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IJCV 2024: Transformer-based ReID Survey

Transformer for Object Re-Identification: A Survey. arXiv

An implementation of UntransReID for unsupervised Re-ID is HERE.
An implementation of UntransReID for cross-modality visible-infrared unsupervised Re-ID is HERE.
An implementation of the unified experimental standard for animal Re-ID is HERE.

Highlights

An in-depth analysis of Transformer's strengths, highlighting its impact across four key Re-ID directions: image/video-based, limited data/annotations, cross-modal, and special scenarios.
A new Transformer-based unsupervised baseline, UntransReID, achieving state-of-the-art performance on both single/cross modal Re-ID.
A unified experimental standard for animal Re-ID, designed to address its unique challenges and evaluate the potential of Transformer-based approaches.

Citation

Please kindly cite this paper in your publications if it helps your research:

@article{ye2024transformer,
  title={Transformer for Object Re-Identification: A Survey},
  author={Ye, Mang and Chen, Shuoyi and Li, Chenyue and Zheng, Wei-Shi and Crandall, David and Du, Bo},
  journal={arXiv preprint arXiv:2401.06960},
  year={2024}
}

TPAMI 2021 ReID-Survey with a Powerful AGW Baseline

Deep Learning for Person Re-identification: A Survey and Outlook. PDF with supplementary materials. arXiv

An implementation of AGW for cross-modality visible-infrared Re-ID is HERE.
An implementation of AGW for video Re-ID is HERE
An implementation of AGW for partial Re-ID is HERE.

A simplified introduction in Chinese on 知乎.

Highlights

A comprehensive survey with in-depth analysis for closed- and open-world person Re-ID in recent years (2016-2020).
A new evaluation metric, namely mean Inverse Negative Penalty (mINP), which measures the ability to find the hardest correct match.
A new AGW baseline with non-local Attention block, Generalized mean pooling and Weighted regularization triplet. It acheieves competitive performance on FOUR challenging Re-ID tasks, including single-modality image-based Re-ID, video-based Re-ID, Partial Re-ID and cross-modality Re-ID.

AGW on Single-Modality Image Re-ID with mINP

DukeMTMC dataset

Method	Pretrained	Rank@1	mAP	mINP	Model	Paper
BagTricks	ImageNet	86.4%	76.4%	40.7%	Code	Bag of Tricks and A Strong Baseline for Deep Person Re-identification. In ArXiv 19. PDF
ABD-Net	ImageNet	89.0%	78.6%	42.1%	Code	ABD-Net: Attentive but Diverse Person Re-Identification. In ICCV 19. PDF
AGW	ImageNet	89.0%	79.6%	45.7%	GoogleDrive	Deep Learning for Person Re-identification: A Survey and Outlook

Market-1501 dataset

Method	Pretrained	Rank@1	mAP	mINP	Model	Paper
BagTricks	ImageNet	94.5%	85.9%	59.4%	Code	Bag of Tricks and A Strong Baseline for Deep Person Re-identification. In ArXiv 19. arXiv
ABD-Net	ImageNet	95.6%	88.3%	66.2%	Code	ABD-Net: Attentive but Diverse Person Re-Identification. In ICCV 19. PDF
AGW	ImageNet	95.1%	87.8%	65.0%	GoogleDrive	Deep Learning for Person Re-identification: A Survey and Outlook. In ArXiv 20. arXiv

CUHK03 dataset

Method	Pretrained	Rank@1	mAP	mINP	Model	Paper
BagTricks	ImageNet	58.0%	56.6%	43.8%	Code	Bag of Tricks and A Strong Baseline for Deep Person Re-identification. In ArXiv 19. PDF
AGW	ImageNet	63.6%	62.0%	50.3%	GoogleDrive	Deep Learning for Person Re-identification: A Survey and Outlook. In ArXiv 20. arXiv

MSMT17 dataset

Method	Pretrained	Rank@1	mAP	mINP	Model	Paper
BagTricks	ImageNet	63.4%	45.1%	12.4%	Code	Bag of Tricks and A Strong Baseline for Deep Person Re-identification. In ArXiv 19. arXiv
AGW	ImageNet	68.3%	49.3%	14.7%	GoogleDrive	Deep Learning for Person Re-identification: A Survey and Outlook. In ArXiv 20. arXiv

Quick Start

1. Prepare dataset

Create a directory to store reid datasets under this repo, taking Market1501 for example

cd ReID-Survey
mkdir toDataset

Set _C.DATASETS.ROOT_DIR = ('./toDataset') inconfig/defaults.py
Download dataset to toDataset/ from http://www.liangzheng.org/Project/project_reid.html
Extract dataset and rename to market1501. The data structure would like:

toDataset
    market1501 
        bounding_box_test/
        bounding_box_train/
        ......

Partial-REID and Partial-iLIDS datasets are provided by https://github.com/lingxiao-he/Partial-Person-ReID

2. Install dependencies

pytorch=1.0.0
torchvision=0.2.1
pytorch-ignite=0.1.2
yacs
scipy=1.2.1
h5py

3. Train

To train a AGW model with on Market1501 with GPU device 0, run similarly:

python3 tools/main.py --config_file='configs/AGW_baseline.yml' MODEL.DEVICE_ID "('0')" DATASETS.NAMES "('market1501')" OUTPUT_DIR "('./log/market1501/Experiment-AGW-baseline')"

4. Test

To test a AGW model with on Market1501 with weight file './pretrained/dukemtmc_AGW.pth', run similarly:

python3 tools/main.py --config_file='configs/AGW_baseline.yml' MODEL.DEVICE_ID "('0')" DATASETS.NAMES "('market1501')"  MODEL.PRETRAIN_CHOICE "('self')" TEST.WEIGHT "('./pretrained/market1501_AGW.pth')" TEST.EVALUATE_ONLY "('on')" OUTPUT_DIR "('./log/Test')"

Citation

Please kindly cite this paper in your publications if it helps your research:

@article{pami21reidsurvey,
  title={Deep Learning for Person Re-identification: A Survey and Outlook},
  author={Ye, Mang and Shen, Jianbing and Lin, Gaojie and Xiang, Tao and Shao, Ling and Hoi, Steven C. H.},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
  year={2021},
}

Contact: mangye16@gmail.com