Perturbation-Regularized Open-Vocabulary Remote Sensing Segmentation with Unified Multi-Domain Evaluation
Pi-Seg is a lightweight framework for open-vocabulary remote sensing segmentation that enhances transferability using semantically guided perturbation learning. It improves performance by injecting learnable perturbations into text and visual features, validated on a large benchmark, proving it offers robust and efficient generalization across diverse geospatial tasks.