FSR: a feature self-regulation network for partially occluded hand pose estimation
Author:
Funder
National Natural Science Foundation of China
Publisher
Springer Science and Business Media LLC
Subject
Electrical and Electronic Engineering,Signal Processing
Link
https://link.springer.com/content/pdf/10.1007/s11760-021-02069-z.pdf
Reference53 articles.
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3. Deng, X., Zhang, Y., Shi, J., Zhu, Y., Cheng, D., Zuo, D., Cui, Z., Tan, P., Chang, L., Wang, H.: Hand Pose Understanding With Large-Scale Photo-Realistic Rendering Dataset. IEEE Trans. Image Process. 30, 4275–4290 (2021)
4. Ge, L., Cai, Y., Weng, J., Yuan, J.: Hand PointNet: 3D hand pose estimation using point sets. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 8417–8426 (2018)
5. Neverova, N., Wolf, C., Nebout, F., Taylor, G.W.: Hand pose estimation through semi-supervised and weakly-supervised learning. Comput. Vis. Image Underst. 164, 56–67 (2017)
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