Region Aware Squeeze-and-Excitation Networks for Image Segmentation

TL;DR

This paper introduces Region Aware Squeeze-and-Excitation Networks for image segmentation, which improve upon traditional Squeeze-and-Excitation modules by incorporating spatially guided pooling. The proposed modules enable region-aware channel recalibration and outperform existing spatial-channel attention variants on several benchmarks. They offer complementary computational tradeoffs and provide structured spatial interpretability.

Why It Matters

The proposed approach has practical significance in image segmentation tasks, such as object detection and scene understanding, where accurate pixel-level classification is crucial. It can also be applied to other dense prediction tasks, like image generation and editing. The improved performance and efficiency of the proposed modules can lead to better real-world applications.

Key Findings

• Region Aware Squeeze-and-Excitation Networks outperform traditional SE modules
• The proposed modules provide structured spatial interpretability
• They offer complementary computational tradeoffs