Split Attention Mechanism: Enhancing Feature Representation with Group Partitioning

The initial step involves partitioning the input feature map into R distinct groups, which are then subjected to a split attention operation. Following this, the features of each group are merged to create a feature map that matches the size of the input. This feature map is subsequently combined with the input via a bottleneck in the neck.