Local Transformer

Local transformers represent a significant advancement in deep learning, aiming to improve the efficiency and effectiveness of transformer architectures by focusing computational resources on localized regions of input data while still capturing global context. Current research emphasizes hybrid models combining local transformer blocks with convolutional neural networks or global attention mechanisms, often within encoder-decoder frameworks, to address limitations in handling high-frequency information and long-range dependencies. These advancements are impacting various fields, including image segmentation, object tracking, and remote sensing, by enabling more accurate and efficient processing of high-dimensional data.