Frequency Aware Transformer

Frequency-aware transformers are enhancing various machine learning tasks by incorporating frequency domain information into the transformer architecture. Current research focuses on developing novel transformer blocks and modules that effectively integrate spatial and frequency features, often using techniques like fractional Fourier transforms and Laplacian pyramids, to improve performance in image and signal processing applications such as image deblurring, video super-resolution, and biosignal analysis. This approach leads to more robust and accurate models, particularly for handling complex data with diverse frequency components and non-stationary characteristics, impacting fields ranging from medical imaging to computer vision. The resulting models often demonstrate superior performance compared to traditional methods that rely solely on spatial information.