InsPose: Instance-Aware Networks for Single-Stage Multi-Person Pose Estimation

@article{Shi2021InsPoseIN,
  title={InsPose: Instance-Aware Networks for Single-Stage Multi-Person Pose Estimation},
  author={Dahu Shi and Xing Wei and Xiaodong Yu and Wenming Tan and Ye Ren and Shiliang Pu},
  journal={Proceedings of the 29th ACM International Conference on Multimedia},
  year={2021}
}
  • Dahu Shi, Xing Wei, +3 authors Shiliang Pu
  • Published 2021
  • Computer Science
  • Proceedings of the 29th ACM International Conference on Multimedia
Multi-person pose estimation is an attractive and challenging task. Existing methods are mostly based on two-stage frameworks, which include top-down and bottom-up methods. Two-stage methods either suffer from high computational redundancy for additional person detectors or they need to group keypoints heuristically after predicting all the instance-agnostic keypoints. The single-stage paradigm aims to simplify the multi-person pose estimation pipeline and receives a lot of attention. However… Expand

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References

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TLDR
A novel network structure called Cascaded Pyramid Network (CPN) is presented which targets to relieve the problem from these "hard" keypoints, with state-of-art results on the COCO keypoint benchmark, with average precision at 73.0. Expand
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