Frequency Attention

Frequency attention is a rapidly developing technique that leverages the frequency domain of data (e.g., images, audio spectrograms) to improve various machine learning tasks. Current research focuses on integrating frequency attention modules into existing architectures like transformers and convolutional neural networks, often for knowledge distillation or enhancing feature extraction. This approach shows promise in improving performance across diverse applications, including image processing (e.g., exposure correction, deepfake detection), speech recognition, and even medical diagnosis (e.g., cerebral palsy prediction), by capturing global context and enhancing robustness to noise or compression artifacts. The resulting models often demonstrate improved accuracy and interpretability compared to purely spatial-domain methods.