{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,9]],"date-time":"2024-09-09T11:37:15Z","timestamp":1725881835756},"publisher-location":"Cham","reference-count":39,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783319541839"},{"type":"electronic","value":"9783319541846"}],"license":[{"start":{"date-parts":[[2017,1,1]],"date-time":"2017-01-01T00:00:00Z","timestamp":1483228800000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2017]]},"DOI":"10.1007\/978-3-319-54184-6_14","type":"book-chapter","created":{"date-parts":[[2017,3,9]],"date-time":"2017-03-09T15:44:25Z","timestamp":1489074265000},"page":"221-236","source":"Crossref","is-referenced-by-count":5,"title":["Joint Training of Generic CNN-CRF Models with Stochastic Optimization"],"prefix":"10.1007","author":[{"given":"A.","family":"Kirillov","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"D.","family":"Schlesinger","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"S.","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"B.","family":"Savchynskyy","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"P. H. S.","family":"Torr","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"C.","family":"Rother","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2017,3,10]]},"reference":[{"key":"14_CR1","doi-asserted-by":"crossref","unstructured":"Lin, G., Shen, C., Reid, I.D., van den Hengel, A.: Efficient piecewise training of deep structured models for semantic segmentation. preprint arXiv:1504.01013 (2015)","DOI":"10.1109\/CVPR.2016.348"},{"key":"14_CR2","unstructured":"Lafferty, J., McCallum, A., Pereira, F.C.: Conditional random fields: probabilistic models for segmenting and labeling sequence data. In: ICML, pp. 282\u2013289 (2001)"},{"key":"14_CR3","unstructured":"Chen, L., Schwing, A.G., Yuille, A.L., Urtasun, R.: Learning deep structured models. In: ICML, pp. 1785\u20131794 (2015)"},{"key":"14_CR4","doi-asserted-by":"crossref","unstructured":"Nowozin, S., Rother, C., Bagon, S., Sharp, T., Yao, B., Kohli, P.: Decision tree fields. In: ICCV (2011)","DOI":"10.1109\/ICCV.2011.6126429"},{"key":"14_CR5","doi-asserted-by":"crossref","first-page":"1605","DOI":"10.1109\/5.58346","volume":"78","author":"IK Sethi","year":"1990","unstructured":"Sethi, I.K.: Entropy nets: from decision trees to neural networks. Proc. IEEE 78, 1605\u20131613 (1990)","journal-title":"Proc. IEEE"},{"key":"14_CR6","unstructured":"Richmond, D.L., Kainmueller, D., Yang, M.Y., Myers, E.W., Rother, C.: Relating cascaded random forests to deep convolutional neural networks for semantic segmentation. preprint arXiv:1507.07583 (2015)"},{"key":"14_CR7","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. preprint arXiv:1411.4038 (2014)","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"14_CR8","doi-asserted-by":"crossref","first-page":"1915","DOI":"10.1109\/TPAMI.2012.231","volume":"35","author":"C Farabet","year":"2013","unstructured":"Farabet, C., Couprie, C., Najman, L., LeCun, Y.: Learning hierarchical features for scene labeling. TPAMI 35, 1915\u20131929 (2013)","journal-title":"TPAMI"},{"key":"14_CR9","unstructured":"Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: Semantic image segmentation with deep convolutional nets and fully connected CRFs. preprint arXiv:1412.7062 (2014)"},{"key":"14_CR10","unstructured":"Kr\u00e4henb\u00fchl, P., Koltun, V.: Efficient inference in fully connected CRFs with gaussian edge potentials. In: NIPS (2011)"},{"key":"14_CR11","doi-asserted-by":"crossref","unstructured":"Zheng, S., Jayasumana, S., Romera-Paredes, B., Vineet, V., Su, Z., Du, D., Huang, C., Torr, P.H.S.: Conditional random fields as recurrent neural networks. In: Proceedings of ICCV (2015)","DOI":"10.1109\/ICCV.2015.179"},{"key":"14_CR12","unstructured":"Schwing, A.G., Urtasun, R.: Fully connected deep structured networks. preprint arXiv:1503.02351 (2015)"},{"key":"14_CR13","doi-asserted-by":"crossref","unstructured":"Adams, A., Baek, J., Davis, M.A.: Fast high-dimensional filtering using the permutohedral lattice. In: Computer Graphics Forum, vol. 29. Wiley Online Library (2010)","DOI":"10.1111\/j.1467-8659.2009.01645.x"},{"key":"14_CR14","doi-asserted-by":"crossref","unstructured":"Domke, J.: Learning graphical model parameters with approximate marginal inference. TPAMI 35, 2454\u20132467 (2013)","DOI":"10.1109\/TPAMI.2013.31"},{"key":"14_CR15","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"331","DOI":"10.1007\/978-3-319-10602-1_22","volume-title":"Computer Vision \u2013 ECCV 2014","author":"M Kiefel","year":"2014","unstructured":"Kiefel, M., Gehler, P.V.: Human pose estimation with fields of parts. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 331\u2013346. Springer, Heidelberg (2014). doi: 10.1007\/978-3-319-10602-1_22"},{"key":"14_CR16","doi-asserted-by":"crossref","first-page":"2451","DOI":"10.1109\/TIP.2009.2028254","volume":"18","author":"A Barbu","year":"2009","unstructured":"Barbu, A.: Training an active random field for real-time image denoising. IEEE Trans. Image Process. 18, 2451\u20132462 (2009)","journal-title":"IEEE Trans. Image Process."},{"key":"14_CR17","doi-asserted-by":"crossref","unstructured":"Ross, S., Munoz, D., Hebert, M., Bagnell, J.A.: Learning message-passing inference machines for structured prediction. In: Proceedings of CVPR (2011)","DOI":"10.1109\/CVPR.2011.5995724"},{"key":"14_CR18","unstructured":"Stoyanov, V., Ropson, A., Eisner, J.: Empirical risk minimization of graphical model parameters given approximate inference, decoding, and model structure. In: Proceedings of AISTATS (2011)"},{"key":"14_CR19","unstructured":"Tompson, J.J., Jain, A., LeCun, Y., Bregler, C.: Joint training of a convolutional network and a graphical model for human pose estimation. In: Proceedings of NIPS (2014)"},{"key":"14_CR20","doi-asserted-by":"crossref","unstructured":"Liu, Z., Li, X., Luo, P., Loy, C.C., Tang, X.: Semantic image segmentation via deep parsing network. In: Proceedings of ICCV (2015)","DOI":"10.1109\/ICCV.2015.162"},{"key":"14_CR21","unstructured":"Sutton, C., McCallum, A.: Piecewise training of undirected models. In: Conference on Uncertainty in Artificial Intelligence (UAI) (2005)"},{"key":"14_CR22","unstructured":"He, X., Zemel, R.S., Carreira-perpi\u00f1\u00e1n, M.\u00c1.: Multiscale conditional random fields for image labeling. In: CVPR. Citeseer (2004)"},{"key":"14_CR23","doi-asserted-by":"crossref","unstructured":"Wainwright, M.J., Jordan, M.I.: Graphical models, exponential families, and variational inference. Found. Trends $${\\textregistered }$$ Mach. Learn. 1, 1\u2013305 (2008)","DOI":"10.1561\/2200000001"},{"key":"14_CR24","doi-asserted-by":"crossref","first-page":"721","DOI":"10.1109\/TPAMI.1984.4767596","volume":"6","author":"S Geman","year":"1984","unstructured":"Geman, S., Geman, D.: Stochastic relaxation, gibbs distributions, and the Bayesian restoration of images. TPAMI 6, 721\u2013741 (1984)","journal-title":"TPAMI"},{"key":"14_CR25","doi-asserted-by":"crossref","unstructured":"Robbins, H., Monro, S.: A stochastic approximation method. Ann. Math. Stat. 400\u2013407 (1951)","DOI":"10.1214\/aoms\/1177729586"},{"key":"14_CR26","volume-title":"Introduction to Stochastic Search and Optimization: Estimation, Simulation, and Control","author":"JC Spall","year":"2005","unstructured":"Spall, J.C.: Introduction to Stochastic Search and Optimization: Estimation, Simulation, and Control, vol. 65. Wiley, Hoboken (2005)"},{"key":"14_CR27","doi-asserted-by":"crossref","unstructured":"Geyer, C.J.: Practical Markov chain Monte Carlo. Stat. Sci. 473\u2013483 (1992)","DOI":"10.1214\/ss\/1177011137"},{"key":"14_CR28","doi-asserted-by":"crossref","DOI":"10.1093\/oso\/9780198522195.001.0001","volume-title":"Graphical Models","author":"SL Lauritzen","year":"1996","unstructured":"Lauritzen, S.L.: Graphical Models. Oxford University Press, Oxford (1996)"},{"key":"14_CR29","unstructured":"Gonzalez, J., Low, Y., Gretton, A., Guestrin, C.: Parallel Gibbs sampling: from colored fields to thin junction trees. In: International Conference on Artificial Intelligence and Statistics. pp. 324\u2013332 (2011)"},{"key":"14_CR30","doi-asserted-by":"crossref","first-page":"1771","DOI":"10.1162\/089976602760128018","volume":"14","author":"G Hinton","year":"2002","unstructured":"Hinton, G.: Training products of experts by minimizing contrastive divergence. Neural Comput. 14, 1771\u20131800 (2002)","journal-title":"Neural Comput."},{"key":"14_CR31","unstructured":"Yuille, A.L.: The convergence of contrastive divergences. In: NIPS (2004)"},{"key":"14_CR32","doi-asserted-by":"crossref","unstructured":"Tieleman, T.: Training restricted Boltzmann machines using approximations to the likelihood gradient. In: ICML. ACM, New York (2008)","DOI":"10.1145\/1390156.1390290"},{"key":"14_CR33","unstructured":"Everingham, M., Van Gool, L., Williams, C.K.I., Winn, J., Zisserman, A.: The PASCAL Visual Object Classes Challenge 2012 (VOC 2012) Results"},{"key":"14_CR34","doi-asserted-by":"crossref","unstructured":"Hariharan, B., Arbelaez, P., Bourdev, L., Maji, S., Malik, J.: Semantic contours from inverse detectors. In: International Conference on Computer Vision (ICCV) (2011)","DOI":"10.1109\/ICCV.2011.6126343"},{"key":"14_CR35","unstructured":"Denil, M., Matheson, D., de Freitas, N.: Consistency of online random forests. In: ICML (2013)"},{"key":"14_CR36","first-page":"1097","volume-title":"Advances in Neural Information Processing Systems","author":"A Krizhevsky","year":"2012","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Pereira, F., Burges, C.J.C., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25, pp. 1097\u20131105. Curran Associates Inc., New York (2012)"},{"key":"14_CR37","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. CoRR abs\/1409.1556 (2014)"},{"key":"14_CR38","unstructured":"Ren, S., Cao, X., Wei, Y., Sun, J.: Global refinement of random forest. In: CVPR (2015)"},{"key":"14_CR39","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1111\/cgf.12758","volume":"34","author":"MM Cheng","year":"2015","unstructured":"Cheng, M.M., Prisacariu, V.A., Zheng, S., Torr, P.H.S., Rother, C.: Densecut: densely connected CRFs for realtime Grabcut. Comput. Graph. Forum 34, 193\u2013201 (2015)","journal-title":"Comput. Graph. Forum"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ACCV 2016"],"original-title":[],"link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-319-54184-6_14","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,22]],"date-time":"2024-06-22T22:21:59Z","timestamp":1719094919000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-319-54184-6_14"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017]]},"ISBN":["9783319541839","9783319541846"],"references-count":39,"URL":"https:\/\/doi.org\/10.1007\/978-3-319-54184-6_14","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2017]]}}}