3 results about "Cross modality" patented technology

A road scene target detection method, system, device and medium based on a Transformer and cross-modal, the method comprising: constructing a road scene target detection network model based on a Transformer and cross-modal; using an RGB and infrared dual-modal road scene target detection dataset for model training, and using the trained model to realize road scene target detection; the system, device and medium are used to realize the method; the application constructs three key modules of cross-modal feature fusion, direction structure enhancement and scale perception attention, systematically improves the feature representation capability of the model from three aspects of modal complementation, structure expression and scale modeling, and the three modules are mutually coordinated, so that the overall network can still maintain stable and accurate perception performance in a complex environment, and a technical solution with high robustness and generalization capability is provided for multi-modal target detection and scene understanding.

This application discloses a medical image multimodal computing method and system based on cross-modal generation, applicable to the field of medical imaging technology. The method includes: acquiring brain image data and preprocessing the brain image data; training a dual-path Siamese neural network based on the preprocessed brain image data; wherein the dual-path Siamese neural network includes a cross-modal generation network and a brain image diagnosis network; inputting the first intermediate layer feature map of the acquired cross-modal generation network and the second intermediate layer feature map of the brain image diagnosis network into a cross-modal feature fusion module for feature fusion to obtain a diagnostic result; acquiring brain image data of a target user, inputting the target user's brain image data into the trained dual-path Siamese neural network and the cross-modal feature fusion module, and outputting an evaluation result.

The application relates to the technical field of low-altitude airspace intelligent management, in particular to a low-altitude air route resource quantitative evaluation method and system and a storage medium, which comprises the following steps: inputting standardized multi-modal data into a multi-modal deep fusion evaluation model, extracting deep features of each mode through a mode-specific encoder, realizing information fusion between modes through a cross-modal attention fusion module, and extracting space-time dependent features through a space-time feature extraction network; calculating a plurality of basic evaluation indexes of low-altitude air route resources based on the space-time dependent features, dynamically fusing the plurality of basic evaluation indexes through an evaluation output layer using a weighting mechanism, and generating a comprehensive score of low-altitude air route resources. Through the innovative mode-specific encoder and cross-modal attention fusion module, deep features of each mode are extracted, the complex interaction and dependency relationship between modes are more accurately modeled, and the evaluation is more comprehensive and accurate.