Fetching the paper…
Reading the bibliography…
Optical flow estimation can be formulated as an end-to-end supervised learning problem, which yields estimates with a superior accuracy-runtime tradeoff compared to alternative methodology.
MacKay, D.J.C.: A practical bayesian framework for backpropagation networks. Neural Computation 4
1992
Earlier work this paper cites.
Nix, D.A., Weigend, A.S.: Estimating the mean and variance of the target probability distribution. In: Neural Networks, 1994. IEEE World Congress on Computational Intelligence. vol. 1, pp. 55–60 vol.1 (June 1994). https://doi.org/10.1109/ICNN.1994.374138
1994
Earlier work this paper cites.
Neal, R.: Bayesian learning for neural networks. PhD thesis, University of Toronto (1996)
1996
Earlier work this paper cites.
Bruhn, A., Weickert, J.: A Confidence Measure for Variational Optic flow Methods, pp. 283–298. Springer Netherlands, Dordrecht (2006), https://doi.org/10.1007/1-4020-3858-8_15
2006
Earlier work this paper cites.
Kondermann, C., Kondermann, D., Jähne, B., Garbe, C.: An Adaptive Confidence Measure for Optical Flows Based on Linear Subspace Projections, pp. 132–141. Springer Berlin Heidelberg, Berlin, Heidelberg (2007), https://doi.org/10.1007/978-3-540-74936-3_14
2007
Earlier work this paper cites.
Kondermann, C., Mester, R., Garbe, C.: A Statistical Confidence Measure for Optical Flows, pp. 290–301. Springer Berlin Heidelberg, Berlin, Heidelberg (2008), https://doi.org/10.1007/978-3-540-88690-7_22
2008
Earlier work this paper cites.
Graves, A.: Practical variational inference for neural networks. In: In Advances in Neural Information Processing Systems (NIPS) 2011. p. 2348–2356 (2011)
2011
Earlier work this paper cites.
Kybic, J., Nieuwenhuis, C.: Bootstrap optical flow confidence and uncertainty measure. Computer Vision and Image Understanding 115
2011
Earlier work this paper cites.
Welling, M., Teh, Y.: Bayesian learning via stochastic gradient Langevin dynamics. In: Proceedings of the 28th International Conference on Machine Learning (ICML’11) (2011)
2011
Earlier work this paper cites.
Aodha, O.M., Humayun, A., Pollefeys, M., Brostow, G.J.: Learning a confidence measure for optical flow. IEEE Transactions on Pattern Analysis and Machine Intelligence 35
2012
Earlier work this paper cites.
Butler, D.J., Wulff, J., Stanley, G.B., Black, M.J.: A naturalistic open source movie for optical flow evaluation. In: European Conference on Computer Vision (ECCV) (2012)
2012
Earlier work this paper cites.
Guzman-Rivera, A., Batra, D., Kohli, P.: Multiple choice learning: Learning to produce multiple structured outputs. In: Int. Conference on Neural Information Processing Systems (NIPS) (2012)
2012
Earlier work this paper cites.
Chen, T., Fox, E., Guestrin, C.: Stochastic gradient Hamiltonian Monte Carlo. In: Proceedings of the 31th International Conference on Machine Learning, (ICML’14) (2014)
2014
Cited alongside, same era.
Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., Darrell, T.: Caffe: Convolutional architecture for fast feature embedding. In: Proc. ACMMM. pp. 675–678 (2014)
2014
Cited alongside, same era.
Bailer, C., Taetz, B., Stricker, D.: Flow fields: Dense correspondence fields for highly accurate large displacement optical flow estimation. In: IEEE Int. Conference on Computer Vision (ICCV) (2015)
2015
Cited alongside, same era.
Blundell, C., Cornebise, J., Kavukcuoglu, K., Wierstra, D.: Weight uncertainty in neural network. In: Proceedings of the 32nd International Conference on Machine Learning (ICML 2015). pp. 1613–1622
2015
Cited alongside, same era.
Mayer, N., Ilg, E., Häusser, P., Fischer, P., Cremers, D., Dosovitskiy, A., Brox, T.: A large dataset to train convolutional networks for disparity, optical flow, and scene flow estimation. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). pp. 4040–4048 (June 2016). https://doi.org/10.1109/CVPR.2016.438
2016
Later among the works it cites.
Chen, Q., Koltun, V.: Photographic image synthesis with cascaded refinement networks. In: IEEE Int. Conference on Computer Vision (ICCV) (2017)
2017
Later among the works it cites.
Huang, G., Li, Y., Pleiss, G.: Snapshot ensembles: Train 1, get M for free. In: Int. Conference on Learning Representations (ICLR) (2017)
2017
Later among the works it cites.
Ilg, E., Mayer, N., Saikia, T., Keuper, M., Dosovitskiy, A., Brox, T.: Flownet 2.0: Evolution of optical flow estimation with deep networks. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017)
2017
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Dosovitskiy, A., Fischer, P., Ilg, E., Häusser, P., Hazırbaş, C., Golkov, V., v.d. Smagt, P., Cremers, D., Brox, T.: Flownet: Learning optical flow with convolutional networks. In: IEEE Int. Conference on Computer Vision (ICCV) (2015)
2015
Cited alongside, same era.
Hernández-Lobato, J., Adams, R.: Probabilistic backpropagation for scalable learning of Bayesian neural networks. In: Proceedings of the 32nd International Conference on Machine Learning (ICML’15) (2015)
2015
Cited alongside, same era.
Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 448–456. PMLR, Lille, France (07–09 Jul 2015), http://proceedings.mlr.press/v37/ioffe15.html
2015
Cited alongside, same era.
Revaud, J., Weinzaepfel, P., Harchaoui, Z., Schmid, C.: EpicFlow: Edge-Preserving Interpolation of Correspondences for Optical Flow. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2015)
2015
Cited alongside, same era.
Gal, Y., Ghahramani, Z.: Dropout as a bayesian approximation: Representing model uncertainty in deep learning. In: Int. Conference on Machine Learning (ICML) (2016)
2016
Cited alongside, same era.
Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: NIPS workshop (2016)
2016
Cited alongside, same era.
Lee, S., Purushwalkam, S., Cogswell, M., Ranjan, V., Crandall, D., Batra, D.: Stochastic multiple choice learning for training diverse deep ensembles. In: Int. Conference on Neural Information Processing Systems (NIPS) (2016)
2016
Cited alongside, same era.
Kendall, A., Gal, Y.: What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision? In: Int. Conference on Neural Information Processing Systems (NIPS) (2017)
2017
Later among the works it cites.
Loshchilov, I., Hutter, F.: Sgdr: Stochastic gradient descent with warm restarts. In: Int. Conference on Learning Representations (ICLR) (2017)
2017
Later among the works it cites.
Novotny, D., Larlus, D., Vedaldi, A.: Learning 3D object categories by looking around them. In: IEEE Int. Conference on Computer Vision (ICCV) (2017)
2017
Later among the works it cites.
Pang, J., Sun, W., Ren, J.S.J., Yang, C., Yan, Q.: Cascade residual learning: A two-stage convolutional neural network for stereo matching. In: IEEE Int. Conference on Computer Vision (ICCV) Workshop (2017)
2017
Later among the works it cites.
Rupprecht, C., Laina, I., DiPietro, R., Baust, M., Tombari, F., Navab, N., Hager, G.D.: Learning in an uncertain world: Representing ambiguity through multiple hypotheses. In: International Conference on Computer Vision (ICCV) (2017)
2017
Later among the works it cites.
Ummenhofer, B., Zhou, H., Uhrig, J., Mayer, N., Ilg, E., Dosovitskiy, A., Brox, T.: Demon: Depth and motion network for learning monocular stereo. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017), http://lmb.informatik.uni-freiburg.de//Publications/2017/UZUMIDB17
2017
Later among the works it cites.
Wannenwetsch, A.S., Keuper, M., Roth, S.: Probflow: Joint optical flow and uncertainty estimation. In: IEEE Int. Conference on Computer Vision (ICCV) (Oct 2017)
2017
Later among the works it cites.