🤖 AI Summary
This work addresses the challenge of insufficient accuracy in segmenting fine structures and object boundaries—particularly in resource-constrained settings—by proposing FoR-Net, a lightweight architecture for semantic segmentation. FoR-Net incorporates a region-focused inductive bias that selectively enhances information-rich and challenging regions through importance map prediction and a Top-K activation mechanism. It further aggregates multi-scale spatial context via parallel convolutional branches. Evaluated on the Cityscapes benchmark under standard training protocols and limited computational resources, FoR-Net achieves competitive overall performance while significantly improving segmentation consistency in difficult regions.
📝 Abstract
We present FoR-Net, a lightweight architecture for semantic segmentation that focuses on identifying and enhancing hard regions. Instead of relying on heavy global modeling, FoR-Net adopts an efficient strategy that selectively emphasizes informative regions through a learned importance map and a Top-K activation mechanism. Specifically, a selector module predicts region-wise importance, enabling the model to focus on challenging areas such as thin structures and object boundaries. Multi-scale reasoning is achieved using convolutional branches with different receptive fields, allowing diverse spatial context aggregation. We evaluate FoR-Net on the Cityscapes benchmark under limited computational resources. Despite its lightweight design and standard training configuration, FoR-Net achieves competitive performance and demonstrates improved consistency in challenging regions. These results suggest that region-focused reasoning provides a simple yet effective inductive bias for efficient semantic segmentation.