146 results about "Temporal context" patented technology

The invention discloses a time series anomaly detection method based on combination of hierarchical adaptive attention and Mamba. According to the method, a multi-granularity token routing strategy is provided, the strategy dynamically allocates computing resources in a time context, adaptively concentrates processing capacity on an information segment, and keeps wider perception at the same time, so that attention computing can be dynamically focused on different time scales and modes according to the complexity of input data; according to the method, the Mama is improved, so that parameters of the Mama can be dynamically adjusted according to characteristics of an input sequence, the long-distance dependency relationship is effectively simulated, and meanwhile, the modeling capability of a nonlinear time mode is enhanced. The abnormal score calculation process comprises three stages: reconstruction error calculation, error normalization and hierarchical score fusion. Different from a traditional method using a fixed threshold, the self-adaptive threshold selection strategy constructed by the method considers time context and data set features, the efficiency and precision of anomaly detection are improved, and effective support is provided for development of time sequence anomaly detection.

The invention provides a network camera monitoring identification method and system based on artificial intelligence, and the method comprises the steps: firstly obtaining a monitoring data stream which is outputted by a network camera and comprises a video frame sequence and a corresponding time sequence metadata sequence, and then carrying out the spatial-temporal context coding processing of the monitoring data stream; the method comprises the following steps: generating a context feature cube containing spatial position information and time evolution information, then executing a normal behavior mode learning operation based on the context feature cube, and generating a reference feature library containing typical scene feature templates and feature evolution rule description; and dynamically matching and comparing the context feature cube of the current time period with the reference feature library, calculating a feature matching deviation value and generating an abnormal confidence score, and finally generating a monitoring early warning instruction containing abnormal occurrence time, a space coordinate range and a confidence level identifier according to the abnormal confidence score and corresponding space-time position information. And the accuracy and the early warning effect of monitoring and identification of the network camera are effectively improved.

A system and a method for modeling local and global spatio-temporal context in a video for video recognition includes obtaining an input feature map and transforming the input feature map using linear functions to generate a spatial feature map and a temporal feature map corresponding to a video. The method further includes generating hierarchical contextual feature maps based on the spatial feature map and the temporal feature map that represent a context of the video at multiple levels of granularity. The method further includes aggregating the hierarchical contextual feature maps based on gating weights to obtain a spatial modulator and a temporal modulator that are representative of an aggregated context across the multiple levels. The method further includes obtaining an output spatio-temporal feature map based on the spatial modulator, the temporal modulator, and a query token associated with the video.

The invention provides a multi-modal data situation intelligent arrangement system and method, and relates to the technical field of data arrangement. The system comprises a dynamic heterogeneous data fusion module, an intelligent situation arrangement module and an incremental situation analysis module. And the dynamic heterogeneous data fusion module maps the multi-modal data flow into a hypergraph structure with dynamic weight through a semantic hypergraph fusion method of spatio-temporal context perception. And the situation intelligent arrangement module generates an evolution path of the cross-modal situation according to the node association strength and the semantic similarity in the hypergraph structure. And the incremental situation analysis module performs multi-granularity situation prediction and anomaly detection on an evolution path through hypergraph tensor decomposition and an attention mechanism, and dynamically optimizes an arrangement strategy. The method can be used for rapidly generating a large-screen situation display page of a data track and a situation thermodynamic diagram, high-speed situation arrangement and updating are achieved, and visual and dynamic support is provided for commanding and decision making.

The invention discloses a multi-modal news recommendation method and system in combination with clock interests, and relates to the technical field of data mining and recommendation methods.The method comprises the steps that a user behavior sequence, a candidate news set and a related timestamp set are extracted; obtaining the multi-modal coding representation of the historical interaction news through the time perception multi-modal feature coding; performing clock interest modeling based on multi-modal coding representation of historical interactive news, and fusing long and short-term interests through Gaussian weighted aggregation and long-term interest enhancement operation to obtain a user interest vector; and calculating the matching degree of the candidate news and the current user interest vector, applying time period sensitive suppression through time sequence gating, performing click probability prediction on the output of the time sequence gating by adopting a linear network, and performing a dynamic recommendation decision. According to the method, by means of the feature fusion technology of time context perception, the interest continuity of the hour granularity is captured, and the recommended content is more accurately matched with the requirements of the user in different time periods.

The invention relates to the technical field of shield tunneling machine cutterhead evaluation, and discloses an intelligent evaluation system and method for the state of a shield tunneling machine cutterhead, and the system obtains rich operation state statistical characteristics through the collection of multi-source data of torque, current and vibration sensors and the utilization of a sliding window feature extraction technology. Further, time sequence context coding is carried out on the time queue of the cutterhead operation state statistical characteristics to obtain cutterhead operation state time sequence context coding characteristics, and deep information of the cutterhead operation state is captured. And finally, inputting the coded cutter head operation state time sequence context coding features into the trained GBRT model to realize accurate prediction of the cutter wear degree. According to the method, the accuracy and comprehensiveness of state monitoring are improved, the early warning capability of potential faults is enhanced, and a guarantee is provided for safe and stable operation of the shield tunneling machine.

The invention discloses a weak supervision group behavior recognition method based on dynamic prompt tuning, and belongs to the field of video understanding. According to the method, a pre-trained vision-language model is adaptively expanded to a group behavior recognition task for challenges such as complex group behavior semantics, strong spatio-temporal context dependency and lack of individual labels in a video. According to the technical scheme, a dynamic prompt generation technology with visual conditions is provided, instance-level text prompts can be automatically generated according to input video content, the accuracy of vision-text semantic alignment is enhanced, meanwhile, a time sequence fusion module is integrated in a model, and the purpose is to fuse key time information in multiple frames through modeling inter-frame time sequence dependence. The model calculates a similarity score between visual and text features and takes the similarity score as a classification prediction basis, and finally, a cross entropy loss function is adopted to carry out end-to-end training on the network. The validity of the method is verified on a volleyball data set and an NBA data set.

The invention discloses a sequence recommendation method based on a time-aware hierarchical attention network, and the method comprises the steps: obtaining a user interaction sequence of a target user, and obtaining a time interval sequence and a time context sequence of the interaction of the target user based on the user interaction sequence; inputting the user interaction sequence, the time interval sequence and the time context sequence into a trained sequence recommendation model to obtain a user interest representation based on a time interval and a user interest representation fused with time context information; and determining a correlation score with the user interaction sequence according to the user interest representation, obtaining a final correlation score through weighted fusion, determining a recommended item list according to the final correlation score, and recommending the recommended item list to a target user. According to the method, the problem of insufficient noise interference and context fusion in time information modeling of a traditional method is solved.

Examples relate to systems and methods for enhancing generative AI outputs using eye tracking data. An eye tracking system accesses eye gaze information associated with a field of view of a head-wearable apparatus and generates contextual information associated with the field of view of the head-wearable apparatus based on the eye gaze information. The eye tracking system processes, by a generative machine learning model, the contextual information and at least one image of the field of view of the head-wearable apparatus to generate an output and presents on a display of the head-wearable apparatus the output generated by the generative machine learning model.

The invention discloses a retrieval enhancement generation method for a time-sensitive problem, which comprises the following steps of: mixed time perception retrieval: enhancing document retrieval by adding time constraint on the basis of semantic relevance, and guiding by a time card to ensure that the retrieved document not only conforms to the meaning of query, but also conforms to the semantic relevance; the time context is met; the progressive multi-step reflection comprises the following steps of: firstly, acquiring and evaluating an initial document set by applying mixed time perception retrieval; if a document is retrieved, generating a final answer by using a large language model; otherwise, entering a reflection stage, and summarizing useful time information in the retrieved document into a context; and merging document sets accumulated in all iterations to generate a final answer. According to the method, a new framework integrating dynamic knowledge updating and time reasoning into the retrieval and generation process is provided, and accurate and timely response can be made to time-related problems.

The invention provides a multi-modal agricultural technology question and answer method and system, and belongs to the technical field of artificial intelligence, and the method comprises the steps: extracting a text feature vector, an image feature vector, a time sequence feature vector and a spatio-temporal context feature vector according to a query text and a crop image; calculating an initial fusion feature according to the spatio-temporal context feature vector, the image feature vector and the time sequence feature vector; when the query text contains a semantic entity of a preset type, calculating to obtain a final fusion feature according to the text feature vector and the initial fusion feature; splicing the final fusion feature and the text feature vector, and mapping the spliced vector to a space-time knowledge graph for reasoning to obtain a causal reasoning path; and generating a question and answer result according to the causal reasoning path. According to the method, deep alignment of time and space and semantics is carried out on the multi-modal agricultural data, and causal reasoning is carried out in combination with the knowledge graph with time and space constraints, so that the accuracy of a question and answer result is remarkably improved.

The invention belongs to the field of computer science and technology, and particularly relates to a multi-mode weak supervision video anomaly detection method and device, equipment and a medium. The method comprises the following steps: firstly, carrying out dynamic modeling on inter-fragment information by utilizing an external attention mechanism to obtain cross-fragment global context information; the temporal context aggregation module and the multi-scale time network are used to capture global and local information of the visual information and the text information within the fragment, and a feature representation containing local context details and the global information is generated. In addition, a multi-modal adaptive fusion mode is adopted, key modal features are focused in combination with target weights, further processing is performed through a multi-scale convolution attention module, and feature representation with higher discrimination is extracted. According to the method, the dependence on accurate annotation data in traditional video anomaly detection is effectively reduced; through hierarchical context modeling and an adaptive attention mechanism, the feature expression ability and the key information capture efficiency are enhanced, and a reliable anomaly detection solution is provided for an intelligent monitoring system.

The invention discloses a continuous sign language recognition method based on layered space-time enhancement. The method comprises the following steps: acquiring a sign language video; and inputting the sign language video into the trained sign language recognition model to obtain a first recognition result and a second recognition result, and taking the first recognition result as a final sign language recognition result. According to the continuous sign language recognition method based on hierarchical space-time enhancement, multi-stage output of a ResNet34 network is captured through an alignment module, and additional hierarchical alignment supervision is provided; according to the method, the alignment module and the timing causal module are integrated into the ResNet34 network, future and past information is aggregated through the timing causal module, so that more accurate vocabulary boundary perception is achieved, the alignment module and the timing causal module are integrated into the ResNet34 network, good balance between accuracy and calculation cost is achieved, an efficient and accurate solution is provided for a continuous sign language recognition task, and the continuous sign language recognition efficiency is improved. The recognition performance and robustness are remarkably improved, and the problem of under-fitting of the ResNet34 network in space and time contexts is solved.

The invention discloses a depression state prediction method and system based on voice multi-scale time domain perception, and the method comprises the steps: collecting voice signals of a participant reading a unified standardized text, and carrying out the preprocessing of the voice signals, and generating a corresponding Mel spectrogram; extracting time context features by using a spectrum-time domain feature extraction algorithm to obtain joint feature representation; carrying out multi-scale division on the combined feature along the time dimension, and fusing the features of each scale into a global feature; and obtaining depression state discrimination results of the participants based on the global features. By combining the spectrum-time domain feature extraction algorithm and the frame-level time attention, the time domain locality features of depression speech such as inter-sentence pause, hesitant pause, inter-word transition and formant blurring can be explicitly analyzed and accurately positioned on the Mel spectrum, so that the recognition accuracy and the result stability are remarkably improved.

The invention discloses an intelligent recommendation method and system for travel content based on element universe, and the method comprises the steps: enabling a user to wear equipment to enter the travel element universe, and enabling the system to collect the feedback information of the user in real time; performing time sequence context semantic coding on real-time feedback of the user by adopting a semantic understanding model based on deep learning so as to extract context semantic association fed back by the user in real time, and determining whether the preliminary recommendation model enters a cold start stage or not based on the context semantic association; and if yes, the model determines the direction of the recommended travel content and performs personalized intelligent recommendation on the user. In this way, new users can be helped to find interested contents more quickly, recommendation results can be continuously optimized along with interaction between the users and the system, and the user experience satisfaction degree is improved.

The invention belongs to the technical field of drought monitoring, and particularly discloses a drought monitoring method and system based on multi-source data and spatio-temporal context embedding, and the method comprises the following steps: obtaining drought index data, multi-source dynamic remote sensing factor data and static geographic data of a meteorological station in a research area, and carrying out the preprocessing, obtaining original remote sensing features; extracting attribute values from the static geographic data according to the longitude and latitude of a site, extracting a space context embedding vector and a time context embedding vector based on a multi-layer perceptron and a long-short-term memory network, generating a modulated space-time context embedding vector, and splicing the modulated space-time context embedding vector with the original remote sensing features to obtain a final feature vector; and inputting the information enhanced final feature vector and the monthly standardized rainfall evapotranspiration index SPEI-3 of each station into a GXGBDM model, and outputting a drought monitoring result. By adopting the technical scheme, the drought monitoring result can be efficiently and accurately generated by fusing the multi-source data and the spatio-temporal context embedding information.

The invention relates to the technical field of data prediction, in particular to a flight guarantee time prediction method and device, a medium and equipment, by constructing a multi-dimensional initial feature set and fusing a channel attention mechanism, and by capturing a long-time sequence dependency relationship, the capturing capability of a preorder guarantee node delay conduction effect is remarkably improved; through dual-module adaptive selection based on node attributes, prediction demands of different types of guarantee nodes are accurately matched, and the overall precision of whole-process node prediction is improved; the prediction value of the ith guarantee node and the time sequence context feature are spliced to form the dynamic enhancement feature of the subsequent guarantee node, so that the transmission and fusion of the preorder prediction information to the subsequent node are realized, and the prediction of the subsequent guarantee node can adapt to the running state change of the preorder guarantee node in real time; the subsequent prediction deviation caused by the delay of the preorder guarantee node is effectively reduced, and the dynamic adaptability and timeliness of the prediction result are improved.

In various examples, machine learning models that benefit from temporal context while being computationally efficient to train and use are described herein. For instance, the disclosed systems and methods may apply a temporal series of images to a model and use intermediate features output from one or more backbone layers of the model as training data. In some examples, one or more recursive layers and / or one or more head layers of the model—or another model—may be trained using the training data by applying the intermediate features to the recursive layer(s). The recursive layer(s) may output a state representative of a temporal combination of the intermediate features, and the state may be applied to the head layer(s) to make one or more predictions. During inference, the recursive layer(s) may, in some examples, continuously update the state based on previous states of the recursive layer(s).

In various examples, to improve path perception in machine learning implementations, a temporal model includes a backbone model trained to predict one or more path perception outputs, such as, path geometry, path class, path uncertainty and / or other path attributes, for a current input frame. To create temporal context, the temporal model enables the backbone model to separately operate (in parallel or otherwise) on a set of frames that are temporally related to the current input frame. The outputs of the separate executions of the backbone model are then concatenated and processed via one or more convolution operations to generate a set of features that will be fed to the final output layer of the pipeline that encapsulates one or more path perception outputs that are generated based on temporal context.

The invention relates to the technical field of bridge construction monitoring, and discloses a bridge construction crack dynamic detection method and system based on AI visual identification. According to the method, pixel-level time domain and space domain feature separation and fusion are carried out by analyzing an obtained multi-frame continuous image sequence, a target structure feature map fused with spatio-temporal context information is generated, and a potential crack region is positioned according to the target structure feature map. And constructing a region evolution model by analyzing a pixel intensity multi-direction discrete change rule of the candidate region, and outputting quantitative description of a crack growth trend by using a crack growth prediction model. And a structural integrity dynamic score is generated through comprehensive growth trend and multi-scale texture analysis, and real-time detection and evaluation of the crack are achieved. According to the method, the accuracy and robustness of crack detection in a complex construction environment are improved, and the dynamic development trend of the crack can be effectively predicted.

The invention discloses a spatio-temporal trajectory prediction method and device based on a multi-modal condition potential diffusion generation model, electronic equipment and a storage medium, and the method comprises the steps: cleaning and aligning multi-source data such as trajectory data, environment semantics and a road network, and constructing standardized input; extracting spatio-temporal context representation through multi-modal attention, mining an environment topological structure in combination with an iterative graph network, and generating structured node embedding; adopting dual-channel cross-modal attention fusion trajectory dynamic features and graph structure information to form unified semantic representation; the conditional variation auto-encoder fuses feature codes to a low-dimensional potential space, conditional reverse denoising generation is executed by using a potential diffusion model, and a future trajectory sequence is recovered step by step; and light weight of the model is realized through diffusion consistency distillation, and reverse sampling is compressed. Through the multimode topology-diffusion distillation integrated architecture, the precision, continuity and reasoning efficiency of trajectory prediction under sparse noise data are improved, and the method is suitable for scenes such as ecological monitoring, intelligent traffic and navigation.

The application discloses a depression state prediction method and system based on voice multiscale time domain perception, and the method comprises the following steps: collecting voice signals of participants reading unified standardized texts, and generating corresponding Mel spectrum graphs after preprocessing; extracting time context features by using a spectrum-time domain feature extraction algorithm to obtain joint feature representation; performing multiscale division on the joint features along the time dimension, and then fusing the features of each scale into global features; and obtaining a depression state discrimination result of the participants based on the global features. By combining the spectrum-time domain feature extraction algorithm and frame-level time attention, the application can explicitly analyze and accurately locate the time domain local features of depression voice, such as inter-sentence pause, hesitation pause, inter-word transition and resonance peak fuzziness, so that the accuracy of recognition and the stability of results are significantly improved.

The application discloses an aerial view generation method based on multi-scale feature transformation and time sequence context, and relates to the technical field of map generation. The method fully utilizes the complementarity of image and laser radar data by fusing the image and the laser radar data, improves the accuracy and robustness of aerial view generation, and can still remain stable under bad weather; a multi-scale space conversion module extracts different scale features, enhances the feature expression capability, and makes the aerial view clearer and more accurate; time sequence information is introduced, past time features are used to enhance current features, and dynamic perception capability is improved; an advanced backbone network and a feature alignment and fusion module are adopted, so that high efficiency and flexibility are ensured; and specific network structures such as Swin-T, PointPillars and random inactivation layers are applied, so that the accuracy and generalization capability are further improved.

The present invention belongs to the technical field of video segmentation, and relates to a video segmentation method based on temporal context association, including: 1. Obtain the original video; 2. For adjacent frames in the original video, use an optical flow network to extract the valid pixels generated in the optical flow calculation; 3. Encode the valid pixels into a context encoding to emphasize the valid positions of existing features after temporal changes; 4. Construct a temporal multi-scale memory network in combination with the context encoding; 5. Read out features that are more unified with the current frame through the temporal multi-scale memory network; The method of this application can be used not only for natural images in video format, but also for medical video data with more complex noise conditions, such as inputting an ultrasound video and specifying the segmented organ to output the segmentation result of the corresponding organ; The method of the present invention can automatically segment the target cavity / organ in the ultrasound video sequence, thereby assisting the physician to automatically locate the target area, reducing manual recognition, and improving the diagnosis efficiency.

The invention relates to an intelligent lamp control method based on a multi-dimensional spatio-temporal context snapshot and an edge side instruction fine-tuning enlarging model, and the method comprises the steps: receiving a user adjustment operation, and obtaining a light state before adjustment and a corresponding target light state; generating adjustment feature data according to the difference value; collecting multi-source context data corresponding to the adjustment operation, performing time alignment with the adjustment feature data, packaging the multi-source context data into spatio-temporal context snapshot data, and storing the spatio-temporal context snapshot data; combining continuous adjustments in a preset time window into an adjustment session as statistical sample data; performing conditional statistical aggregation on the statistical sample data to obtain statistical index data, generating cue words, inputting an edge side instruction to finely tune the large language model for reasoning, and outputting structured user portrait data under the constraint of JSON Schema; and generating or updating an intelligent adjustment strategy to control the intelligent lamp. According to the method, user adjustment and context information identification preferences are combined on the edge side, and an executable strategy is formed, so that the intelligence, stability and personalized adaptation capability of automatic adjustment are improved.

The present technique relates to an information processing apparatus, an information processing method, and a program that enable a contextual relationship of data to be proved even when troubleshooting of equipment has been carried out.The information processing apparatus is an information processing apparatus that generates output data including object data to be a verification object of a temporal contextual relationship, the information processing apparatus including: a control unit configured to generate the output data including a hash value calculated on the basis of a part of or all of output data that temporally immediately-precedes the output data, the object data, and a plurality of mutually-different types of ID information related to the object data. The present technique can be applied to cameras.

The present application provides a video processing method, apparatus and device, which relate to the field of computer vision technology and are used to improve the accuracy of video action detection. The video processing method includes: extracting global time context information and local time context information features from the first fusion feature according to the first fusion feature of the video to be detected and a preset feature enhancement network, to obtain the global feature and the local feature of the video to be detected, where the first fusion feature includes the spatial flow feature and the temporal flow feature of the video to be detected; according to the global feature and the local feature, and in combination with a preset boundary prediction network, performing boundary localization on each action in the video to be detected, and determining the video segment corresponding to each action.

The invention provides a universal bacterium detection network model construction method and device, and a storage medium, in an STE pre-training stage, through fusion of explicit time sequence coding and image information of a corresponding growth stage, a spatio-temporal context across the growth stage is coded, and false negative caused by adhesion clusters is reduced; in the DG-MoE migration stage, global features and local features are respectively extracted through dual-granularity experts, so that detection of both tiny bacteria and normal bacteria is considered. Wherein a tiny target expert focuses on extracting local texture features and enhancing position distribution information; normal target experts focus on extracting global morphological features and enhancing boundary information. The problem of morphological difference between tiny bacteria and normal bacteria and the problem of false detection of impurities with similar morphologies are solved through double-granularity experts. The method is irrelevant to a model and can be compatible with an existing target detection algorithm, so that the performance improvement of consistency is realized.

The application provides a video salient object detection method based on spatio-temporal context scene relationship propagation, and relates to the technical field of video salient object detection. First, scene analysis is performed on each frame of video in a video frame sequence to obtain an instance-level object corresponding to each frame of video. Then, global instance-level features, local instance-level features and intra-frame low-level features of each frame of video and the corresponding instance-level object are extracted. Then, a matching frame of any frame of video is randomly sampled from the same video frame sequence, global instance-level features of the matching frame are extracted, the global instance-level features are integrated into global instance-level features of the matching frame, and time features between video frames are obtained. The dense spatial attention mechanism is used to integrate the local instance-level features into the global instance-level features to obtain spatial features. The time features and the spatial features are spliced to obtain spatio-temporal features. The spatio-temporal features and the global instance-level features are input into a convolutional neural network based on a gated recurrent unit for updating to obtain high-level spatio-temporal features. Finally, the intra-frame low-level features and the high-level spatio-temporal features are fused and decoded to generate a video salient object mask detection result. The application utilizes rich inter-frame and intra-frame scene relationship information in the video, and improves the accuracy of video salient object detection in complex scenes.

This application discloses a state diagnosis method, system, device, and medium based on UAV operational data, mainly relating to the field of state diagnosis technology. It addresses the problems of single-channel statistical features failing to capture cross-channel gradient correlations and dynamic coupling relationships between sensor signals, and unidirectional LSTMs only utilizing historical information and neglecting the inverse constraints of future temporal context on the current state. The method includes: inputting training samples into a forward LSTM and a backward LSTM respectively to obtain forward and backward outputs; concatenating the forward and backward outputs; compressing the concatenated data into a fixed-length semantic vector; obtaining the labeled state of the semantic vector; and obtaining a trained classification function based on the semantic vector and the labeled state; when new multi-channel UAV operational data is obtained, obtaining the corresponding new semantic vector; and inputting the new semantic vector into the trained classification function to obtain the predicted state.