
HolmesVAD
Official implementation of "Holmes-VAD: Towards Unbiased and Explainable Video Anomaly Detection via Multi-modal LLM"
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Holmes-VAD is a framework for unbiased and explainable Video Anomaly Detection using multimodal instructions. It addresses biased detection in challenging events by leveraging precise temporal supervision and rich multimodal instructions. The framework includes a largescale VAD instruction-tuning benchmark, VAD-Instruct50k, created with single-frame annotations and a robust video captioner. It offers accurate anomaly localization and comprehensive explanations through a customized solution for interpretable video anomaly detection.
README:
-
[2025.01.05] 🔥🔥🔥We release Holmes-VAU, an upgraded version of Holmes-VAD, featuring improvements in annotation granularity, quantity, and quality, as well as utilizing a more powerful foundational MLLM model. The
HIVAU-70k
benchmark is available now, please stay tuned! - [2024.07.01] 🔥🔥🔥 Our inference code is available, and we release our model at [HolmesVAD-7B].
Towards open-ended Video Anomaly Detection (VAD), existing methods often exhibit biased detection when faced with challenging or unseen events and lack interpretability. To address these drawbacks, we propose Holmes-VAD, a novel framework that leverages precise temporal supervision and rich multimodal instructions to enable accurate anomaly localization and comprehensive explanations.
- Firstly, towards unbiased and explainable VAD system, we construct the first largescale multimodal VAD instruction-tuning benchmark, i.e., VAD-Instruct50k. This dataset is created using a carefully designed semi-automatic labeling paradigm. Efficient single-frame annotations are applied to the collected untrimmed videos, which are then synthesized into high-quality analyses of both abnormal and normal video clips using a robust off-the-shelf video captioner and a large language model (LLM).
- Building upon the VAD-Instruct50k dataset, we develop a customized solution for interpretable video anomaly detection. We train a lightweight temporal sampler to select frames with high anomaly response and fine-tune a multimodal large language model (LLM) to generate explanatory content.
- Python >= 3.10
- Pytorch == 2.0.1
- CUDA Version >= 11.7
- transformers >= 4.37.2
- Install required packages:
# inference only
git clone https://github.com/pipixin321/HolmesVAD.git
cd HolmesVAD
conda create -n holmesvad python=3.10 -y
conda activate holmesvad
pip install --upgrade pip # enable PEP 660 support
pip install -e .
pip install decord opencv-python pytorchvideo
# additional packages for training
pip install -e ".[train]"
pip install flash-attn --no-build-isolation
CUDA_VISIBLE_DEVICES=0 python demo/cli.py --model-path ./checkpoints/HolmesVAD-7B --file ./demo/examples/vad/RoadAccidents133_x264_270_451.mp4
CUDA_VISIBLE_DEVICES=0 python demo/gradio_demo.py
If you find this repo useful for your research, please consider citing our paper:
@article{zhang2024holmes,
title={Holmes-VAD: Towards Unbiased and Explainable Video Anomaly Detection via Multi-modal LLM},
author={Zhang, Huaxin and Xu, Xiaohao and Wang, Xiang and Zuo, Jialong and Han, Chuchu and Huang, Xiaonan and Gao, Changxin and Wang, Yuehuan and Sang, Nong},
journal={arXiv preprint arXiv:2406.12235},
year={2024}
}
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