metadata
datasets:
- earth-insights/EarthReason
base_model:
- Qwen/Qwen2.5-VL-7B-Instruct
library_name: transformers
Bridging Semantics and Geometry: A Decoupled LVLM–SAM Framework for Reasoning Segmentation in Remote Sensing
This is the 7B model of Think2Seg-RS, a decoupled framework for reasoning segmentation in remote sensing (RS) imagery.
Our core idea is to decouple high-level semantic reasoning from low-level geometric execution. Specifically, we train an LVLM prompter (e.g., Qwen-2.5-VL) to control a frozen Segment Anything Model (SAM2) via structured geometric prompts. Through a result-oriented reinforcement learning objective, the LVLM learns to translate abstract semantic reasoning into spatially grounded actions, achieving state-of-the-art performance on the EarthReason dataset.
For more details, code, and the complete framework, please visit our GitHub repository.