Pyramid Transformer

Pyramid Transformers represent a class of deep learning models that leverage hierarchical structures to process data efficiently and effectively, particularly for long sequences or high-resolution inputs. Current research focuses on adapting this architecture to various tasks, including image segmentation, time series forecasting, and natural language processing, often incorporating it with convolutional neural networks or recurrent neural networks to enhance performance. This approach addresses limitations of traditional Transformers in terms of computational cost and scalability, leading to improved results in diverse applications such as medical imaging analysis, autonomous driving, and satellite image processing.