The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Robust Optimization for Deep Regression
Abstract
Convolutional Neural Networks (ConvNets) have successfully contributed to improve the accuracy of regression-based methods for computer vision tasks such as human pose estimation, landmark localization, and object detection. The network optimization has been usually performed with L2 loss and without considering the impact of outliers on the training process, where an outlier in this context is defined by a sample estimation that lies at an abnormal distance from the other training sample estimations in the objective space. In this work, we propose a regression model with ConvNets that achieves robustness to such outliers by minimizing Tukey's biweight function, an M-estimator robust to outliers, as the loss function for the ConvNet. In addition to the robust loss, we introduce a coarse-to-fine model, which processes input images of progressively higher resolutions for improving the accuracy of the regressed values. In our experiments, we demonstrate faster convergence and better generalization of our robust loss function for the tasks of human pose estimation and age estimation from face images. We also show that the combination of the robust loss function with the coarse-to-fine model produces comparable or better results than current state-of-the-art approaches in four publicly available human pose estimation datasets.
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Cites
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
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“Furthermore, weighting training samples also conforms with the idea of curriculum [5] and self-paced learning [22], where each training sample has different contribution to the minimization depending on its error.”
BackgroundNeural Networks: Tricks of the Trade: Second Edition
(2012) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-35289-8 · Cited by 2,116
ChaLearn Looking at People Challenge 2014: Dataset and Results
Escalera, S., Baró, X., Gonzàlez, J., Bautista, M. A., Madadi, M., Reyes, M., Ponce-López, V., Escalante, H. J., Shotton, J., & Guyon, I. (2015) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-16178-5_32 · Cited by 211
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Kendall, A., & Cipolla, R. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.694 · Cited by 803
AggNet: Deep Learning From Crowds for Mitosis Detection in Breast Cancer Histology Images
Albarqouni, S., Baur, C., Achilles, F., Belagiannis, V., Demirci, S., & Navab, N. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2528120 · Cited by 606
Self-citation2D/3D Pose Estimation and Action Recognition Using Multitask Deep Learning
Luvizon, D. C., Picard, D., & Tabia, H. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00539 · Cited by 555
Neural Body Fitting: Unifying Deep Learning and Model Based Human Pose and Shape Estimation
Omran, M., Lassner, C., Pons-Moll, G., Gehler, P., & Schiele, B. (2018) · 2018 International Conference on 3D Vision (3DV) · doi:10.1109/3dv.2018.00062 · Cited by 498
Deep Label Distribution Learning With Label Ambiguity
Gao, B.-B., Xing, C., Xie, C.-W., Wu, J., & Geng, X. (2017) · IEEE Transactions on Image Processing · doi:10.1109/tip.2017.2689998 · Cited by 508
Metadata
- Authors
- Belagiannis, V., Rupprecht, C., Carneiro, G., & Navab, N.
- Venue
- 2015 IEEE International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2015-12
- Pages
- 2830-2838
- OpenAlex
- W2116626343
- Cited by
- 179
- References
- 6
- FWCI
- 6.79
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Belagiannis, V., Rupprecht, C., Carneiro, G., & Navab, N. (2015). Robust Optimization for Deep Regression. 2015 IEEE International Conference on Computer Vision (ICCV). https://doi.org/10.1109/iccv.2015.324
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