Cite

[1] Alex Kendall and Roberto Cipolla. Modelling uncertainty in deep learning for camera relocalization. In 2016 IEEE International Conference on Robotics and Automation (ICRA), pages 4762–4769. IEEE, 2016.10.1109/ICRA.2016.7487679 Search in Google Scholar

[2] Vijay Badrinarayanan Alex Kendall and Roberto Cipolla. Bayesian segnet: Model uncertainty in deep convolutional encoder-decoder architectures for scene understanding. In Gabriel Brostow Tae-Kyun Kim, Stefanos Zafeiriou and Krystian Mikolajczyk, editors, em Proceedings of the British Machine Vision Conference (BMVC), pages 57.1–57.12. BMVA Press, September 2017. Search in Google Scholar

[3] Abhijit Guha Roy, Sailesh Conjeti, Nassir Navab, Christian Wachinger, Alzheimer’s Disease Neuroimaging Initiative, et al. Bayesian quicknat: model uncertainty in deep whole-brain segmentation for structure-wise quality control. NeuroImage, 195:11–22, 2019.10.1016/j.neuroimage.2019.03.042 Search in Google Scholar

[4] Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. Imagenet classification with deep convolutional neural networks. In Advances in Neural Information Processing Systems, pages 1097–1105, 2012. Search in Google Scholar

[5] Karen Simonyan and Andrew Zisserman. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556, 2014. Search in Google Scholar

[6] Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich. Going deeper with convolutions. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 1–9, 2015.10.1109/CVPR.2015.7298594 Search in Google Scholar

[7] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 770–778, 2016. Search in Google Scholar

[8] Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun. Faster r-cnn: Towards real-time object detection with region proposal networks. In C. Cortes, N. Lawrence, D. Lee, M. Sugiyama, and R. Garnett, editors, Advances in Neural Information Processing Systems, volume 28. Curran Associates, Inc., 2015. Search in Google Scholar

[9] Joseph Redmon, Santosh Divvala, Ross Girshick, and Ali Farhadi. You only look once: Unified, real-time object detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2016.10.1109/CVPR.2016.91 Search in Google Scholar

[10] Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Scott Reed, Cheng-Yang Fu, and Alexander C Berg. Ssd: Single shot multibox detector. In European Conference on Computer Vision, pages 21–37. Springer, 2016.10.1007/978-3-319-46448-0_2 Search in Google Scholar

[11] Jonathan Long, Evan Shelhamer, and Trevor Darrell. Fully convolutional networks for semantic segmentation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 3431–3440, 2015.10.1109/CVPR.2015.7298965 Search in Google Scholar

[12] Vijay Badrinarayanan, Alex Kendall, and Roberto Cipolla. Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(12):2481–2495, 2017.10.1109/TPAMI.2016.2644615 Search in Google Scholar

[13] Liang-Chieh Chen, George Papandreou, Iasonas Kokkinos, Kevin Murphy, and Alan L Yuille. Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(4):834–848, 2017.10.1109/TPAMI.2017.2699184 Search in Google Scholar

[14] Alexander Toshev and Christian Szegedy. Deep-pose: Human pose estimation via deep neural networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 1653–1660, 2014.10.1109/CVPR.2014.214 Search in Google Scholar

[15] Alex Kendall, Matthew Grimes, and Roberto Cipolla. Posenet: A convolutional network for real-time 6-dof camera relocalization. In Proceedings of the IEEE International Conference on Computer Vision, pages 2938–2946, 2015.10.1109/ICCV.2015.336 Search in Google Scholar

[16] Wadim Kehl, Fabian Manhardt, Federico Tombari, Slobodan Ilic, and Nassir Navab. Ssd-6d: Making rgb-based 3d detection and 6d pose estimation great again. In Proceedings of the IEEE International Conference on Computer Vision, pages 1521–1529, 2017.10.1109/ICCV.2017.169 Search in Google Scholar

[17] Xucong Zhang, Yusuke Sugano, Mario Fritz, and Andreas Bulling. It’s written all over your face: Full-face appearance-based gaze estimation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, July 2017.10.1109/CVPRW.2017.284 Search in Google Scholar

[18] Xucong Zhang, Yusuke Sugano, Mario Fritz, and Andreas Bulling. Mpiigaze: Real-world dataset and deep appearance-based gaze estimation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 41(1):162–175, 2019.10.1109/TPAMI.2017.2778103 Search in Google Scholar

[19] Seonwook Park, Adrian Spurr, and Otmar Hilliges. Deep pictorial gaze estimation. In Proceedings of the European Conference on Computer Vision, pages 721–738, 2018.10.1007/978-3-030-01261-8_44 Search in Google Scholar

[20] Rajeev Ranjan, Vishal M Patel, and Rama Chellappa. Hyperface: A deep multi-task learning framework for face detection, landmark localization, pose estimation, and gender recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 41(1):121–135, 2017.10.1109/TPAMI.2017.2781233 Search in Google Scholar

[21] Yilun Chen, Zhicheng Wang, Yuxiang Peng, Zhiqiang Zhang, Gang Yu, and Jian Sun. Cascaded pyramid network for multi-person pose estimation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 7103–7112, 2018.10.1109/CVPR.2018.00742 Search in Google Scholar

[22] Bin Xiao, Haiping Wu, and Yichen Wei. Simple baselines for human pose estimation and tracking. In Proceedings of the European Conference on Computer Vision, pages 466–481, 2018.10.1007/978-3-030-01231-1_29 Search in Google Scholar

[23] George Papandreou, Tyler Zhu, Nori Kanazawa, Alexander Toshev, Jonathan Tompson, Chris Bregler, and Kevin Murphy. Towards accurate multi-person pose estimation in the wild. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 4903–4911, 2017.10.1109/CVPR.2017.395 Search in Google Scholar

[24] Stéphane Lathuilière, Pablo Mesejo, Xavier Alameda-Pineda, and Radu Horaud. A comprehensive analysis of deep regression. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019.10.1109/TPAMI.2019.291052330990175 Search in Google Scholar

[25] Wei-Yin Loh. Classification and regression trees. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 1(1):14–23, 2011.10.1002/widm.8 Search in Google Scholar

[26] Harris Drucker, Christopher JC Burges, Linda Kaufman, Alex J Smola, and Vladimir Vapnik. Support vector regression machines. In Advances in Neural Information Processing Systems, pages 155–161, 1997. Search in Google Scholar

[27] Dipendra Jha, Logan Ward, Zijiang Yang, Christopher Wolverton, Ian Foster, Wei-keng Liao, Alok Choudhary, and Ankit Agrawal. Irnet: A general purpose deep residual regression framework for materials discovery. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pages 2385–2393, 2019. Search in Google Scholar

[28] Dongwei Chen, Fei Hu, Guokui Nian, and Tiantian Yang. Deep residual learning for nonlinear regression. Entropy, 22(2):193, 2020.10.3390/e22020193751661933285968 Search in Google Scholar

[29] Lianfa Li, Ying Fang, Jun Wu, Jinfeng Wang, and Yong Ge. Encoder-decoder full residual deep networks for robust regression and spatiotemporal estimation. IEEE Transactions on Neural Networks and Learning Systems, 2020.10.1109/TNNLS.2020.3017200866590332881694 Search in Google Scholar

[30] David JC MacKay. A practical bayesian framework for backpropagation networks. Neural Computation, 4(3):448–472, 1992.10.1162/neco.1992.4.3.448 Search in Google Scholar

[31] Alex Graves. Practical variational inference for neural networks. In Advances in Neural Information Processing Systems, pages 2348–2356, 2011. Search in Google Scholar

[32] Charles Blundell, Julien Cornebise, Koray Kavukcuoglu, and Daan Wierstra. Weight uncertainty in neural networks. arXiv preprint arXiv:1505.05424, 2015. Search in Google Scholar

[33] Yarin Gal and Zoubin Ghahramani. Dropout as a Bayesian approximation: Representing model uncertainty in deep learning. In Maria Florina Balcan and Kilian Q. Weinberger, editors, Proceedings of The 33rd International Conference on Machine Learning, volume 48 of Proceedings of Machine Learning Research, pages 1050–1059, New York, New York, USA, 20–22 Jun 2016. PMLR. Search in Google Scholar

[34] David Krueger, Chin-Wei Huang, Riashat Islam, Ryan Turner, Alexandre Lacoste, and Aaron Courville. Bayesian hypernetworks. arXiv preprint arXiv:1710.04759, 2017. Search in Google Scholar

[35] Christos Louizos and Max Welling. Multiplicative normalizing flows for variational bayesian neural networks. arXiv preprint arXiv:1703.01961, 2017. Search in Google Scholar

[36] Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. Dropout: a simple way to prevent neural networks from overfitting. The Journal of Machine Learning Research, 15(1):1929–1958, 2014. Search in Google Scholar

[37] Sergey Ioffe and Christian Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:1502.03167, 2015. Search in Google Scholar

[38] Vinod Nair and Geoffrey E Hinton. Rectified linear units improve restricted boltzmann machines. In ICML, 2010. Search in Google Scholar

[39] L. Shi, C. Copot, and S. Vanlanduit. A deep regression model for safety control in visual servoing applications. In 2020 Fourth IEEE International Conference on Robotic Computing (IRC), page preprint, 2020.10.1109/IRC.2020.00063 Search in Google Scholar

[40] Tim Pearce, Alexandra Brintrup, Mohamed Zaki, and Andy Neely. High-quality prediction intervals for deep learning: A distribution-free, ensembled approach. In International Conference on Machine Learning, pages 4075–4084, 2018. Search in Google Scholar

eISSN:
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Language:
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Computer Sciences, Databases and Data Mining, Artificial Intelligence