Strip Transformer

Strip Transformers are a class of transformer-based models designed to improve efficiency and performance in various computer vision and natural language processing tasks by processing data in strips or segments rather than as a whole. Current research focuses on developing novel architectures, such as radial strip transformers for image deblurring and position-aware transformers for document translation, that leverage both local and global information within these strips to enhance feature extraction and reduce computational costs. These advancements are significant because they enable faster inference, improved accuracy on challenging tasks like image super-resolution and medical data analysis, and offer solutions to the quadratic complexity limitations of standard transformers.