Multi-Domain Pose Network for Multi-Person Pose Estimation and Tracking
Abstract
Multi-person human pose estimation and tracking in the wild is important and challenging. For training a powerful model, large-scale training data are crucial. While there are several datasets for human pose estimation, the best practice for training on multi-dataset has not been investigated. In this paper, we present a simple network called Multi-Domain Pose Network (MDPN) to address this problem. By treating the task as multi-domain learning, our methods can learn a better representation for pose prediction. Together with prediction heads fine-tuning and multi-branch combination, it shows significant improvement over baselines and achieves the best performance on PoseTrack ECCV 2018 Challenge without additional datasets other than MPII and COCO.
Cite
Text
Guo et al. "Multi-Domain Pose Network for Multi-Person Pose Estimation and Tracking." European Conference on Computer Vision Workshops, 2018. doi:10.1007/978-3-030-11012-3_17Markdown
[Guo et al. "Multi-Domain Pose Network for Multi-Person Pose Estimation and Tracking." European Conference on Computer Vision Workshops, 2018.](https://mlanthology.org/eccvw/2018/guo2018eccvw-multidomain/) doi:10.1007/978-3-030-11012-3_17BibTeX
@inproceedings{guo2018eccvw-multidomain,
title = {{Multi-Domain Pose Network for Multi-Person Pose Estimation and Tracking}},
author = {Guo, Hengkai and Tang, Tang and Luo, Guozhong and Chen, Riwei and Lu, Yongchen and Wen, Linfu},
booktitle = {European Conference on Computer Vision Workshops},
year = {2018},
pages = {209-216},
doi = {10.1007/978-3-030-11012-3_17},
url = {https://mlanthology.org/eccvw/2018/guo2018eccvw-multidomain/}
}