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Dec 30, 2021
Jan 30, 2020

Improving Pedestrian Attribute Recognition With Weakly-Supervised Multi-Scale Attribute-Specific Localization

Code for the paper "Improving Pedestrian Attribute Recognition With Weakly-Supervised Multi-Scale Attribute-Specific Localization", ICCV 2019, Seoul.

[Paper] [Poster]

Contact: chufeng.t@foxmail.com or tcf18@mails.tsinghua.edu.cn

Environment

  • Python 3.6+
  • PyTorch 0.4+

Datasets

The original datasets should be processed to match the DataLoader.

We provide the label lists for training and testing.

Training and Testing

python main.py --approach=inception_iccv --experiment=rap
python main.py --approach=inception_iccv --experiment=rap -e --resume='model_path'

Pretrained Models

We provide the pretrained models for reference, the results may slightly different with the values reported in our paper.

Dataset mA Link
PETA 86.34 Model
RAP 81.86 Model
PA-100K 80.45 Model

Reference

If this work is useful to your research, please cite:

@inproceedings{tang2019improving,
  title={Improving Pedestrian Attribute Recognition With Weakly-Supervised Multi-Scale Attribute-Specific Localization},
  author={Tang, Chufeng and Sheng, Lu and Zhang, Zhaoxiang and Hu, Xiaolin},
  booktitle={Proceedings of the IEEE International Conference on Computer Vision},
  pages={4997--5006},
  year={2019}
}

About

Code for the paper "Improving Pedestrian Attribute Recognition With Weakly-Supervised Multi-Scale Attribute-Specific Localization", ICCV 2019, http://arxiv.org/abs/1910.04562.

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