VAR

This is the official project website of our paper: Towards Video Anomaly Retrieval from Video Anomaly Detection: New Benchmarks and Model

Peng Wu, Jing Liu Senior Member, IEEE, Xiangteng He, Yuxin Peng Senior Member, IEEE, Peng Wang, and Yanning Zhang Senior Member, IEEE

UCF-Crime Captions

UCFCrime-AR Captions are released to facilitate future research.

Features

I3D features of UCFCrime-AR

I3D&VGGish features of XDViolence-AR

Highlight

• We introduce a new task named video anomaly retrieval to bridge the gap between the literature and real-world applications in terms of video anomaly analysis. To our knowledge, this is the first work moves towards VAR from VAD;
• We present two large-scale benchmarks, i.e., UCFCrime- AR and XDViolence-AR, based on public VAD datasets. The former is applied to video-text VAR, the latter is to video-audio VAR;
• We propose a model called ALAN, aiming at challenges in VAR, where anomaly-led sampling, video prompt based masked phrase modeling, and cross-modal align- ment are introduced for the attention of anomalous segments, enhancement of fine-grained associations, and multi-perspective match, respectively.

Citation

If you find this repo useful for your research, please consider citing our paper:

@article{wu2023towards,
  title={Towards Video Anomaly Retrieval from Video Anomaly Detection: New Benchmarks and Model},
  author={Wu, Peng and Liu, Jing and He, Xiangteng and Peng, Yuxin and Wang, Peng and Zhang, Yanning},
  journal={arXiv preprint arXiv:2307.12545},
  year={2023}
}

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