Add links to paper, project page, GitHub repository, and citation

README.md

@@ -1,14 +1,16 @@
 ---
 license: mit
-pretty_name: "AMOD: Aerial Multi-view Object Detection"
-task_categories:
-- object-detection
 size_categories:
 - 10K<n<100K
+task_categories:
+- object-detection
+pretty_name: 'AMOD: Aerial Multi-view Object Detection'
 ---
 
 # AMOD: A Large-scale Benchmark for RGB-T Multi-view Aerial Military Object Detection
 
+[Project Page](https://unique-chan.github.io/G-MAD-Project) | [Paper](https://huggingface.co/papers/2607.19942) | [GitHub](https://github.com/unique-chan/G-MAD)
+
 - Dataset overview:
   ![AMOD](https://unique-chan.github.io/AMOD-Project/static/images/Homepage-fig1.jpg)
   - (a) Sample images with annotations from our AMOD dataset.
@@ -40,4 +42,15 @@ size_categories:
     |—— 📁 AMOD-T
   ~~~
   - Note that RGB and thermal modalities are perfectly aligned, with identical image counts, instance counts, and bounding box annotations in our benchmark.
-  - To facilitate modality-specific usage (e.g., RGB-only or thermal-only research), RGB and thermal (T) data are stored in separate directories, each containing identical annotations. Consequently, annotations from either EO or IR can be used interchangeably for the same scene.
+  - To facilitate modality-specific usage (e.g., RGB-only or thermal-only research), RGB and thermal (T) data are stored in separate directories, each containing identical annotations. Consequently, annotations from either EO or IR can be used interchangeably for the same scene.
+
+## Citation
+
+```bibtex
+@inproceedings{G-MAD2026,
+  title={G-MAD: A Game-Based Data Generation Framework for Multi-View RGB-T Aerial Object Detection},
+  author={Kim, Yechan and Park, JongHyun and Yoon, Dongho and Jung, Namhoon and Jeon, Moongu},
+  year={2026},
+  booktitle={ACM International Conference on Multimedia}
+}
+```