Add dataset card, link to paper and project page

README.md

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+---
+task_categories:
+- image-segmentation
+tags:
+- remote-sensing
+- semi-supervised-learning
+---
+
+# Toward Stable Semi-Supervised Remote Sensing Segmentation via Co-Guidance and Co-Fusion
+
+[Project Page](https://xavierjiezou.github.io/Co2S/) | [Paper](https://huggingface.co/papers/2512.23035) | [GitHub](https://github.com/XavierJiezou/co2s)
+
+Co2S is a stable semi-supervised remote sensing (RS) segmentation framework designed to mitigate pseudo-label drift and error accumulation. It synergistically fuses priors from vision-language models (CLIP) and self-supervised models (DINOv3).
+
+## Introduction
+Semi-supervised remote sensing image semantic segmentation often struggles with confirmation bias and pseudo-label drift. Co2S addresses these challenges using a heterogeneous dual-student architecture comprising two distinct ViT-based vision foundation models.
+
+## Key Features
+- **Heterogeneous Dual-Student Architecture**: Utilizes pretrained CLIP and DINOv3 to mitigate error accumulation.
+- **Explicit-Implicit Semantic Co-Guidance Mechanism**: Employs text embeddings and learnable queries to provide class-level guidance and enhance semantic consistency.
+- **Global-Local Feature Collaborative Fusion Strategy**: Fuses global contextual information from CLIP with local structural details from DINOv3 for precise segmentation results.
+
+The framework demonstrates leading performance across six popular remote sensing datasets and diverse partition protocols.