# MPN

**Repository Path**: fenglihuasha/MPN

## Basic Information

- **Project Name**: MPN
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2024-08-02
- **Last Updated**: 2024-08-02

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Codes_MPN
Official codes of CVPR21 paper: Learning Normal Dynamics in Videos with Meta Prototype Network (https://arxiv.org/abs/2104.06689)

# MPN Framework
![image](./DPU.png)

![image](./MetaLearningPhase.png)

# Paper Results on Unsupervised VAD
![image](./UnsupervisedVAD.png)
# Paper Results on Few-shot VAD
![image](./Few-shotVAD.png)


# Preparation 
Please download the corresponding benchmarks in 'data' directory. Then prepare the environment as in requirement.txt. 
We have uploaded several trained models on online (Baidunetdisk(link：https://pan.baidu.com/s/1qcGmdmZlmAgqsAzw_i5BhA code：mapz) or Drive (https://drive.google.com/drive/folders/1ketomxctszHo7jpGQS3RxbZGq7e_M3e4?usp=sharing)).


# Unsupervised Anomaly Detection Model Training
Run 'python Train.py' to train a model with DPU model.

# Unsupervised Anomaly Detection Model Testing
Run 'python Test.py' to train a model with DPU model.

# Meta-learning Anomaly Detection Model Training
Run 'python Train_meta.py' to train a model with MPU model.

# Meta-learning Anomaly Detection Model Testing
Run 'python Test_meta.py' to test a model with MPU model.

If you find this work helpful, please cite:
```
@inproceedings{Lv2021MPN,
  author    = {Hui LV and
	       Chen Chen and
               Zhen Cui and
               Chunyan Xu and
               Yong Li and
               Jian Yang},
  title     = {Learning Normal Dynamics in Videos with Meta Prototype Network},
  booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year      = {2021}
}
```