Enhancing U-Net for Semantic Segmentation with Integrated Attention Mechanisms

This research investigates the efficacy of integrating attention mechanisms into the U-Net architecture for semantic segmentation tasks. Various attention modules including spatial and channel-wise attention mechanisms are incorporated and evaluated against the baseline U-Net model on benchmark datasets. Results show that the attention-augmented U-Net variants outperform the baseline in terms of segmentation accuracy, particularly in scenarios with complex object structures and cluttered backgrounds. Computational overhead is analyzed, revealing trade-offs between performance gains and computational cost.