metadata
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.
- Project page: https://xiaofeng-tan.github.io/projects/FG-Diff/index.html
- 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
Usage
Please refer to the GitHub repository for detailed setup, training, and evaluation instructions.
Citation
If you find this work useful, please consider citing:
@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}
}