---
base_model:
- Qwen/Qwen2.5-VL-3B-Instruct
library_name: transformers
pipeline_tag: image-segmentation
---

# Think2Seg-RS-3B

This repository contains the 3B prompter model for **Think2Seg-RS**, a decoupled framework for reasoning segmentation in remote sensing (RS) imagery.

## Overview

Think2Seg-RS addresses the limitations of coupling linguistic reasoning and pixel prediction in remote sensing analysis. The framework decouples high-level semantic reasoning from low-level geometric execution by training an LVLM prompter (based on Qwen2.5-VL) to control a frozen Segment Anything Model (SAM2) via structured geometric prompts. 

Through a result-oriented reinforcement learning objective, the model learns to translate abstract semantic reasoning into spatially grounded actions, achieving state-of-the-art performance on the EarthReason dataset.

- **Paper:** [Bridging Semantics and Geometry: A Decoupled LVLM-SAM Framework for Reasoning Segmentation in Remote Sensing](https://huggingface.co/papers/2512.19302)
- **Repository:** [GitHub - Ricardo-XZ/Think2Seg-RS](https://github.com/Ricardo-XZ/Think2Seg-RS)
- **Base Model:** [Qwen2.5-VL-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-3B-Instruct)

## Key Features
- **Decoupled Architecture:** Separates high-level semantic reasoning from low-level geometric execution.
- **Geospatial Understanding:** Optimized for the complexities of remote sensing imagery and heterogeneous backgrounds.
- **Zero-shot Generalization:** The learned prompting policy generalizes effectively across multiple referring segmentation benchmarks.

## Setup and Usage

For installation, training, and evaluation scripts, please visit the official [GitHub repository](https://github.com/Ricardo-XZ/Think2Seg-RS).

## Citation

If you find this work helpful, please consider citing:

```bibtex
@article{think2seg_rs_2025,
  title={Bridging Semantics and Geometry: A Decoupled LVLM-SAM Framework for Reasoning Segmentation in Remote Sensing},
  author={Luo, Junyu and Luo, Xiao and Chen, Xiusi and Xiao, Zhiping and Ju, Wei and Zhang, Ming},
  journal={arXiv preprint arXiv:2512.19302},
  year={2025}
}
```