3results about How to "Avoid prediction" patented technology

This invention discloses a frequency-domain guided multi-scale deformable alignment method and system for UAV target detection. The method first acquires and preprocesses a dataset of UAV aerial images. Next, it improves the YOLOv8 model, performing target detection based on the preprocessed UAV aerial image dataset and outputting the target detection results. Finally, the improved YOLOv8 model is integrated into a pre-configured training environment, using training and validation images from the UAV aerial image dataset for training and validation. This invention significantly enhances the model's ability to perceive small targets, thereby significantly improving the localization and classification accuracy of small targets, reducing detection errors, and improving overall accuracy.

The application discloses a kind of weakly supervised image semantic understanding methods based on multi-task learning, comprising the following steps: obtaining task missing image, constructing multi-level task sharing encoder, extracting high-level semantic information layer by layer, input corresponding decoder branch;Construct public space-task space feature mapping module, through the unaligned task fusion module and task interaction mapping module, update each subtask feature by mapping;Task adaptive feature update module is constructed, and multi-level iterative update unaligned task feature;Task adaptive weakly supervised image semantic understanding framework is constructed, model loss function is established, image data with task missing is input into model, and obtains multi-task prediction result such as semantic segmentation, depth estimation, surface normal estimation.The application is according to the data information of task label unaligned, through the mapping interaction of public space and task space, fully fuses unaligned task feature, iteratively generates high-quality multi-task prediction result, can effectively handle weakly supervised problem with task missing, and simultaneously improves each task prediction accuracy.

The embodiment of the application relates to the technical field of data processing, in particular to a graph representation learning method and device for out-of-distribution generalization, equipment and a storage medium, aiming to obtain adaptive graph structure data representation of out-of-distribution environment and improve the accuracy of graph structure data related prediction. The method comprises the following steps: inputting an original graph data set into a graph structure data representation network, identifying stable subgraphs and noise subgraphs, performing representation processing on the identified graph structure data, obtaining vectorized representation of the stable subgraphs and vectorized representation of the noise subgraphs; simulating a multi-distribution environment, under the multi-distribution environment, performing prediction according to the vectorized representation of the stable subgraphs to obtain corresponding prediction results; performing loss function calculation on the prediction results and labels of the original graph structure data, optimizing parameters of the graph structure data representation network, and obtaining a graph structure data representation model; and performing a graph data related task through the model to obtain a target result of the graph data related task.