DAFC

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Distribution-aware Forgetting Compensation for Exemplar-Free Lifelong Person Re-identification

DAFC 최신버젼 다운로드

최종 버전 다운로드 (.zip)

// repository documentation

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## Distribution-aware Forgetting Compensation for Exemplar-Free Lifelong Person Re-identification

🎯Our paper has been accepted by [IEEE Transactions on Multimedia 2025](https://arxiv.org/abs/2504.15041)
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### Introduction

```
Our work proposes a novel Distribution-aware Forgetting Compensation (DAFC) model that explores cross-domain shared representation learning and domain-specific distribution integration without relying on old exemplars or knowledge distillation.
```
![](https://raw.githubusercontent.com/LiuShiBen/DAFC/master/docs/DAFC.png)

### Requirements
- Python 3.7
- torch 1.10.0
- For more detailed requirements, run
```
pip install -r requirements.txt
```
### Dataset preparation
- Please follow [Torchreid_Datasets_Doc](https://kaiyangzhou.github.io/deep-person-reid/datasets.html) to download datasets and unzip them to your data path .
- Prepare the Seen dataset structure as follow:   ./docs/seen dataset structure .md
- Prepare the Unseen dataset structure as follow:  ./docs/Unseen dataset structure .md
### Training

Training order-1 for training.

```
python lifelong_train.py --data-dir=/your seen dataset path  --logs-dir==/save path --training_order="order-1"
```

Training order-2 for training.

```
python lifelong_train.py --data-dir=/your seen dataset path  --logs-dir==/save path --training_order="order-2"
```
### Testing
```
python evaluate.py --data-dir=/your test dataset path"
```

### Contact

If you have any questions, please contact Shiben Liu at liushiben310@163.com.

DAFC

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