{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,7]],"date-time":"2026-10-07T21:15:11Z","timestamp":1791407711371,"version":"4.3.3"},"publisher-location":"Cham","reference-count":34,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783319464770","type":"print"},{"value":"9783319464787","type":"electronic"}],"license":[{"start":{"date-parts":[[2016,1,1]],"date-time":"2016-01-01T00:00:00Z","timestamp":1451606400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2016,1,1]],"date-time":"2016-01-01T00:00:00Z","timestamp":1451606400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2016]]},"DOI":"10.1007\/978-3-319-46478-7_25","type":"book-chapter","created":{"date-parts":[[2016,9,15]],"date-time":"2016-09-15T09:48:37Z","timestamp":1473932917000},"page":"402-418","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":104,"title":["Fast, Exact and Multi-scale Inference for Semantic Image Segmentation with Deep Gaussian CRFs"],"prefix":"10.1007","author":[{"given":"Siddhartha","family":"Chandra","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Iasonas","family":"Kokkinos","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2016,9,16]]},"reference":[{"key":"25_CR1","doi-asserted-by":"crossref","unstructured":"Zheng, S., Jayasumana, S., Romera-Paredes, B., Vineet, V., Su, Z., Du, D., Huang, C., Torr, P.: Conditional random fields as recurrent neural networks. In: ICCV (2015)","DOI":"10.1109\/ICCV.2015.179"},{"key":"25_CR2","doi-asserted-by":"crossref","unstructured":"Vemulapalli, R., Tuzel, O., Liu, M.Y., Chellapa, R.: Gaussian conditional random field network for semantic segmentation. In: CVPR, June 2016","DOI":"10.1109\/CVPR.2016.351"},{"key":"25_CR3","unstructured":"Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: Semantic image segmentation with deep convolutional nets and fully connected CRFs. arXiv preprint \n                      arXiv:1412.7062\n                      \n                     (2014)"},{"key":"25_CR4","doi-asserted-by":"publisher","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. PAMI 35, 1915\u20131929 (2013)","journal-title":"PAMI"},{"key":"25_CR5","doi-asserted-by":"crossref","unstructured":"Mostajabi, M., Yadollahpour, P., Shakhnarovich, G.: Feedforward semantic segmentation with zoom-out features. In: CVPR (2015)","DOI":"10.1109\/CVPR.2015.7298959"},{"key":"25_CR6","doi-asserted-by":"crossref","unstructured":"Hariharan, B., Arbel\u00e1ez, P., Girshick, R., Malik, J.: Hypercolumns for object segmentation and fine-grained localization. In: CVPR (2015)","DOI":"10.1109\/CVPR.2015.7298642"},{"key":"25_CR7","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: CVPR, pp. 3431\u20133440 (2015)","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"25_CR8","unstructured":"Farabet, C., Couprie, C., Najman, L., Lecun, Y.: Scene parsing with multiscale feature learning, purity trees, and optimal covers. In: ICML (2012)"},{"key":"25_CR9","unstructured":"Chen, L.C., Schwing, A.G., Yuille, A.L., Urtasun, R.: Learning deep structured models. In: ICML (2015)"},{"key":"25_CR10","doi-asserted-by":"crossref","unstructured":"Vemulapalli, R., Tuzel, O., Liu, M.: Deep Gaussian conditional random field network: a model-based deep network for discriminative denoising. In: CVPR (2016)","DOI":"10.1109\/CVPR.2016.519"},{"key":"25_CR11","doi-asserted-by":"crossref","unstructured":"Ionescu, C., Vantzos, O., Sminchisescu, C.: Matrix backpropagation for deep networks with structured layers. In: ICCV (2015)","DOI":"10.1109\/ICCV.2015.339"},{"key":"25_CR12","unstructured":"Kr\u00e4henb\u00fchl, P., Koltun, V.: Efficient inference in fully connected CRFs with Gaussian edge potentials. In: NIPS (2011)"},{"key":"25_CR13","unstructured":"Couprie, C.: Multi-label energy minimization for object class segmentation. In: 2012 Proceedings of the 20th European on Signal Processing Conference (EUSIPCO), pp. 2233\u20132237. IEEE (2012)"},{"key":"25_CR14","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. In: CVPR (2016)","DOI":"10.1109\/CVPR.2016.348"},{"key":"25_CR15","doi-asserted-by":"crossref","unstructured":"Liu, Z., Li, X., Luo, P., Loy, C.C., Tang, X.: Semantic image segmentation via deep parsing network. In: CVPR, pp. 1377\u20131385 (2015)","DOI":"10.1109\/ICCV.2015.162"},{"key":"25_CR16","doi-asserted-by":"crossref","unstructured":"Tappen, M.F., Liu, C., Adelson, E.H., Freeman, W.T.: Learning Gaussian conditional random fields for low-level vision. In: CVPR (2007)","DOI":"10.1109\/CVPR.2007.382979"},{"key":"25_CR17","doi-asserted-by":"crossref","unstructured":"Jancsary, J., Nowozin, S., Sharp, T., Rother, C.: Regression tree fields - an efficient, non-parametric approach to image labeling problems. In: CVPR (2012)","DOI":"10.1109\/CVPR.2012.6247950"},{"key":"25_CR18","doi-asserted-by":"crossref","unstructured":"Vu, T.H., Osokin, A., Laptev, I.: Context-aware CNNs for person head detection. In: ICCV, pp. 2893\u20132901 (2015)","DOI":"10.1109\/ICCV.2015.331"},{"key":"25_CR19","unstructured":"Shewchuk, J.R.: An introduction to the conjugate gradient method without the agonizing pain. \n                      https:\/\/www.cs.cmu.edu\/~quake-papers\/painless-conjugate-gradient.pdf"},{"key":"25_CR20","volume-title":"Numerical Recipes in C","author":"WH Press","year":"1992","unstructured":"Press, W.H., Teukolsky, S.A., Vetterling, W.T., Flannery, B.P.: Numerical Recipes in C, 2nd edn. Cambridge University Press, New York (1992)","edition":"2"},{"key":"25_CR21","volume-title":"Matrix Computations","author":"GH Golub","year":"1996","unstructured":"Golub, G.H., Loan, C.F.V.: Matrix Computations, 3rd edn. Johns Hopkins University Press, Baltimore (1996)","edition":"3"},{"key":"25_CR22","doi-asserted-by":"publisher","first-page":"1768","DOI":"10.1109\/TPAMI.2006.233","volume":"28","author":"L Grady","year":"2006","unstructured":"Grady, L.: Random walks for image segmentation. PAMI 28, 1768\u20131783 (2006)","journal-title":"PAMI"},{"key":"25_CR23","unstructured":"Golub, G.H., Loan, V., F., C: Matrix computations. 3(1\u20132), 510 (1996)"},{"key":"25_CR24","series-title":"Monographs on Statistics and Applied Probability","doi-asserted-by":"crossref","DOI":"10.1201\/9780203492024","volume-title":"Gaussian Markov Random Fields: Theory and Applications","author":"H Rue","year":"2005","unstructured":"Rue, H., Held, L.: Gaussian Markov Random Fields: Theory and Applications. Monographs on Statistics and Applied Probability, vol. 104. Chapman & Hall, London (2005)"},{"issue":"1\u20132","key":"25_CR25","first-page":"136","volume":"1","author":"MJ Wainwright","year":"2008","unstructured":"Wainwright, M.J., Jordan, M.I.: Graphical models, exponential families, and variational inference. Found. Trends Mach. Learn. 1(1\u20132), 136\u2013138 (2008)","journal-title":"Found. Trends Mach. Learn."},{"key":"25_CR26","doi-asserted-by":"crossref","unstructured":"Chen, L., Yang, Y., Wang, J., Xu, W., Yuille, A.L.: Attention to scale: scale-aware semantic image segmentation. In: CVPR (2016)","DOI":"10.1109\/CVPR.2016.396"},{"key":"25_CR27","unstructured":"Kokkinos, I.: Pushing the boundaries of boundary detection using deep learning. In: ICLR (2016)"},{"key":"25_CR28","doi-asserted-by":"crossref","first-page":"740","DOI":"10.1007\/978-3-319-10602-1_48","volume-title":"Computer Vision \u2013 ECCV 2014","author":"Tsung-Yi Lin","year":"2014","unstructured":"Lin, T.-Y., et al.: Microsoft COCO: common objects in context. In: ECCV (2014)"},{"key":"25_CR29","doi-asserted-by":"crossref","unstructured":"Chen, L.C., Papandreou, G., Murphy, K., Yuille, A.L.: Weakly- and semi-supervised learning of a deep convolutional network for semantic image segmentation. In: ICCV (2015)","DOI":"10.1109\/ICCV.2015.203"},{"key":"25_CR30","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: ICLR (2015)"},{"key":"25_CR31","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"25_CR32","unstructured":"Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: Deeplab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. \n                      arXiv:1606.00915\n                      \n                     (2016)"},{"key":"25_CR33","doi-asserted-by":"crossref","unstructured":"Eigen, D., Fergus, R.: Predicting depth, surface normals and semantic labels with a common multi-scale convolutional architecture. In: ICCV, pp. 2650\u20132658 (2015)","DOI":"10.1109\/ICCV.2015.304"},{"key":"25_CR34","unstructured":"Kokkinos, I.: Ubernet: a universal cnn for the joint treatment of low-, mid-, and high- level vision problems. In: POCV Workshop (2016)"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2016"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-319-46478-7_25","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,10,10]],"date-time":"2020-10-10T01:13:11Z","timestamp":1602292391000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-319-46478-7_25"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016]]},"ISBN":["9783319464770","9783319464787"],"references-count":34,"URL":"https:\/\/doi.org\/10.1007\/978-3-319-46478-7_25","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2016]]},"assertion":[{"value":"16 September 2016","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":"Amsterdam","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"The Netherlands","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2016","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 October 2016","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16 October 2016","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2016","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.eccv2016.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"}]}}