The Split Attention module which is a computational unit made up of feature-map groups facilitates information interaction

It allows for the integration of multiple sources of information that may be distributed across different feature maps, and enables efficient communication between these sources. This is achieved by splitting the attention of the network between different feature maps, allowing it to selectively attend to relevant information and ignore irrelevant information. By doing so, the Split Attention module can enhance the performance of deep neural networks on a wide range of tasks, including image classification, object detection, and semantic segmentation. Overall, the Split Attention module represents an important advancement in the field of deep learning, as it enables more effective and efficient information processing in neural networks