Learning-via-Translation

Image-Image Domain Adaptation with Preserved Self-Similarity and Domain-Dissimilarity for Person Re-identification (https://arxiv.org/pdf/1711.07027.pdf)

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Framework Overview

Learning via translation for domain adaptation in person re-ID consists of two steps:

1. Source-target image translation

The first step is to translate the annotated dataset from source domain to target domain in an unsupervised manner.

For more reference, you can find our modified training code and generating code in ./SPGAN. We wrote a detailed README. If you still has some question, feel free to contact me ([email protected]).

2. Feature learning

With the translated dataset that contains labels, feature learning methods are applied to train re-ID models.

Feature Learning

The code for re-ID feature learning is mainly modified from IDE.

Train

1. Train baselines:

 cd $IDE_ROOT
  # train IDE on Market-1501
  ./experiments/market/train_IDE_ResNet_50_baseline.sh  
  # train IDE on DukeMTMC-reID
  ./experiments/duke/train_IDE_ResNet_50_baseline.sh
  # The IDE models for Market-1501 and DukeMTMC-reID are saved under: "out/market_train" and "out/duke_train", respectively
  # If you encounter this problem: bash: ./experiments/market/train_IDE_ResNet_50_baseline.sh: Permission denied
  # Please execute: chmod 777 -R experiments/

2. Train IDE models trained on the translated images:

 cd $IDE_ROOT
  # train IDE on Market-1501
  ./experiments/market/train_IDE_ResNet_50_domain.sh  
  # train IDE on DukeMTMC-reID
  ./experiments/duke/train_IDE_ResNet_50_domain.sh

Feature Extraction

# 1. Market-1501:
cd $IDE_ROOT/market_evaluation
Run Matlab: extract_feature.m
# The IDE features are saved under: "market_evaluation/feat"
# 2. DukeMTMC-reID:
cd $IDE_ROOT/duke_evaluation
Run Matlab: extract_feature.m
# The IDE features are saved under: "duke_evaluation/feat"

Evaluation

  Run Matlab: baseline_evaluation_IDE.m

Trained Model

• Baseline models

  IDE_Market and IDE_Duke: Onedrive; Baidu Yun

• IDE models trained on the translated images:

  IDE_Market2duke (SPGAN) and IDE_Duke2Market (SPGAN): Onedrive; Baidu Yun

Results

(see more details in paper)

Contributing

If you have an implementation for a different framework, or an enhanced version of the current code, feel free to contribute to the repository. For any issues related to the code you can use the issue tracker from GitHub.

References

[1] W. Deng, L. Zheng, G. Kang, Y. Yang, Q. Ye, and J. Jiao. Image-image domain adaptation with preserved self-similarity and domain-dissimilarity for person re-identification. arXiv preprint arXiv:1711.07027, 2017.

[2] L. Zheng, Y. Yang, and A. G. Hauptmann. Person reidentification: Past, present and future. arXiv preprint arXiv:1610.02984, 2016.

License

The code is released under the MIT License. See the attached LICENSE file.