FoR-Net: Focus-on-Regions Network for Semantic Segmentation
FoR-Net is a semantic segmentation framework designed to improve performance by explicitly focusing on difficult regions like object boundaries and thin structures. It achieves this using a selector module to predict region importance, which then uses a Top-K mechanism to select the most informative spatial locations. This selective reasoning enhances feature learning, leading to competitive results under resource constraints.