The DOI matches its Crossref record; 65 of the 65 references it cites were found in a registry or an index.
Adaptive Wing Loss for Robust Face Alignment via Heatmap Regression
Open accessAbstract
Heatmap regression with a deep network has become one of the mainstream approaches to localize facial landmarks. However, the loss function for heatmap regression is rarely studied. In this paper, we analyze the ideal loss function properties for heatmap regression in face alignment problems. Then we propose a novel loss function, named Adaptive Wing loss, that is able to adapt its shape to different types of ground truth heatmap pixels. This adaptability penalizes loss more on foreground pixels while less on background pixels. To address the imbalance between foreground and background pixels, we also propose Weighted Loss Map, which assigns high weights on foreground and difficult background pixels to help training process focus more on pixels that are crucial to landmark localization. To further improve face alignment accuracy, we introduce boundary prediction and CoordConv with boundary coordinates. Extensive experiments on different benchmarks, including COFW, 300W and WFLW, show our approach outperforms the state-of-the-art by a significant margin on various evaluation metrics. Besides, the Adaptive Wing loss also helps other heatmap regression tasks.
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Cites
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ArcFace: Additive Angular Margin Loss for Deep Face Recognition
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Stacked Hourglass Networks for Human Pose Estimation
Newell, A., Yang, K., & Deng, J. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46484-8_29 · Cited by 5,634
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2D Human Pose Estimation: New Benchmark and State of the Art Analysis
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Cited by
Alzubaidi, L., Bai, J., Al-Sabaawi, A., Santamaría, J., Albahri, A. S., Al-dabbagh, B. S. N., Fadhel, M. A., Manoufali, M., Zhang, J., Al-Timemy, A. H., et al. (2023) · Journal of Big Data · doi:10.1186/s40537-023-00727-2 · Cited by 907
Autonomous robotic laparoscopic surgery for intestinal anastomosis
Saeidi, H., Opfermann, J. D., Kam, M., Wei, S., Leonard, S., Hsieh, M. H., Kang, J. U., & Krieger, A. (2022) · Science Robotics · doi:10.1126/scirobotics.abj2908 · Cited by 384
She, J., Hu, Y., Shi, H., Wang, J., Shen, Q., & Mei, T. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.00618 · Cited by 264
LUVLi Face Alignment: Estimating Landmarks’ Location, Uncertainty, and Visibility Likelihood
Kumar, A., Marks, T. K., Mou, W., Wang, Y., Jones, M., Cherian, A., Koike-Akino, T., Liu, X., & Feng, C. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00826 · Cited by 165
Satellite Pose Estimation Competition 2021: Results and Analyses
Park, T. H., Märtens, M., Jawaid, M., Wang, Z., Chen, B., Chin, T.-J., Izzo, D., & D’Amico, S. (2023) · Acta Astronautica · doi:10.1016/j.actaastro.2023.01.002 · Cited by 91
Metadata
- Authors
- Wang, X., Bo, L., & Fuxin, L.
- Venue
- 2019 IEEE/CVF International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2019-10
- Pages
- 6970-6980
- OpenAlex
- W2982772166
- Cited by
- 313
- References
- 65
- FWCI
- 11.30
- Type
- conference paper
- Language
- en
- Open access
- green
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