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SimCC: A Simple Coordinate Classification Perspective for Human Pose Estimation

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Computer Vision – ECCV 2022 (ECCV 2022)

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Abstract

The 2D heatmap-based approaches have dominated Human Pose Estimation (HPE) for years due to high performance. However, the long-standing quantization error problem in the 2D heatmap-based methods leads to several well-known drawbacks: 1) The performance for the low-resolution inputs is limited; 2) To improve the feature map resolution for higher localization precision, multiple costly upsampling layers are required; 3) Extra post-processing is adopted to reduce the quantization error. To address these issues, we aim to explore a brand new scheme, called SimCC, which reformulates HPE as two classification tasks for horizontal and vertical coordinates. The proposed SimCC uniformly divides each pixel into several bins, thus achieving sub-pixel localization precision and low quantization error. Benefiting from that, SimCC can omit additional refinement post-processing and exclude upsampling layers under certain settings, resulting in a more simple and effective pipeline for HPE. Extensive experiments conducted over COCO, CrowdPose, and MPII datasets show that SimCC outperforms heatmap-based counterparts, especially in low-resolution settings by a large margin. Code is now publicly available at https://github.com/leeyegy/SimCC.

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Notes

  1. 1.

    The upsampling modules used in SimpleBaseline [38] recover the feature map resolution from 1/32\(\times \) to 1/4\(\times \) input size, consisting of three deconvolution layers.

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Acknowledgement

This work is supported in part by the National Natural Science Foundation of China under Grant 62171248, and the PCNL KEY project (PCL2021A07), and in part by the National Natural Science Foundation of China under Grant 61773117.

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Correspondence to Shu-Tao Xia .

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Li, Y. et al. (2022). SimCC: A Simple Coordinate Classification Perspective for Human Pose Estimation. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13666. Springer, Cham. https://doi.org/10.1007/978-3-031-20068-7_6

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