Stage Transformer Framework

Stage Transformer frameworks leverage the power of Transformer networks by decomposing complex tasks into sequential stages, improving performance and efficiency. Current research focuses on applying this approach to diverse areas, including time series forecasting, semantic parsing, music generation, and image segmentation, often employing variations of Transformer architectures tailored to specific problem domains. This two-stage (or sometimes multi-stage) approach addresses limitations of single-stage models, particularly in handling long-range dependencies and complex data structures, leading to improved accuracy and reduced computational costs in various applications. The resulting advancements have significant implications for improving the performance of AI systems across a range of fields.