  Recent advancements in foundation models (FMs), such as GPT-4 and LLaMA, have
attracted significant attention due to their exceptional performance in
zero-shot learning scenarios. Similarly, in the field of visual learning,
models like Grounding DINO and the Segment Anything Model (SAM) have exhibited
remarkable progress in open-set detection and instance segmentation tasks. It
is undeniable that these FMs will profoundly impact a wide range of real-world
visual learning tasks, ushering in a new paradigm shift for developing such
models. In this study, we concentrate on the remote sensing domain, where the
images are notably dissimilar from those in conventional scenarios. We
developed a pipeline that leverages multiple FMs to facilitate remote sensing
image semantic segmentation tasks guided by text prompt, which we denote as
Text2Seg. The pipeline is benchmarked on several widely-used remote sensing
datasets, and we present preliminary results to demonstrate its effectiveness.
Through this work, we aim to provide insights into maximizing the applicability
of visual FMs in specific contexts with minimal model tuning. The code is
available at https://github.com/Douglas2Code/Text2Seg.
