Hybrid Attention Transformer

Hybrid Attention Transformers (HATs) aim to improve the performance of transformer networks in various image processing tasks by combining different attention mechanisms. Current research focuses on architectures that integrate channel attention (capturing global context) with window-based self-attention (emphasizing local details), often incorporating overlapping windows to enhance inter-region information flow. These improvements address limitations of standard transformers in utilizing the full spatial range of input data, leading to significant gains in image restoration (e.g., super-resolution, denoising) and medical image segmentation (e.g., brain tumor delineation). The resulting advancements promise more accurate and efficient solutions for diverse image-related applications.