https://github.com/wangkai930418/dwopp_code
code for our BMVC 2022 paper "Positive Pair Distillation Considered Harmful: Continual Meta Metric Learning for Lifelong Object Re-Identification"
https://github.com/wangkai930418/dwopp_code
bmvc continual-learning incremental-learning knowledge-distillation meta-learning meta-metric-learning metric-learning object-reid person-reid
Last synced: 10 months ago
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code for our BMVC 2022 paper "Positive Pair Distillation Considered Harmful: Continual Meta Metric Learning for Lifelong Object Re-Identification"
- Host: GitHub
- URL: https://github.com/wangkai930418/dwopp_code
- Owner: wangkai930418
- Created: 2022-10-01T12:14:03.000Z (over 3 years ago)
- Default Branch: master
- Last Pushed: 2022-10-05T10:15:49.000Z (over 3 years ago)
- Last Synced: 2025-04-13T15:11:50.555Z (about 1 year ago)
- Topics: bmvc, continual-learning, incremental-learning, knowledge-distillation, meta-learning, meta-metric-learning, metric-learning, object-reid, person-reid
- Language: Python
- Homepage:
- Size: 103 KB
- Stars: 9
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: readme.md
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README
# **[Positive Pair Distillation Considered Harmful: Continual Meta Metric Learning for Lifelong Object Re-Identification (BMVC 2022)](https://arxiv.org/abs/2210.01600)**
Here we afford the code to reproduce the experimental results on Market-1501 dataset for our paper *"Positive Pair Distillation Considered Harmful: Continual Meta Metric Learning for Lifelong Object Re-Identification".*
[Kai Wang](https://scholar.google.com/citations?user=j14vd0wAAAAJ), [Chenshen Wu](https://scholar.google.com/citations?user=FO7GyVwAAAAJ&hl=en), [Andy Bagdanov](https://scholar.google.com/citations?user=_Fk4YUcAAAAJ&hl=en), [Xialei Liu* (corresponding)](https://mmcheng.net/xliu/), [Shiqi Yang](https://www.shiqiyang.xyz/), Shangling Jui and [Joost van de Weijer](https://scholar.google.com/citations?user=Gsw2iUEAAAAJ&hl=en)
## download dataset
Market-1501 dataset can be directly downloaded from http://zheng-lab.cecs.anu.edu.au/Project/project_reid.html
After extracting the files, you need to have the following files structure:
```
|-- market1501
|-- bounding_box_train
|-- bounding_box_test
|-- gt_bbox
|-- gt_query
|-- query
|-- readme.txt
```
Or if you have already had the dataset locally, you can create a soft link to it by:
```
ln -s /your/path/to/market1501 ./
```
## Requirements
All python packages in my experimental environment is listed in *requirements.txt*
## Reproducing
You can directly run the bash files as below.
### Market-1501, DwoPP(Our method)
```
bash CL_DwoPP.sh
```
### Market-1501, DwPP
```
bash CL_DwPP.sh
```
### Market-1501, FT
```
bash CL_FT.sh
```
### Market-1501, Joint training
```
bash joint_train_dmml_market.sh
```
## Others
Other datasets cannot be directly downloaded from their websites due to the privacy issue, please contact the datasets authors. If you have any question, do not hesitate to contact me or post an issue.