| license: mit | |
| pipeline_tag: video-classification | |
| # FG-Diff: Frequency-Guided Diffusion Model with Perturbation Training for Skeleton-Based Video Anomaly Detection | |
| This repository contains the pre-trained checkpoints and code for the paper [Frequency-Guided Diffusion Model with Perturbation Training for Skeleton-Based Video Anomaly Detection](https://huggingface.co/papers/2412.03044). | |
| - **Project page:** [https://xiaofeng-tan.github.io/projects/FG-Diff/index.html](https://xiaofeng-tan.github.io/projects/FG-Diff/index.html) | |
| - **Code:** [https://github.com/xiaofeng-tan/fgdmad-code](https://github.com/xiaofeng-tan/fgdmad-code) | |
| ## Overview | |
| FG-Diff is a frequency-guided diffusion model for skeleton-based video anomaly detection. It improves robustness in open-set scenarios through perturbation training and uses frequency information to focus on principal motion components. | |
| ## Checkpoints | |
| Pre-trained models are available on Hugging Face: [https://huggingface.co/ModelsWeights/AD-FG-Diff](https://huggingface.co/ModelsWeights/AD-FG-Diff) | |
| ## Usage | |
| Please refer to the [GitHub repository](https://github.com/xiaofeng-tan/fgdmad-code) for detailed setup, training, and evaluation instructions. | |
| ## Citation | |
| If you find this work useful, please consider citing: | |
| ```bibtex | |
| @article{tan2026fgdiff, | |
| title={Frequency-Guided Diffusion Model with Perturbation Training for Skeleton-Based Video Anomaly Detection}, | |
| author={Tan, Xiaofeng and Wang, Hongsong and Geng, Xin and Wang, Liang}, | |
| journal={IEEE Transactions on Image Processing}, | |
| year={2026}, | |
| doi={10.1109/TIP.2026.3730816} | |
| } | |
| ``` |