SFA: Efficient Attention Mechanism for Superior CNN Performance

Abstract Attention mechanisms are critical tools for enhancing the performance of convolutional neural networks (CNNs), focusing on spatial and channel dimensions of feature maps, known as spatial attention and channel attention, respectively. While many advanced attention methods combine these dimensions to improve performance, particularly in downstream computer vision tasks, such methods often introduce significant computational overhead or fail to effectively capture long-range spatial dependencies alongside channel attention. To address these challenges, this paper proposes the sequential fusion attention (SFA) method, which introduces a complementary fusion strategy to integrate spatial and channel attention. Spatial attention leverages strip pooling to model long-range dependencies, while channel attention employs dynamic encoding to refine features. By utilizing a grouped processing approach, the SFA module achieves an optimal balance between computational efficiency and representation power. Extensive experiments on benchmark datasets demonstrate that SFA consistently outperforms state-of-the-art attention mechanisms, delivering competitive accuracy in image classification, object detection, and semantic segmentation tasks while maintaining reduced model complexity. This work underscores the potential of lightweight attention mechanisms in modern computer vision and paves the way for further innovations in resource-efficient neural network design. Our code is publicly available at the following URL: https://github.com/Xuwei86/SFA