Wavelet Attention

Wavelet attention integrates wavelet transforms with attention mechanisms in neural networks to improve feature extraction and processing, primarily aiming to enhance performance in various computer vision tasks. Current research focuses on incorporating wavelet attention into diverse architectures, such as convolutional neural networks (CNNs), transformers, and graph neural networks, often for applications like image classification, change detection, and time series forecasting. This approach addresses limitations of traditional methods by efficiently capturing both high-frequency details and global contextual information, leading to improved accuracy and efficiency in various fields, including medical image analysis and remote sensing.