Add comprehensive dataset card for ViF-CoT-4K

README.md

@@ -0,0 +1,127 @@
+---
+task_categories:
+- video-text-to-text
+license: cc-by-4.0
+language:
+- en
+tags:
+- video-detection
+- ai-generated-content
+- explainable-ai
+- multimodal
+---
+
+# ViF-CoT-4K Dataset
+
+This repository hosts the **ViF-CoT-4K** dataset, a key component of the [Skyra: AI-Generated Video Detection via Grounded Artifact Reasoning](https://huggingface.co/papers/2512.15693) paper. Skyra is a specialized multimodal large language model (MLLM) designed to identify human-perceivable visual artifacts in AI-generated videos, leveraging them as grounded evidence for both detection and explanation.
+
+-   **Paper**: [Skyra: AI-Generated Video Detection via Grounded Artifact Reasoning](https://huggingface.co/papers/2512.15693)
+-   **Project Page**: https://joeleelyf.github.io/Skyra/
+-   **Code**: https://github.com/JoeLeelyf/Skyra
+
+## Introduction
+
+The misuse of AI-driven video generation technologies has raised serious social concerns, highlighting the urgent need for reliable AI-generated video detectors. Most existing methods are limited to binary classification and lack the necessary explanations for human interpretation. **ViF-CoT-4K** addresses this by providing a specialized dataset to train multimodal large language models (MLLMs) to identify human-perceivable visual artifacts in AI-generated videos and leverage them as grounded evidence for both detection and explanation.
+
+ViF-CoT-4K represents the first large-scale AI-generated video artifact dataset with fine-grained human annotations, supporting the development of models capable of spatio-temporal artifact perception, explanation capability, and detection accuracy.
+
+### Hierarchical Artifact Taxonomy
+
+The dataset defines a comprehensive taxonomy to categorize AI generation errors, dividing them into **Low-level Forgery** (e.g., texture/color anomalies) and **Violation of Laws** (e.g., physical inconsistencies).
+
+<p align="center">
+  <img src="https://github.com/JoeLeelyf/Skyra/raw/main/static/images/taxonomy.png" alt="Taxonomy of Artifacts" width="60%">
+</p>
+
+## Dataset: ViF-CoT-4K
+
+**ViF-CoT-4K** is constructed to address the lack of detailed artifact annotations in existing datasets.
+
+-   **Scale**: ~4,000 videos, including high-quality samples from **Sora-2, Wan2.1, Kling**, and more.
+-   **Annotation**: Fine-grained labels including artifact type, textual explanation, timestamps, and bounding boxes.
+-   **Real-Fake Pairs**: Generated videos are semantically aligned with real counterparts to prevent shortcut learning.
+<p align="center">
+  <img src="https://github.com/JoeLeelyf/Skyra/raw/main/static/images/statistics.png" alt="Dataset Statistics" width="90%">
+</p>
+
+## Usage
+
+### Requirements
+-   **SFT Stage**: follow [LlaMA-Factory](https://github.com/hiyouga/LLaMA-Factory) for environment setup.
+-   **RL Stage**: follow [verl](https://github.com/volcengine/verl) for environment setup.
+-   **Inference**: follow [Qwen-2.5-VL](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct) for quick start and [vLLM](https://github.com/vllm-project/vllm) for deployment.
+
+### Data Preparation
+-   Training data: Download and prepare the **ViF-CoT-4K** dataset from [here](https://huggingface.co/datasets/JoeLeelyf/ViF-CoT-4K).
+
+-   Evaluation data: Download evaluation datasets (e.g., **ViF-Bench**) from [here](https://huggingface.co/datasets/JoeLeelyf/ViF-Bench). And modify the path to your local directory in `test_index.json`.
+The `test_index.json` file should contain the following format:
+```json
+{
+    "Real": [
+        "path_to_parsed_frames_dir/Real/gdymHI9S6gM-0",
+        ...
+    ],
+    "LTX-Video-13B-T": [
+        "path_to_parsed_frames_dir/Fake/LTX-Video-13B-T/gdymHI9S6gM-0",
+        ...
+    ],
+    ...
+}
+```
+
+### Supervised Fine-Tuning (SFT)
+We use LLaMA-Factory for SFT. You can start training after setup the dataset config following the instructions in the LLaMA-Factory repository.
+
+```bash
+cd train/LLaMA-Factory
+bash train.sh
+```
+
+### Reinforcement Learning (RL)
+We use verl for RL training with GRPO, with adapted reward design provided in `train/verl/verl/utils/reward_score/ladm.py`.
+
+### Evaluation
+
+Evaluate scripts are provided in the `eval/` directory. You can run the evaluation script as follows:
+
+-   inference: Run inference to get model predictions and explanations, save the results in a JSON file.
+```bash
+cd eval
+bash scripts/Skyra/inference.sh
+# or
+python inference.py \
+    --index_json /path_to/test_index.json \
+    --model_path /path_to/Skyra-SFT \
+    --model_name Skyra-SFT \
+    --save_dir results/Skyra
+```
+
+-   evaluation: Evaluate the model predictions against ground truth and compute metrics.
+```bash
+cd eval
+bash scripts/Skyra/eval.sh
+# or
+python eval.py \
+    --json_file_path results/Skyra/Skyra-SFT_predictions.json
+```
+
+## License
+
+The **ViF-CoT-4K** dataset and **Skyra** model weights are released under the **CC BY 4.0** license. Users must adhere to the terms of source datasets (Kinetics-400, Panda-70M, HD-VILA-100M).
+
+## Citation
+
+If you find Skyra or ViF-CoT-4K useful, please cite our paper:
+
+```bibtex
+@misc{li2025skyraaigeneratedvideodetection,
+      title={Skyra: AI-Generated Video Detection via Grounded Artifact Reasoning}, 
+      author={Yifei Li and Wenzhao Zheng and Yanran Zhang and Runze Sun and Yu Zheng and Lei Chen and Jie Zhou and Jiwen Lu},
+      year={2025},
+      eprint={2512.15693},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={https://arxiv.org/abs/2512.15693}, 
+}
+```