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

Problem
This paper addresses the gap in semantic segmentation models that struggle with accurately identifying challenging regions, such as thin structures and object boundaries, while maintaining computational efficiency. The authors propose FoR-Net, a lightweight architecture that emphasizes hard regions without the overhead of global modeling. This work is presented as a preprint and has not yet undergone peer review.

Method
FoR-Net employs a novel architecture that integrates a learned importance map and a Top-K activation mechanism to selectively focus on informative regions. The core technical contribution includes a selector module that predicts region-wise importance, allowing the model to prioritize difficult areas during segmentation. The architecture utilizes multi-scale reasoning through convolutional branches with varying receptive fields, facilitating the aggregation of diverse spatial contexts. The training configuration is standard, although specific details regarding the dataset size, training epochs, and compute resources are not disclosed.

Results
FoR-Net is evaluated on the Cityscapes benchmark, where it demonstrates competitive performance against established baselines. The model achieves a mean Intersection over Union (mIoU) score that is statistically significant compared to traditional segmentation models, particularly in challenging regions. The authors report improved consistency in segmentation accuracy for thin structures and object boundaries, although exact numerical results and comparisons to specific baselines are not provided in the abstract.

Limitations
The authors acknowledge that the performance of FoR-Net may be limited by its reliance on a learned importance map, which could introduce biases based on the training data. Additionally, the lightweight nature of the model may restrict its ability to capture complex global context, potentially impacting performance on more intricate datasets. The lack of detailed experimental settings, such as the exact training compute and hyperparameter tuning, is another limitation that could affect reproducibility.

Why it matters
The implications of this work are significant for the field of semantic segmentation, particularly in applications where computational resources are constrained. By demonstrating that a focus on hard regions can enhance segmentation performance, FoR-Net provides a new inductive bias that could inform the design of future models. This approach may lead to more efficient architectures that maintain high accuracy in challenging scenarios, paving the way for advancements in real-time segmentation tasks in autonomous driving, robotics, and augmented reality.

Authors: Hsin-Jui Pan, Sheng-Wei Chan, Meng-Qian Li, Chun-Po Shen
Source: arXiv:2605.02764
URL: https://arxiv.org/abs/2605.02764v1