RSUniVLM: A Unified Vision Language Model for Remote Sensing via Granularity-oriented Mixture of Experts

Understand

Remote Sensing Vision-Language Models (RS VLMs) have made much progress in the tasks of remote sensing (RS) image comprehension.

• While performing well in multi-modal reasoning and multi-turn conversations, the existing models lack pixel-level understanding and struggle with multi-image inputs.
• In this work, we propose RSUniVLM, a unified, end-to-end RS VLM designed for comprehensive vision understanding across multiple granularity, including image-level, region-level, and pixel-level tasks.
• RSUniVLM also performs effectively in multi-image analysis, with instances of change detection and change captioning.