STADB_ReID

Official implementation of "STADB: A Self-Thresholding Attention Guided ADB Network for Person Re-identification", Bo Jiang, Sheng Wang, Xiao Wang, Aihua Zheng [arxiv]

Background and Motivation

Recently, Batch DropBlock network (BDB) has demonstrated its effectiveness on person image representation and re-identification task via feature erasing. However, BDB drops the features randomly which may lead to sub-optimal results. In this paper, we propose a novel Self-Thresholding attention guided Adaptive DropBlock network (STADB) for person re-ID which can adaptively erase the most discriminative regions. Specifically, STADB first obtains an attention map by channel-wise pooling and returns a drop mask by thresholding the attention map. Then, the input features and self-thresholding attention guided drop mask are multiplied to generate the dropped feature maps. In addition, STADB utilizes the spatial and channel attention to learn a better feature map and iteratively trains the feature dropping module for person re-ID. Experiments on several benchmark datasets demonstrate that the proposed STADB outperforms many other related methods for person re-ID.

Framework

Feature Visualization && Results

Environment

pip install python3
pip install cython
pip install torch
pip install torchvision
pip install scikit-learn
pip install tensorboardX
pip install fire

Training

Here, we take the Market1501 dataset as an example:

python main_reid.py train --save_dir='./pytorch-ckpt/market-bfe' --max_epoch=400 --eval_step=30 --dataset=market1501 --test_batch=128 --train_batch=128 --optim=adam --adjust_lr

Testing

python main_reid.py train --save_dir='./pytorch-ckpt/market_bfe' --model_name=bfe --train_batch=32 --test_batch=32 --dataset=market1501 --pretrained_model='./pytorch-ckpt/market_bfe/best_model.pth.tar' --evaluate

Citation

If you find this work useful for your research, please cite this paper:

@article{bojiang2020stadb,
  title={STADB: A Self-Thresholding Attention Guided ADB Network for Person Re-identification},
  author={Bo Jiang, Sheng Wang, Xiao Wang, Aihua Zheng},
  journal={arXiv:2007.03584},
  year={2020}
}

If you have any questions about this work, please submit an issue or send emails to the authors. Thanks for your attention!