Enhance model card: Add pipeline tag, paper link, GitHub link, and usage details (#1)

- Enhance model card: Add pipeline tag, paper link, GitHub link, and usage details (36c16f8dc5d94e4a480311e4ea297063fad97fd6)


Co-authored-by: Niels Rogge <nielsr@users.noreply.huggingface.co>

README.md

@@ -1,3 +1,77 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+pipeline_tag: video-classification
+---
+
+# Referee: Reference-aware Audiovisual Deepfake Detection
+
+This repository contains the `Referee` model, presented in the paper [Referee: Reference-aware Audiovisual Deepfake Detection](https://huggingface.co/papers/2510.27475).
+
+Code: [https://github.com/ewha-mmai/referee](https://github.com/ewha-mmai/referee)
+
+## Abstract
+<img src="https://github.com/ewha-mmai/referee/raw/main/referee.png" alt="Referee Architecture" width="900"/>
+
+Since deepfakes generated by advanced generative models have rapidly posed serious threats, existing audiovisual deepfake detection approaches struggle to generalize to unseen forgeries. We propose a novel reference-aware audiovisual deepfake detection method, called *Referee*. Speaker-specific cues from only one-shot examples are leveraged to detect manipulations beyond spatiotemporal artifacts. By matching and aligning identity-related queries from reference and target content into cross-modal features, Referee jointly reasons about audiovisual synchrony and identity consistency. Extensive experiments on FakeAVCeleb, FaceForensics++, and KoDF demonstrate that Referee achieves state-of-the-art performance on cross-dataset and cross-language evaluation protocols. Experimental results highlight the importance of cross-modal identity verification for future deepfake detection.
+
+## Requirements
+### Environment
+To train or evaluate Referee, you must first set up the environment:
+
+```bash
+conda create -n referee python=3.8.16
+conda activate referee
+pip install -r requirements.txt
+```
+
+### Dataset
+For training and evaluation, the dataset should be prepared following the specified format.
+An example dataset structure is provided in the [GitHub repository's `data/`](https://github.com/ewha-mmai/referee/tree/main/data).
+
+### Pretrained Checkpoints
+This project requires pretrained checkpoints to run training, evaluation, or fine-tuning.
+
+-   **Training from Scratch**  
+    To train the model from scratch, download the Synchformer checkpoint trained on **LRS3** from the [link](https://github.com/v-iashin/Synchformer) and place it in the `model/pretrained/` directory.
+
+-   **Evaluation or Fine-tuning Referee**  
+    To evaluate or fine-tune **Referee**, download the provided checkpoint from the [link](https://huggingface.co/eunsanglee/Referee/tree/main) and put it into the `model/pretrained/` directory.
+
+## Train
+To train Referee, you can use the provided `train.sh`. Some training-specific settings, such as the number of epochs, starting epoch, and training dataset, are set directly in `train.sh`.
+
+You can change most training parameters in the config file, `configs/pair_sync.yaml`. For example, you can adjust the learning rate, batch size, number of layers, etc.
+
+Once you have set all parameters as desired, you can start training Referee using:
+
+```bash
+sh scripts/train.sh
+```
+
+## Evaluation
+To evaluate Referee, you can use the provided `test.sh`. Some evaluation-specific settings, such as the model path and test dataset, are set directly in `test.sh`.
+
+You can change most evaluation parameters in the config file, `configs/pair_sync.yaml`. For example, you can adjust the number of layers, the number of identity queries, etc.
+
+Once you have set all parameters as desired, you can start evaluating Referee using:
+
+```bash
+sh scripts/test.sh
+```
+
+## Acknowledgement
+This project heavily references the implementation of [SynchFormer](https://github.com/v-iashin/Synchformer).
+
+We thank the authors for making their code publicly available.
+
+## Citation
+If you find our work helpful or inspiring, please feel free to cite it:
+
+```bibtex
+@article{boo2025referee,
+  title={Referee: Reference-aware Audiovisual Deepfake Detection},
+  author={Boo, Hyemin and Lee, Eunsang and Lee, Jiyoung},
+  journal={arXiv preprint arXiv:2510.27475},
+  year={2025}
+}
+```