Improve model card: Add pipeline tag, paper, code, and usage

This PR significantly enhances the model card for the VISION model by:
- Adding the `pipeline_tag: image-to-image`, which correctly categorizes the model and improves its discoverability on the Hugging Face Hub.
- Including a direct link to the associated paper: [VISION: Prompting Ocean Vertical Velocity Reconstruction from Incomplete Observations](https://huggingface.co/papers/2509.21477).
- Providing the paper's abstract for a comprehensive overview.
- Adding a link to the official GitHub repository for easy access to the code and resources.
- Including a "Quick Start" section with installation and inference code snippets directly from the GitHub README, enabling users to quickly get started with the model.
- Incorporating the main illustrative image from the GitHub repository and a proper BibTeX citation.

Please review these additions and merge if they align with the repository's guidelines.

README.md

@@ -1,3 +1,69 @@
----
-license: mit
----
+---
+license: mit
+pipeline_tag: image-to-image
+---
+
+# VISION: Prompting Ocean Vertical Velocity Reconstruction from Incomplete Observations
+
+This model is presented in the paper [VISION: Prompting Ocean Vertical Velocity Reconstruction from Incomplete Observations](https://huggingface.co/papers/2509.21477).
+
+<div align="center">
+<img src="https://raw.githubusercontent.com/YuanGao-YG/VISION/main/img/vision_main.jpg" width="1080">
+</div>
+
+## Abstract
+Reconstructing subsurface ocean dynamics, such as vertical velocity fields, from incomplete surface observations poses a critical challenge in Earth science, a field long hampered by the lack of standardized, analysis-ready benchmarks. To systematically address this issue and catalyze research, we first build and release KD48, a high-resolution ocean dynamics benchmark derived from petascale simulations and curated with expert-driven denoising. Building on this benchmark, we introduce VISION, a novel reconstruction paradigm based on Dynamic Prompting designed to tackle the core problem of missing data in real-world observations. The essence of VISION lies in its ability to generate a visual prompt on-the-fly from any available subset of observations, which encodes both data availability and the ocean's physical state. More importantly, we design a State-conditioned Prompting module that efficiently injects this prompt into a universal backbone, endowed with geometry- and scale-aware operators, to guide its adaptive adjustment of computational strategies. This mechanism enables VISION to precisely handle the challenges posed by varying input combinations. Extensive experiments on the KD48 benchmark demonstrate that VISION not only substantially outperforms state-of-the-art models but also exhibits strong generalization under extreme data missing scenarios. By providing a high-quality benchmark and a robust model, our work establishes a solid infrastructure for ocean science research under data uncertainty.
+
+## Code
+The official code is available on the [GitHub repository](https://github.com/YuanGao-YG/VISION).
+
+## Quick Start
+
+### Installation
+
+- cuda 11.8
+
+```bash
+# git clone this repository
+git clone https://github.com/YuanGao-YG/VISION.git
+cd VISION
+
+# create new anaconda env
+conda env create -f environment.yml
+conda activate vision
+```
+
+### Inference
+
+Pre-trained weights and test data are available on [Hugging Face](https://huggingface.co/YuanGao-YG/VISION/tree/main). Download them and place them in the same location.
+
+1. Inference using Incomplete Observations (SSH)
+```bash
+python inference_io_ssh_vision.py
+```
+
+3. Inference using Incomplete Observations (SSH + U + V)
+```bash
+python inference_io_ssh_u_v_vision.py
+```
+
+2. Inference using Complete Observations (SSH + U + V + B)
+```bash
+python inference_co_ssh_u_v_b_vision.py
+```
+
+## Citation
+If you find our work useful, please consider citing it:
+
+```bibtex
+@article{gao2025vision,
+  title={VISION: Prompting Ocean Vertical Velocity Reconstruction from Incomplete Observations},
+  author={Gao, Yuan and Wu, Hao and Wen, Qingsong and Wang, Kun and Wu, Xian and Huang, Xiaomeng},
+  journal={arXiv preprint arXiv:2509.21477},
+  year={2025},
+  eprint={2509.21477},
+  archivePrefix={arXiv},
+  primaryClass={cs.CL},
+  url={https://arxiv.org/abs/2509.21477}
+}
+```