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Densely-Anchored Sampling for Deep Metric Learning (ECCV 2022)

Created by Lizhao Liu, Shangxin Huang from South China University of Technology.

This repository contains the official PyTorch-implementation of our ECCV 2022 paper Densely-Anchored Sampling for Deep Metric Learning.

In particular, we release the code for reproducing the results of Table (1) and Table (2) in the main paper.

Suggestions are always welcome!

Usage

Environment

• TITAN XP with CUDA 11.2
• PyTorch 1.8.1
• torchvision 0.9.1
• faiss 1.6.1 (GPU version) for embedding clustering and retrieval
• A full list of dependencies are in requirements.txt, you can use

  pip install -r requirements.txt
  

  to download all the dependencies.

Dataset Preparation

We use three datasets: CUB, CARS, SOP in the paper. Download them via links below and unzip them after downloaded.

• CUB200-2011 (1.08 GB): https://www.dropbox.com/s/tjhf7fbxw5f9u0q/cub200.tar?dl=0
• CARS196 (1.86 GB): https://www.dropbox.com/s/zi2o92hzqekbmef/cars196.tar?dl=0
• SOP (2.84 GB): https://www.dropbox.com/s/fu8dgxulf10hns9/online_products.tar?dl=0
• In-Shop (Multiple Files): http://mmlab.ie.cuhk.edu.hk/projects/DeepFashion/InShopRetrieval.html

After unzip, the data fold structure will look like these:

• For CUB/CARS:

cub200/cars196
└───images
|    └───001.Black_footed_Albatross
|           │   Black_Footed_Albatross_0001_796111
|           │   ...
|    ...

• For SOP:

online_products
└───images
|    └───bicycle_final
|           │   111085122871_0.jpg
|    ...
|
└───Info_Files
|    │   bicycle.txt
|    │   ...

• For In-Shop

in-shop
└─img
|    └─MEN
|         └─Denim
|               └─id_00000080
|                  │   01_1_front.jpg
|                  │   ...
|               ...
|         ...
|    ...
|
└─Eval
|  │   list_eval_partition.txt

Reproduce the results in Table (1) and Table (2)

• Change the DATA_ROOT variable in table1.sh and table2.sh to the path that contains the above datasets
• To produce the results in Table (1), simply run the follow command

bash table1.sh

• To produce the results in Table (2), simply run the follow command

bash table2.sh

Quantitative Results

Qualitative Results

• Top 3 retrieved results using the model trained by contrastive loss and distance-weighted sampling method that are equipped w/ or w/o on CARS. The expected and unexpected results are framed by green and red rectangles, respectively.

• Top 3 retrieved results using the model trained by margin loss that are equipped w/ or w/o on SOP. The expected and unexpected results are framed by green and red rectangles, respectively.

• Top 6 retrieved results with different scales on CARS. The expected and unexpected results are framed by green and red rectangles, respectively

• Top 3 retrieved results with MTS on CARS. The expected and unexpected results are framed by green and red rectangles, respectively.

Acknowledgement

We borrow many codes from Revisiting_Deep_Metric_Learning_PyTorch. Please show some support!

Citation

If you find this code helpful for your research, please consider citing

@inproceedings{liu2022das,
  title={DAS: Densely-Anchored Sampling for Deep Metric Learning},
  author={Liu, Lizhao and Huang, Shangxin and Zhuang, Zhuangwei and Yang, Ran and Tan, Mingkui and Wang, Yaowei},
  booktitle={European Conference on Computer Vision},
  year={2022},
  organization={Springer}
}