Official Implementation for MAPS

MAPS: A Noise-Robust Progressive Learning Approach for Source-Free Domain Adaptive Keypoint Detection

This implementation is based on UDA_PoseEstimation.

Framework:

1. train on the source domain;
2. Construct the proxy source domain and train on target dataset.

Dataset:

• Please put the hand datasets H3D and RHD under the folder './hand_data/', put the human datasets LSP and SURREAL under the folder './human_data'

Training:

1. Hand dataset

   # train source model
   python hand_src.py 
   # train target model
   python hand_tgt_proxy.py

2. Human dataset

   # train source model
   python human_src.py
   # train target model
   python human_tgt_proxy.py

Citation

If you find this code useful for your research, please cite our paper

@article{ding2023maps,
  title={MAPS: A Noise-Robust Progressive Learning Approach for Source-Free Domain Adaptive Keypoint Detection},
  author={Ding, Yuhe and Liang, Jian and Jiang, Bo and Zheng, Aihua and He, Ran},
  journal={arXiv preprint arXiv:2302.04589},
  year={2023}
}