Self-attention can improve a model’s access to global information, but it comes with increased computational complexity. DAS, a novel fully convolutional method, uses deformable convolutions and gating mechanisms to focus attention on relevant features efficiently. With O(n) complexity, DAS outperforms traditional attention mechanisms, enhancing performance on various datasets like Stanford Dogs and ImageNet. This approach bridges the gap between local and global information integration in CNNs.