Strong but simple baseline with dual-granularity triplet loss for VT-ReID

Pytorch code for "Strong but Simple Baseline with Dual-Granularity Triplet Loss for Visible-Thermal Person Re-Identification"(arxiv).

Highlights

• Our proposed dual-granularity triplet loss well organizes the sample-based triplet loss and center-based triplet loss in a hierarchical fine to coarse granularity manner, just with some simple configurations of typical operations, such as pooling and batch normalization.

• Experiments on RegDB and SYSU-MM01 datasets show that our DGTL can improve the VT-ReID performance with only the global features by large margins, which can be a strong VT-ReID baseline to boost the future research with high quality.

Results

Dataset	Rank1	mAP		Rank1	mAP
	visible to	thermal		thermal to	visible
RegDB	83.92%	73.78%		81.59%	71.65%
	all	search		indoor	serach
SYSU-MM01	57.34%	55.13%		63.11%	69.20%

Usage

Our code extends the pytorch implementation of Cross-Modal-Re-ID-baseline in Github. Please refer to the offical repo for details of data preparation.

Training

Train a model for RegDB by

python train.py --dataset regdb --lr 0.1 --gpu 0 --bpool max --cpool max --hcloss HcTri

Train a model for SYSU-MM01 by

python train.py --dataset sysu --lr 0.1 --batch-size 6 --num_pos 8 --gpu 1 --bpool avg --cpool max --hcloss HcTri --margin_hc 0.5

Parameters: More parameters can be found in the manuscript and code.

4. Citation

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

@article{liu2020parameter,
  title={Parameter sharing exploration and hetero-center triplet loss for visible-thermal person re-identification},
  author={Liu, Haijun and Tan, Xiaoheng and Zhou, Xichuan},
  journal={IEEE Transactions on Multimedia},
  volume={23},
  pages={4414--4425},
  year={2020},
  publisher={IEEE}
}

@article{liu2021strong,
  title={Strong but simple baseline with dual-granularity triplet loss for visible-thermal person re-identification},
  author={Liu, Haijun and Chai, Yanxia and Tan, Xiaoheng and Li, Dong and Zhou, Xichuan},
  journal={IEEE Signal Processing Letters},
  volume={28},
  pages={653--657},
  year={2021},
  publisher={IEEE}
}

@article{Tan2022AFS,
  title={A Fourier-Based Semantic Augmentation for Visible-Thermal Person Re-Identification},
  author={Xiaoheng Tan and Yanxia Chai and Fenglei Chen and Haijun Liu},
  journal={IEEE Signal Processing Letters},
  year={2022},
  volume={29},
  pages={1684-1688}
}