Pixel-in-Pixel Net: Towards Efficient Facial Landmark Detection in the Wild
Author:
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Computer Vision and Pattern Recognition,Software
Link
https://link.springer.com/content/pdf/10.1007/s11263-021-01521-4.pdf
Reference75 articles.
1. Bansal, A., Nanduri, A., Castillo, C. D., Ranjan R., & Chellappa, R., (2016). Umdfaces: An annotated face dataset for training deep networks. arXiv:1611.01484
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3. Burgos-Artizzu, X. P., Perona, P., & Dollár, P., (2013). Robust face landmark estimation under occlusion. In: Proceedings of the IEEE International Conference on Computer Vision (pp. 1513-1520).
4. Chandran, P., Bradley, D., Gross, M., & Beeler, T., (2020). Attention-driven cropping for very high resolution facial landmark detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 5861-5870).
5. Chen, D., Hua, G., Wen, F., & Sun, J., (2016). Supervised transformer network for efficientface detection. In: European Conference on Computer Vision (pp. 122-138). Springer, Cham.
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