[ACM MM2021] MGH: Metadata Guided Hypergraph Modeling for Unsupervised Person Re-identification

Related tags

Deep Learning MGH.pytorch
Overview

Introduction

This project is developed based on FastReID, which is an ongoing ReID project.

Projects

BUC

In projects/BUC, we implement AAAI 2019 paper A Bottom-Up Clustering Approach to Unsupervised Person Re-Identification. The original implementation is in BUC.

SpCL

In projects/SpCL, we implement NeurIPS 2020 paper Self-paced Contrastive Learning with Hybrid Memory for Domain Adaptive Object Re-ID. The original implementation is in SpCL.

HCT

In projects/HCT, we implement CVPR 2020 paper Hierarchical Clustering with Hard-batch Triplet Loss for Person Re-identification. The original implementation is in HCT

MGH

In projects/MGH, we implement ACM MM 2021 paper MGH: Metadata Guided Hypergraph Modeling for Unsupervised Person Re-identification. Please refer it's README for more details. Paper

Citation

Please cite our work:

@inproceedings{wu2021mgh,
  author    = {Yiming Wu and
               Xintian Wu and 
               Jian Tian and 
               Xi Li},
  title     = {MGH: Metadata Guided Hypergraph Modeling for Unsupervised Person Re-identification},
  booktitle = ACMMM,
  year      = 2021,
  publisher = {{ACM}}}
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Releases(v0.1-alpha)

  • v0.1-alpha(Aug 23, 2021)

    For projects MGH, these models are trained on Market1501, DukeMTMC, and MSMT17. The model files should be organized as follow

    models
├── duke
│   └── model_duke.pth
├── market
│   └── model_market.pth
└── msmt17
    └── model_msmt17.pth

    if you want to test these models, please check projects/MGH/test.sh, and follow projects/MGH/README.md to run testing.

    Source code(tar.gz)
    Source code(zip)
    models.zip(965.39 MB)
Owner
WuYiming
WuYiming
GitHub
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