obtaining a trained feature extraction network; and training the multiple instance learning model using the sample images in the sample image set through multiple instance learning based on a mutual attention mechanism, obtaining a trained multiple instance learning model. The above scheme can reduce the computational complexity of the image classification model. The embodiments of this application can be applied to various scenarios such as cloud technology, artificial intelligence, smart transportation, and assisted driving.

A method includes obtaining, using at least one processor of an electronic device, one or more instance level supervised artificial intelligence (AI) models. The method also includes obtaining, using the at least one processor, aggregated level label information related to the one or more instance level supervised AI models. The method further includes obtaining, using the at least one processor, instance level feature information related to the one or more instance level supervised AI models. In addition, the method includes training, using the at least one processor, the one or more instance level supervised AI models using the instance level feature information and the aggregated level label information to obtain one or more trained instance level supervised AI models.

The application discloses a hidden camera discovery method and system based on multiple example learning, sniffs and captures video flow of all WiFi signals in an environment, extracts a video flow feature vector, uses a video stream recognition model M1 to recognize WiFi messages containing video streams in the environment, obtains a corresponding source MAC address set, marks the MAC address as a possible hidden camera device, takes real-time messages corresponding to each MAC address as input, extracts message feature vectors to combine into a multiple example feature vector sequence, identifies whether uplink flow of the MAC address has burst video flow through a camera flow discovery model, triggers burst video flow through human movement, determines whether a hidden camera is shooting in the current area through the camera flow discovery model, and outputs a MAC address set of the hidden camera. The application applies a multiple example learning model to hidden camera discovery, introduces multiple example features caused by human movement leading to burst video flow, and improves the accuracy of hidden camera discovery.