FoR-Net: Learning to Focus on Hard Regions for Efficient Semantic Segmentation

TL;DR

FoR-Net is a lightweight semantic segmentation model that efficiently emphasizes hard regions like boundaries using a learned importance map and multi-scale reasoning, achieving competitive results with limited resources.

Contribution

Introduces FoR-Net, a novel lightweight architecture that selectively focuses on challenging regions through a learned importance map and multi-scale features.

Findings

FoR-Net achieves competitive performance on Cityscapes with limited resources.

The model improves segmentation consistency in challenging regions.

Region-focused reasoning acts as an effective inductive bias.

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.