Cross Scale Attention

Cross-scale attention mechanisms in deep learning aim to improve model performance by effectively integrating information from multiple feature scales, addressing limitations of single-scale processing in various computer vision and medical image analysis tasks. Current research focuses on incorporating cross-scale attention into transformer-based architectures and convolutional neural networks, often employing novel modules to efficiently fuse multi-scale features and reduce computational costs. These advancements lead to improved accuracy and efficiency in applications such as object detection, image segmentation, and fine-grained visual recognition, impacting fields ranging from medical diagnosis to autonomous driving.