Global Local

"Global-local" approaches in various fields aim to improve model performance by effectively integrating both broad contextual information (global) and fine-grained details (local). Current research focuses on developing novel fusion mechanisms within diverse architectures, including transformers and convolutional neural networks, often incorporating uncertainty estimation or attention mechanisms to optimize the combination of these information sources. This strategy has demonstrated significant improvements across numerous applications, such as image segmentation, time series forecasting, and 3D object detection, leading to more robust and accurate results in these domains. The resulting advancements are impacting various sectors, including healthcare, autonomous vehicles, and disaster response.