14 results about "Selective attention" patented technology

The invention relates to a multi-vehicle cooperative sensing method based on an attention mechanism, and belongs to the technical field of mobile communication. A multi-vehicle collaborative perception scene lacks selective attention to perception information of different values, so that perception characteristics of each vehicle are difficult to effectively utilize. At the same time, in a high mobility scene, it is difficult to align sensing information time delay and asynchronous features, resulting in low dynamic target sensing precision. In order to solve the problem, the method comprises the following steps: firstly, filtering redundant sensing information of cooperative vehicles, and constructing a screening model based on aerial view feature intersection-to-union ratio so as to select high-quality cooperative vehicles; spatial calibration is completed in combination with sparse consensus foreground features and geometric consistency verification, and feature fusion is realized through an attention mechanism; and finally, finishing time calibration of the dynamic target by adopting a time alignment strategy based on distillation learning. The method is suitable for multi-vehicle cooperative perception in an Internet of Vehicles scene, the focus on high-quality perception information is improved, and meanwhile, the real-time perception precision and robustness are enhanced.

The invention discloses a two-dimensional cognitive training system and method integrating attention and an execution function, relates to the field of medical rehabilitation, and is used for ensuring comprehensiveness, scientificity, pertinence and long-term effectiveness of cognitive function training. According to the method, a concentrated attention training task, a continuous attention training task, a selective attention training task, an alternate attention training task and a decentralized attention training task are sequentially carried out; the training task of each dimension comprises multiple levels of sub-training tasks with gradually increased difficulty; in the training tasks of each dimension, at least a suppression control task and a memory task are fused; the training tasks of each dimension respectively collect training data to evaluate training indexes. According to the method, the cognitive function training task is more scientific, comprehensive and effective.

The invention discloses a cognitive function assessment data acquisition method and device, relates to the field of medical health, and is used for improving the convenience, efficiency and accuracy of assessment. Aiming at ADHD children, concentrated attention data, continuous attention data, selective attention data, alternate attention data, dispersive attention data, work memory ability data, suppression control ability data and cognitive flexibility ability data are respectively acquired by developing multiple rounds of interactive tasks. According to the invention, the portability of evaluation facilities is improved, and the accuracy and efficiency of data acquisition and evaluation are improved.

A closed-loop selective attention system for resolving conflicts in multi-source or multi-speaker environments, including a plurality of internal attention models, each outputting a probability distribution over candidate sources and an associated confidence score, a fuser detecting conflicts when two or more of said attention models output high-confidence predictions that disagree, a selective sampling policy querying one or more external agents, wherein each external agent possesses a knowledge base, a reliability model, and a communication protocol, a trust and reliability module assigning and updating dynamic trust scores for internal and external agents based on past performance, an efficiency optimizer minimizing communication overhead and decision delay by balancing token usage cost and latency cost, and a dynamical system formulator ensuring convergence of the conflict resolution process under bounded trust, decaying step size, and limited sampling.

A selective attention system for multi-source or multi-speaker environments, including a plurality of independent selective attention (SA) models, each configured to output a probability distribution over a plurality of sources and a confidence score, a modular framework allowing said SA models to be added, replaced, upgraded or blocked in real time without interrupting system operation, an output fuser configured to combine outputs from said SA models based on at least one of model confidence and dynamically learned reliability scores, and a routing mechanism that directs user attention to a single source at a time based on the fused output.

Process for selective attention in a conversation with multiple participants, each participant being either a human or an apparatus, including processing outputs from a plurality of sensors, each sensor sensing a different modality, detecting communication modalities of the multiple participants, a communication modality being either speech or sign language, determining a plurality of probability distributions of source participants to whom a specific participant is paying attention, according to a respective plurality of models, fusing the plurality of probability distributions into a confident probability distribution, defining a confidence level for the confident probability distribution, applying natural language processing to convert each participant's communication to text, clustering the participants into conversation groups, deriving an intended source participant to whom the specific participant is paying attention, according to a source participant with a highest expected utility, and suppressing audio received from participants other than from the intended source participant.

Systems, methods and computer-readable media for providing explainable selective attention in multi-source or multi-speaker environments. A selective attention module receives multimodal sensor data including audio, video, gaze, text, and physiological signals from a plurality of sources. An attention inference engine generates attention distributions over the sources and fuses them into a probabilistic belief state. An explainability module produces interpretable outputs corresponding to the fused belief, including attention matrices, confidence scores, reliability measures, margin-based differentiators, and natural language rationales. The explainability outputs are rendered through visual, auditory, or augmented / virtual reality interfaces to indicate the attended source, suppressed sources, and reasoning for the selection. The system enables user interaction by providing justifications in real time, logging explanations for retrospective analysis, and supporting adaptation of thresholds and model weights based on feedback. The disclosed technology improves transparency, interpretability, and trust in selective attention systems, while maintaining real-time performance in dynamic multi-speaker environments.

A user's attention capabilities can be assessed in a virtual environment. An electronic device, such as a head-mounted display, can display a plurality of visual stimuli concurrently in a 3D virtual environment, and each visual stimulus can be displayed at a position in the 3D virtual environment according to a display scheme. The electronic device can obtain a stream of sensor data measured by the one or more sensors, and can determine a plurality of sequential user responses to the plurality of visual stimuli based on the stream of sensor data. Based on the plurality of sequential user responses, the electronic device can determine an attention indicator indicating an attention capability of the user associated with the electronic device to different visual stimuli.

The application discloses a brain-computer interface system based on vibration stimulation selective attention, and the system comprises a vibration tactile stimulation device, an electroencephalogram acquisition system and a computer for stimulation interface presentation and data processing; a subject sits on a chair about 70 cm in front of the stimulation interface, and vibration stimulation is applied to the index fingers of the left and right hands according to an experimental paradigm process, and the stimulation frequencies are respectively set as optimal specificity stimulation frequencies screened in advance; the subject selectively concentrates attention on a target side according to a prompt of the stimulation interface; the electroencephalogram acquisition system acquires electroencephalogram data of the subject, the acquired data are subjected to subsequent pretreatment, feature extraction and classification to identify the target stimulation concerned by the subject, and the identification result is fed back to the subject in the form of vibration stimulation. The application can decode the user intention by using the electroencephalogram signal feature difference induced by different vibration stimulation selective attention tasks, and then convert the user intention into a target instruction, so that the user can interact with the external environment.

The invention relates to the field of attitude identification, and provides a user attitude tendency identification method and system and an interaction system in order to improve the accuracy of attitude tendency identification, the attitude of each mode is obtained through a self-adaptive attitude emotion polarity detection network, whether an attitude ambiguity problem exists between the modes is judged through an attitude polarity ambiguity diagnosis network, and the user attitude tendency identification accuracy is improved. If ambiguity exists, carrying out feature fusion by adopting a selective attention network, otherwise, carrying out feature fusion by adopting a common attention network; selecting an attention network, adopting a multi-head attention mechanism to learn contribution degrees of various semantics of various modes to tasks, and performing weighted connection on the features according to the contribution degrees to complete feature interactive fusion; the common attention network uses different emotional semantics of a text mode to guide non-text mode learning and connects the learned features to complete feature interaction fusion; and by adopting different feature fusion modes, the attitude tendency identification accuracy is improved.

The application provides a stable knowledge editing method and system based on attention drift constraint, and relates to the technical field of natural language processing. The method comprises the following steps: introducing a selective attention drift limiting strategy in the knowledge editing process of a large language model; positioning the attention head with serious attention drift phenomenon by comparing the model attention output before and after editing; training the model based on a new optimization target function to obtain optimal model parameters and realize stable knowledge editing. The application significantly improves editing accuracy and reduces the probability of incorrect answers. Experimental verification shows that the method effectively improves the performance of specific tasks and provides theoretical support and practical guidance for knowledge editing of large language models.

A context-aware selective attention system for multi-source or multi-speaker environments, including a natural language processor analyzing linguistic context of signals, an attention-shifter that enables transitions between sources even while a prior source remains active, a profile-learner constructing and updating semantic and behavioral profiles of sources, including speech features, emotional tone, and past attentional importance, an adaptive thresholder adjusting attention-decision thresholds based on the profiles, environmental context and model reliability, and a conversation graph manager maintaining a dynamic graph of sources and attentional relationships in real time.

Methods, systems, and apparatuses, including computer programs encoded on computer storage media, for processing an input sequence (or new input token) using a neural network that includes one or more attention layer blocks that each include a selective attention layer. By generating and using aggregated mask scores to modify attention logits used by an attention mechanism, the neural network's performance is improved. Further, by using the aggregated mask scores to also remove one or more context tokens from a context buffer; and by pruning the context buffer based on the aggregated mask scores, the memory cost and computation processing cost of using the neural network to process an input sequence (or new input token) is simultaneously improved.

This invention discloses a cross-modal target perception method based on data feature-based collaborative selective attention. It employs a neuromorphic collaborative selective attention mechanism that combines prior information and modal characteristics to quickly and accurately select effective cross-modal information. Based on this mechanism, a vision-radar selective attention target perception network model is constructed, including a primary and secondary modality definition module, a data-level visual perception information selection module, and a binary corrector for adaptive feature selection. By selecting primary and secondary modal data and converting the data into features, the extracted features are processed using the binary corrector to achieve effective feature focusing. Prediction is then performed, outputting the position and type of radar point targets in the image. This achieves efficient and real-time radar-vision cross-modal target detection, ensuring high accuracy and low latency in target detection.