5 results about "Temporal continuity" patented technology

This invention presents a lightweight physical layer authentication method based on BiLSTM-Linformer. It addresses the decline in model adaptability caused by the complex time-varying nature of CSI data in dynamic environments by proposing a data augmentation method based on temporal interpolation and contrastive learning, a dimensionality reduction method combining PCA and shallow autoencoders, and a lightweight authentication scheme centered on BiLSTM and multi-head self-attention mechanisms. The scheme first uses temporal interpolation to augment CSI data, enhancing temporal continuity, and combines contrastive learning to optimize the model's discriminative ability, improving adaptability to mobile environments. Subsequently, PCA and shallow autoencoders are used for feature dimensionality reduction, reducing computational burden while preserving key CSI patterns and improving authentication efficiency. Finally, BiLSTM is introduced for temporal modeling, and a multi-head self-attention mechanism is combined to optimize temporal feature weight allocation, enabling the model to focus on key changes in CSI data, further improving authentication accuracy and robustness. Experimental results show that the proposed authentication scheme achieves an authentication stability score of 0.95 in dynamic environments, an improvement of approximately 27% compared to existing methods.

This invention belongs to the field of computer vision and video understanding technology, and discloses a weakly supervised video temporal action localization method based on adaptive temporal continuity. The method includes acquiring an unedited video to be processed, extracting original segment features from the video, and generating an encoded feature sequence using a temporal encoder; performing parallel processing on the encoded feature sequence to generate class-independent action score sequences and class activation sequences, respectively; constructing a temporal continuity label generation module; constructing an instance dependency update module; and supervising the instance dependency update module using temporal continuity pseudo-labels to obtain the action localization result. This invention solves the technical problem in existing technologies where neglecting the temporal continuity of actions leads to severe instance confusion and boundary ambiguity in the model.

The present application relates to a kind of speech content keeping representation learning method based on structural entropy adaptive segmentation and segment level alignment, belong to natural language processing technical field.It includes: in the teacher side based on frame level representation constructs self-similarity graph and carries out clustering by structural entropy minimization, according to time continuity, the clustering result is split into multiple continuous segments, and the prototype of each segment is obtained by pooling;In the student side, the differentiable allocation of frame to segment is realized using attention soft segmentation mechanism, and the student segment representation is obtained;After normalizing the teacher segment prototype and student segment representation, segment level alignment loss is calculated to optimize student model parameters, while updating teacher model parameters using exponential moving average method, to realize stable self-distillation training.By aligning on the segment level unit with stronger linguistic meaning, the present application reduces the sensitivity of frame level alignment to speech rate changes, speaker disturbance and noise disturbance, and improves the language content keeping ability and robustness of speech representation.

This invention relates to the field of image recognition technology, specifically to a short video annotation method and system, comprising the following steps: acquiring frame feature regions, constructing multi-scale mapping, analyzing dynamic levels, matching semantic tags, and generating a temporal annotation sequence. In this invention, by fusing directional difference and color statistics in short video frames, a joint measure of texture and color changes is achieved, improving the accuracy of regional dynamic feature expression. Edge direction histograms and color moment features extracted by multi-scale windows are standardized and screened using cosine similarity to ensure consistency of regional features across multiple scales and reduce interference. The normalized product of centroid displacement and brightness changes constructs a dynamic intensity distribution, enhancing the discriminativeness of motion level division. Semantic matching combines motion parameters and semantic vectors for joint calculation, improving the semantic association and accuracy of tag generation. Temporal reorganization uses trajectory aggregation to achieve temporal continuity of tags, ensuring the uniformity and stability of multi-frame annotation in both spatial and temporal dimensions.

This invention proposes a video-based person behavior recognition method based on temporal feature modeling, comprising: acquiring and preprocessing a continuous video frame sequence, extracting person behavior features from each frame to obtain a single-frame feature vector; constructing a temporal feature representation containing local and global temporal features based on the single-frame features to capture subtle local changes and global evolution trends of behavior; adaptively segmenting behavior into segments based on the magnitude and stability of temporal feature changes to obtain segments with high behavior consistency; extracting segment features and matching them with a pre-built behavior template library to identify behavior types; and finally, performing standardized judgment based on behavior duration, frequency of occurrence, and scene constraints to output normal or abnormal behavior results. This invention effectively solves the problems of missing temporal continuity, high false positive rate, insufficient stability, and limited practicality in existing technologies, significantly improving the accuracy and reliability of behavior recognition.