Long Range Attention

Long-range attention mechanisms aim to improve the ability of neural networks, particularly transformers, to effectively process long-range dependencies within data, such as in long sequences or large images. Current research focuses on developing efficient long-range attention architectures, including modifications to existing transformer models and novel attention mechanisms like windowed attention and memory-based approaches, to address computational limitations and improve performance on various tasks. These advancements are significantly impacting computer vision and natural language processing, leading to improved performance in image restoration, object detection, semantic segmentation, and other applications requiring the integration of global context. The resulting models demonstrate improved accuracy and efficiency compared to previous methods.