Differentiable Multi-Granularity Human Representation Learning for Instance-Aware Human Semantic Parsing

@article{Zhou2021DifferentiableMH,
  title={Differentiable Multi-Granularity Human Representation Learning for Instance-Aware Human Semantic Parsing},
  author={Tianfei Zhou and Wenguan Wang and Si Liu and Yi Yang and Luc Van Gool},
  journal={2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2021},
  pages={1622-1631}
}
  • Tianfei ZhouWenguan Wang L. Gool
  • Published 8 March 2021
  • Computer Science
  • 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
To address the challenging task of instance-aware human part parsing, a new bottom-up regime is proposed to learn category-level human semantic segmentation as well as multi-person pose estimation in a joint and end-to-end manner. It is a compact, efficient and powerful framework that exploits structural information over different human granularities and eases the difficulty of person partitioning. Specifically, a dense-to-sparse projection field, which allows explicitly associating dense human… 

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