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Embedding Expansion: Augmentation in Embedding Space for Deep Metric Learning

Official MXNet implementation of Embedding Expansion: Augmentation in Embedding Space for Deep Metric Learning (CVPR 2020)

Byungsoo Ko*, Geonmo Gu* (* Authors contributed equally.)

@NAVER/LINE Vision

• Paper | Supplementary Video | PPT | Poster

Overview

Embedding Expansion

Embedding Expansion (EE) is a novel augmentation method in embedding space which is compatible with existing pair-based metric learning losses.

How it Works

By generating synthetic points with linear interpolation, they can contribute to training a network with augmented information.

Experimental Results

Clustering and retrieval performance (%) on three benchmarks in comparison with other methods. † denotes the HPHN triplet loss, and bold numbers indicate the best score within the same loss.

Getting Started

Requirements

$ pip3 install -r requirements.txt

Prepare Data

• Download CARS196 dataset.

$ mkdir -p data/CARS_196
$ wget http://ai.stanford.edu/~jkrause/car196/car_ims.tgz
$ tar -xvzf car_ims.tgz -C data/CARS_196

Train Models

• Available losses: HPHN-triplet, EE + HPHN-triplet
  • Best recall@1 of EE + HPHN-triplet: 0.745 (0.716 in paper)
• Note: The meaning of epoch in the paper and implementation is 'number of classes * number of instance per class'. So, the total epochs can be seen unusually high but it does not indicate slow convergence.

# EE + HPHN-triplet (n_inner_points=2)
$ python3 main.py --gpu_idx=0 --data_name=cars196 \
--backbone=googlenet --embed_dim=512 \
--loss=hphn_triplet --n_inner_pts=2 --ee_l2norm=True \
--seed=0 --batch_size=128 --image_size=227 \
--optim=adam --lr=0.0001 --lr_decay_factor=0.5 --lr_decay_epochs=400,800,1200,1600 \
--epochs=4000 --eval_epoch_term=5 --save_dir=results/inner-2

# HPHN-triplet (n_inner_points=0)
$ python3 main.py --gpu_idx=0 --data_name=cars196 \
--backbone=googlenet --embed_dim=512 \
--loss=hphn_triplet --n_inner_pts=0 --ee_l2norm=True \
--seed=0 --batch_size=128 --image_size=227 \
--optim=adam --lr=0.0001 --lr_decay_factor=0.5 --lr_decay_epochs=400,800,1200,1600 \
--epochs=4000 --eval_epoch_term=5 --save_dir=results/inner-0

Check Test Results

$ tensorboard --logdir=results --port=10000

• dark blue: EE + HPHN-triplet (n_inner_points=2)

• sky blue: HPHN-triplet (n_inner_points=0)

Citation

If you find Embedding Expansion useful in your research, please consider to cite the following paper.

@inproceedings{ko2020embedding,
    title={Embedding Expansion: Augmentation in Embedding Space for Deep Metric Learning},
    author={Ko, Byungsoo and Gu, Geonmo},
    booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
    year={2020}
}

Acknowledgement

This repository is refactored based on the implementation of Combination of Multiple Global Descriptors for Image Retrieval (CGD). (Special thanks to the authors :D)

If you are interested in extended implementation of deep metric learning, please check the CGD's repository and consider to cite the following paper.

• Arxiv | Github

@article{jun2019combination,
  title={Combination of Multiple Global Descriptors for Image Retrieval},
  author={Jun, HeeJae and Ko, ByungSoo and Kim, Youngjoon and Kim, Insik and Kim, Jongtack},
  journal={arXiv preprint arXiv:1903.10663},
  year={2019}
}

License

Copyright (c) 2020-present NAVER Corp.

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