{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T18:44:58Z","timestamp":1772909098521,"version":"3.50.1"},"publisher-location":"Cham","reference-count":30,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030012335","type":"print"},{"value":"9783030012342","type":"electronic"}],"license":[{"start":{"date-parts":[[2018,1,1]],"date-time":"2018-01-01T00:00:00Z","timestamp":1514764800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2018,1,1]],"date-time":"2018-01-01T00:00:00Z","timestamp":1514764800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018]]},"DOI":"10.1007\/978-3-030-01234-2_40","type":"book-chapter","created":{"date-parts":[[2018,10,5]],"date-time":"2018-10-05T16:13:11Z","timestamp":1538755991000},"page":"677-693","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":140,"title":["Uncertainty Estimates and Multi-hypotheses Networks for Optical Flow"],"prefix":"10.1007","author":[{"given":"Eddy","family":"Ilg","sequence":"first","affiliation":[]},{"given":"\u00d6zg\u00fcn","family":"\u00c7i\u00e7ek","sequence":"additional","affiliation":[]},{"given":"Silvio","family":"Galesso","sequence":"additional","affiliation":[]},{"given":"Aaron","family":"Klein","sequence":"additional","affiliation":[]},{"given":"Osama","family":"Makansi","sequence":"additional","affiliation":[]},{"given":"Frank","family":"Hutter","sequence":"additional","affiliation":[]},{"given":"Thomas","family":"Brox","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2018,10,6]]},"reference":[{"issue":"5","key":"40_CR1","doi-asserted-by":"publisher","first-page":"1107","DOI":"10.1109\/TPAMI.2012.171","volume":"35","author":"OM Aodha","year":"2013","unstructured":"Aodha, O.M., Humayun, A., Pollefeys, M., Brostow, G.J.: Learning a confidence measure for optical flow. IEEE Trans. Pattern Anal. Mach. Intell. 35(5), 1107\u20131120 (2013). https:\/\/doi.org\/10.1109\/TPAMI.2012.171","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"40_CR2","unstructured":"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\u20131622 (2015)"},{"key":"40_CR3","doi-asserted-by":"publisher","first-page":"283","DOI":"10.1007\/1-4020-3858-8_15","volume-title":"Geometric Properties for Incomplete data","author":"A Bruhn","year":"2006","unstructured":"Bruhn, A., Weickert, J.: A confidence measure for variational optic flow methods. In: Klette, R., Kozera, R., Noakes, L., Weickert, J. (eds.) Geometric Properties for Incomplete data, pp. 283\u2013298. Springer, Dordrecht (2006). https:\/\/doi.org\/10.1007\/1-4020-3858-8_15"},{"key":"40_CR4","doi-asserted-by":"crossref","unstructured":"Chen, Q., Koltun, V.: Photographic image synthesis with cascaded refinement networks. In: IEEE International Conference on Computer Vision (ICCV) (2017)","DOI":"10.1109\/ICCV.2017.168"},{"key":"40_CR5","unstructured":"Chen, T., Fox, E., Guestrin, C.: Stochastic gradient Hamiltonian Monte Carlo. In: Proceedings of the 31st International Conference on Machine Learning (ICML 2014) (2014)"},{"key":"40_CR6","doi-asserted-by":"crossref","unstructured":"Dosovitskiy, A., et al.: FlowNet: learning optical flow with convolutional networks. In: IEEE International Conference on Computer Vision (ICCV) (2015)","DOI":"10.1109\/ICCV.2015.316"},{"key":"40_CR7","unstructured":"Gal, Y., Ghahramani, Z.: Dropout as a Bayesian approximation: representing model uncertainty in deep learning. In: International Conference on Machine Learning (ICML) (2016)"},{"key":"40_CR8","unstructured":"Graves, A.: Practical variational inference for neural networks. In: Advances in Neural Information Processing Systems (NIPS), pp. 2348\u20132356 (2011)"},{"key":"40_CR9","unstructured":"Guzman-Rivera, A., Batra, D., Kohli, P.: Multiple choice learning: learning to produce multiple structured outputs. In: International Conference on Neural Information Processing Systems (NIPS) (2012)"},{"key":"40_CR10","unstructured":"Hern\u00e1ndez-Lobato, J., Adams, R.: Probabilistic backpropagation for scalable learning of Bayesian neural networks. In: Proceedings of the 32nd International Conference on Machine Learning (ICML 2015) (2015)"},{"key":"40_CR11","unstructured":"Huang, G., Li, Y., Pleiss, G.: Snapshot ensembles: Train 1, get M for free. In: International Conference on Learning Representations (ICLR) (2017)"},{"key":"40_CR12","doi-asserted-by":"crossref","unstructured":"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)","DOI":"10.1109\/CVPR.2017.179"},{"key":"40_CR13","unstructured":"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\u2013456. PMLR, Lille, 07\u201309 July 2015. http:\/\/proceedings.mlr.press\/v37\/ioffe15.html"},{"key":"40_CR14","doi-asserted-by":"crossref","unstructured":"Jia, Y., et al.: Caffe: convolutional architecture for fast feature embedding. In: Proceedings of ACMMM, pp. 675\u2013678 (2014)","DOI":"10.1145\/2647868.2654889"},{"key":"40_CR15","unstructured":"Kendall, A., Gal, Y.: What uncertainties do we need in Bayesian deep learning for computer vision? In: International Conference on Neural Information Processing Systems (NIPS) (2017)"},{"key":"40_CR16","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"132","DOI":"10.1007\/978-3-540-74936-3_14","volume-title":"Pattern Recognition","author":"C Kondermann","year":"2007","unstructured":"Kondermann, C., Kondermann, D., J\u00e4hne, B., Garbe, C.: An adaptive confidence measure for optical flows based on linear subspace projections. In: Hamprecht, F.A., Schn\u00f6rr, C., J\u00e4hne, B. (eds.) DAGM 2007. LNCS, vol. 4713, pp. 132\u2013141. Springer, Heidelberg (2007). https:\/\/doi.org\/10.1007\/978-3-540-74936-3_14"},{"key":"40_CR17","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"290","DOI":"10.1007\/978-3-540-88690-7_22","volume-title":"Computer Vision \u2013 ECCV 2008","author":"C Kondermann","year":"2008","unstructured":"Kondermann, C., Mester, R., Garbe, C.: A statistical confidence measure for optical flows. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008. LNCS, vol. 5304, pp. 290\u2013301. Springer, Heidelberg (2008). https:\/\/doi.org\/10.1007\/978-3-540-88690-7_22"},{"issue":"10","key":"40_CR18","doi-asserted-by":"publisher","first-page":"1449","DOI":"10.1016\/j.cviu.2011.06.008","volume":"115","author":"J Kybic","year":"2011","unstructured":"Kybic, J., Nieuwenhuis, C.: Bootstrap optical flow confidence and uncertainty measure. Comput. Vis. Image Underst. 115(10), 1449\u20131462 (2011). https:\/\/doi.org\/10.1016\/j.cviu.2011.06.008. http:\/\/www.sciencedirect.com\/science\/article\/pii\/S1077314211001536","journal-title":"Comput. Vis. Image Underst."},{"key":"40_CR19","unstructured":"Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: NIPS Workshop (2016)"},{"key":"40_CR20","unstructured":"Lee, S., Purushwalkam, S., Cogswell, M., Ranjan, V., Crandall, D., Batra, D.: Stochastic multiple choice learning for training diverse deep ensembles. In: International Conference on Neural Information Processing Systems (NIPS) (2016)"},{"key":"40_CR21","unstructured":"Loshchilov, I., Hutter, F.: SGDR: stochastic gradient descent with warm restarts. In: International Conference on Learning Representations (ICLR) (2017)"},{"issue":"3","key":"40_CR22","doi-asserted-by":"publisher","first-page":"448","DOI":"10.1162\/neco.1992.4.3.448","volume":"4","author":"DJC MacKay","year":"1992","unstructured":"MacKay, D.J.C.: A practical Bayesian framework for backpropagation networks. Neural Comput. 4(3), 448\u2013472 (1992)","journal-title":"Neural Comput."},{"key":"40_CR23","doi-asserted-by":"publisher","unstructured":"Mayer, N., et al.: 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\u20134048, June 2016. https:\/\/doi.org\/10.1109\/CVPR.2016.438","DOI":"10.1109\/CVPR.2016.438"},{"key":"40_CR24","doi-asserted-by":"crossref","unstructured":"Neal, R.: Bayesian learning for neural networks. Ph.D. thesis, University of Toronto (1996)","DOI":"10.1007\/978-1-4612-0745-0"},{"key":"40_CR25","doi-asserted-by":"publisher","unstructured":"Nix, D.A., Weigend, A.S.: Estimating the mean and variance of the target probability distribution. In: Neural Networks: IEEE World Congress on Computational Intelligence, vol. 1, pp. 55\u201360, June 1994. https:\/\/doi.org\/10.1109\/ICNN.1994.374138","DOI":"10.1109\/ICNN.1994.374138"},{"key":"40_CR26","doi-asserted-by":"crossref","unstructured":"Novotny, D., Larlus, D., Vedaldi, A.: Learning 3D object categories by looking around them. In: IEEE International Conference on Computer Vision (ICCV) (2017)","DOI":"10.1109\/ICCV.2017.558"},{"key":"40_CR27","doi-asserted-by":"crossref","unstructured":"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 International Conference on Computer Vision (ICCV) Workshop (2017)","DOI":"10.1109\/ICCVW.2017.108"},{"key":"40_CR28","doi-asserted-by":"crossref","unstructured":"Ummenhofer, B., et al.: 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","DOI":"10.1109\/CVPR.2017.596"},{"key":"40_CR29","doi-asserted-by":"crossref","unstructured":"Wannenwetsch, A.S., Keuper, M., Roth, S.: ProbFlow: joint optical flow and uncertainty estimation. In: IEEE International Conference on Computer Vision (ICCV), October 2017","DOI":"10.1109\/ICCV.2017.133"},{"key":"40_CR30","unstructured":"Welling, M., Teh, Y.: Bayesian learning via stochastic gradient Langevin dynamics. In: Proceedings of the 28th International Conference on Machine Learning (ICML 2011) (2011)"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2018"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-01234-2_40","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,10,5]],"date-time":"2022-10-05T00:31:58Z","timestamp":1664929918000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-01234-2_40"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018]]},"ISBN":["9783030012335","9783030012342"],"references-count":30,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-01234-2_40","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018]]},"assertion":[{"value":"6 October 2018","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Munich","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Germany","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2018","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 September 2018","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 September 2018","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2018","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2018.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}