{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,11]],"date-time":"2026-05-11T14:34:15Z","timestamp":1778510055899,"version":"3.51.4"},"publisher-location":"Cham","reference-count":48,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030336752","type":"print"},{"value":"9783030336769","type":"electronic"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","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":[[2019]]},"DOI":"10.1007\/978-3-030-33676-9_3","type":"book-chapter","created":{"date-parts":[[2019,10,25]],"date-time":"2019-10-25T17:20:30Z","timestamp":1572024030000},"page":"33-47","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":47,"title":["Simultaneous Semantic Segmentation and Outlier Detection in Presence of Domain Shift"],"prefix":"10.1007","author":[{"given":"Petra","family":"Bevandi\u0107","sequence":"first","affiliation":[]},{"given":"Ivan","family":"Kre\u0161o","sequence":"additional","affiliation":[]},{"given":"Marin","family":"Or\u0161i\u0107","sequence":"additional","affiliation":[]},{"given":"Sini\u0161a","family":"\u0160egvi\u0107","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,10,25]]},"reference":[{"issue":"8","key":"3_CR1","doi-asserted-by":"publisher","first-page":"1798","DOI":"10.1109\/TPAMI.2013.50","volume":"35","author":"Y Bengio","year":"2013","unstructured":"Bengio, Y., Courville, A.C., Vincent, P.: Representation learning: a review and new perspectives. IEEE Trans. Pattern Anal. Mach. Intell. 35(8), 1798\u20131828 (2013)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"3_CR2","unstructured":"Bevandic, P., Kreso, I., Orsic, M., Segvic, S.: Discriminative out-of-distribution detection for semantic segmentation. CoRR abs\/1808.07703 (2018)"},{"key":"3_CR3","unstructured":"Blum, H., Sarlin, P., Nieto, J.I., Siegwart, R., Cadena, C.: The Fishyscapes benchmark: measuring blind spots in semantic segmentation. CoRR abs\/1904.03215"},{"key":"3_CR4","unstructured":"Brock, A., Donahue, J., Simonyan, K.: Large scale GAN training for high fidelity natural image synthesis. In: ICLR (2019)"},{"key":"3_CR5","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1007\/978-3-540-88682-2_5","volume-title":"Computer Vision \u2013 ECCV 2008","author":"GJ Brostow","year":"2008","unstructured":"Brostow, G.J., Shotton, J., Fauqueur, J., Cipolla, R.: Segmentation and recognition using structure from motion point clouds. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008. LNCS, vol. 5302, pp. 44\u201357. Springer, Heidelberg (2008). https:\/\/doi.org\/10.1007\/978-3-540-88682-2_5"},{"key":"3_CR6","doi-asserted-by":"crossref","unstructured":"Bul\u00f2, S.R., Porzi, L., Kontschieder, P.: In-place activated BatchNorm for memory-optimized training of DNNs. CoRR, abs\/1712.02616, December 5 2017","DOI":"10.1109\/CVPR.2018.00591"},{"issue":"1","key":"3_CR7","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1023\/A:1007379606734","volume":"28","author":"R Caruana","year":"1997","unstructured":"Caruana, R.: Multitask learning. Mach. Learn. 28(1), 41\u201375 (1997). https:\/\/doi.org\/10.1023\/A:1007379606734","journal-title":"Mach. Learn."},{"key":"3_CR8","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"833","DOI":"10.1007\/978-3-030-01234-2_49","volume-title":"Computer Vision \u2013 ECCV 2018","author":"L-C Chen","year":"2018","unstructured":"Chen, L.-C., Zhu, Y., Papandreou, G., Schroff, F., Adam, H.: Encoder-decoder with atrous separable convolution for semantic image segmentation. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11211, pp. 833\u2013851. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01234-2_49"},{"key":"3_CR9","unstructured":"Cordts, M., et al.: The cityscapes dataset. In: CVPRW (2015)"},{"key":"3_CR10","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L., Li, K., Li, F.: ImageNet: a large-scale hierarchical image database. In: CVPR, pp. 248\u2013255 (2009)","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"3_CR11","unstructured":"DeVries, T., Taylor, G.W.: Learning confidence for out-of-distribution detection in neural networks. CoRR abs\/1802.04865 (2018)"},{"key":"3_CR12","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":"3_CR13","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-009-0275-4","volume-title":"The pascal visual object classes (VOC) challenge","author":"M Everingham","year":"2010","unstructured":"Everingham, M., Gool, L., Williams, C.K., Winn, J., Zisserman, A.: The pascal visual object classes (VOC) challenge. Int. J. Comput, Vision (2010)"},{"key":"3_CR14","doi-asserted-by":"publisher","first-page":"1231","DOI":"10.1177\/0278364913491297","volume":"32","author":"A Geiger","year":"2013","unstructured":"Geiger, A., Lenz, P., Stiller, C., Urtasun, R.: Vision meets robotics: the KITTI dataset. Int. J. Robot. Res. (IJRR) 32, 1231\u20131237 (2013)","journal-title":"Int. J. Robot. Res. (IJRR)"},{"key":"3_CR15","unstructured":"Goodfellow, I.J., et al.: Generative adversarial nets. In: NIPS (2014)"},{"key":"3_CR16","unstructured":"Guo, C., Pleiss, G., Sun, Y., Weinberger, K.Q.: On calibration of modern neural networks. In: ICML, pp. 1321\u20131330 (2017)"},{"key":"3_CR17","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., Girshick, R.: Mask R-CNN. In: ICCV (2017)","DOI":"10.1109\/ICCV.2017.322"},{"key":"3_CR18","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"346","DOI":"10.1007\/978-3-319-10578-9_23","volume-title":"Computer Vision \u2013 ECCV 2014","author":"K He","year":"2014","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Spatial pyramid pooling in deep convolutional networks for visual recognition. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8691, pp. 346\u2013361. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-10578-9_23"},{"key":"3_CR19","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"3_CR20","unstructured":"Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: ICLR (2017)"},{"key":"3_CR21","unstructured":"Hendrycks, D., Mazeika, M., Dietterich, T.: Deep anomaly detection with outlier exposure. In: ICLR (2019)"},{"key":"3_CR22","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Weinberger, K.Q.: Densely connected convolutional networks. In: CVPR (2017)","DOI":"10.1109\/CVPR.2017.243"},{"key":"3_CR23","unstructured":"Kendall, A., Gal, Y.: What uncertainties do we need in Bayesian deep learning for computer vision? In: NIPS, pp. 5574\u20135584 (2017)"},{"key":"3_CR24","doi-asserted-by":"crossref","unstructured":"Kong, S., Fowlkes, C.: Pixel-wise attentional gating for parsimonious pixel labeling. arxiv 1805.01556 (2018)","DOI":"10.1109\/WACV.2019.00114"},{"key":"3_CR25","doi-asserted-by":"crossref","unstructured":"Kreso, I., Krapac, J., Segvic, S.: Ladder-style DenseNets for semantic segmentation of large natural images. In: ICCV CVRSUAD 2017, pp. 238\u2013245 (2017)","DOI":"10.1109\/ICCVW.2017.37"},{"key":"3_CR26","doi-asserted-by":"crossref","unstructured":"Kreso, I., Krapac, J., Segvic, S.: Efficient ladder-style DenseNets for semantic segmentation of large images. CoRR abs\/1905.05661 (2019)","DOI":"10.1109\/TITS.2020.2984894"},{"key":"3_CR27","unstructured":"Kreso, I., Orsic, M., Bevandic, P., Segvic, S.: Robust semantic segmentation with ladder-DenseNet models. CoRR abs\/1806.03465 (2018)"},{"key":"3_CR28","unstructured":"Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: NIPS, pp. 6402\u20136413 (2017)"},{"key":"3_CR29","unstructured":"Lee, K., Lee, H., Lee, K., Shin, J.: Training confidence-calibrated classifiers for detecting out-of-distribution samples. In: ICLR (2018)"},{"key":"3_CR30","unstructured":"Lee, K., Lee, K., Lee, H., Shin, J.: A simple unified framework for detecting out-of-distribution samples and adversarial attacks. In: NeurIPS (2018)"},{"key":"3_CR31","unstructured":"Liang, S., Li, Y., Srikant, R.: Enhancing the reliability of out-of-distribution image detection in neural networks. In: ICLR (2018)"},{"key":"3_CR32","doi-asserted-by":"crossref","unstructured":"Lin, T., Doll\u00e1r, P., Girshick, R.B., He, K., Hariharan, B., Belongie, S.J.: Feature pyramid networks for object detection. In: CVPR, pp. 936\u2013944 (2017)","DOI":"10.1109\/CVPR.2017.106"},{"key":"3_CR33","doi-asserted-by":"crossref","unstructured":"Meletis, P., Dubbelman, G.: Training of convolutional networks on multiple heterogeneous datasets for street scene semantic segmentation. In: IV (2018)","DOI":"10.1109\/IVS.2018.8500398"},{"key":"3_CR34","unstructured":"Nalisnick, E.T., Matsukawa, A., Teh, Y.W., G\u00f6r\u00fcr, D., Lakshminarayanan, B.: Do deep generative models know what they don\u2019t know? In: ICLR (2019)"},{"key":"3_CR35","doi-asserted-by":"crossref","unstructured":"Neuhold, G., Ollmann, T., Bul\u00f2, S.R., Kontschieder, P.: The mapillary vistas dataset for semantic understanding of street scenes. In: ICCV (2017)","DOI":"10.1109\/ICCV.2017.534"},{"key":"3_CR36","unstructured":"Ngiam, J., Khosla, A., Kim, M., Nam, J., Lee, H., Y. Ng, A.: Multimodal deep learning. In: ICML, pp. 689\u2013696 (2011)"},{"key":"3_CR37","doi-asserted-by":"crossref","unstructured":"Sabokrou, M., Khalooei, M., Fathy, M., Adeli, E.: Adversarially learned one-class classifier for novelty detection. In: CVPR, pp. 3379\u20133388 (2018)","DOI":"10.1109\/CVPR.2018.00356"},{"issue":"7","key":"3_CR38","doi-asserted-by":"publisher","first-page":"1757","DOI":"10.1109\/TPAMI.2012.256","volume":"35","author":"WJ Scheirer","year":"2013","unstructured":"Scheirer, W.J., de Rezende Rocha, A., Sapkota, A., Boult, T.E.: Toward open set recognition. IEEE Trans. Pattern Anal. Mach. Intell. 35(7), 1757\u20131772 (2013)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"3_CR39","unstructured":"Shafaei, A., Schmidt, M., Little, J.J.: Does your model know the digit 6 is not a cat? a less biased evaluation of \u201coutlier\u201d detectors. CoRR abs\/1809.04729 (2018)"},{"key":"3_CR40","unstructured":"Smith, L., Gal, Y.: Understanding measures of uncertainty for adversarial example detection. In: UAI, abs\/1803.08533 (2018)"},{"key":"3_CR41","doi-asserted-by":"publisher","unstructured":"Torralba, A., Efros, A.A.: Unbiased look at dataset bias. In: CVPR, June 2011. https:\/\/doi.org\/10.1109\/CVPR.2011.5995347","DOI":"10.1109\/CVPR.2011.5995347"},{"key":"3_CR42","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"560","DOI":"10.1007\/978-3-030-01237-3_34","volume-title":"Computer Vision \u2013 ECCV 2018","author":"A Vyas","year":"2018","unstructured":"Vyas, A., Jammalamadaka, N., Zhu, X., Das, D., Kaul, B., Willke, T.L.: Out-of-distribution detection using an ensemble of self supervised leave-out classifiers. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11212, pp. 560\u2013574. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01237-3_34"},{"key":"3_CR43","doi-asserted-by":"crossref","unstructured":"Yu, F., Koltun, V., Funkhouser, T.: Dilated residual networks. In: CVPR (2017)","DOI":"10.1109\/CVPR.2017.75"},{"key":"3_CR44","unstructured":"Yu, F., Zhang, Y., Song, S., Seff, A., Xiao, J.: LSUN: construction of a large-scale image dataset using deep learning with humans in the loop. CoRR abs\/1506.03365 (2015)"},{"key":"3_CR45","doi-asserted-by":"crossref","unstructured":"Zamir, A.R., Sax, A., Shen, W.B., Guibas, L.J., Malik, J., Savarese, S.: Taskonomy: disentangling task transfer learning. In: CVPR (2018)","DOI":"10.24963\/ijcai.2019\/871"},{"key":"3_CR46","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"407","DOI":"10.1007\/978-3-030-01231-1_25","volume-title":"Computer Vision \u2013 ECCV 2018","author":"O Zendel","year":"2018","unstructured":"Zendel, O., Honauer, K., Murschitz, M., Steininger, D., Dom\u00ednguez, G.F.: WildDash - creating hazard-aware benchmarks. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11210, pp. 407\u2013421. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01231-1_25"},{"issue":"1\u20133","key":"3_CR47","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1007\/s11263-017-1020-z","volume":"125","author":"O Zendel","year":"2017","unstructured":"Zendel, O., Murschitz, M., Humenberger, M., Herzner, W.: How good is my test data? introducing safety analysis for computer vision. Int. J. Comput. Vis. 125(1\u20133), 95\u2013109 (2017)","journal-title":"Int. J. Comput. Vis."},{"key":"3_CR48","doi-asserted-by":"crossref","unstructured":"Zhao, H., Shi, J., Qi, X., Wang, X., Jia, J.: Pyramid scene parsing network. In: CVPR (2017)","DOI":"10.1109\/CVPR.2017.660"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-33676-9_3","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,1,26]],"date-time":"2021-01-26T03:20:12Z","timestamp":1611631212000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-33676-9_3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030336752","9783030336769"],"references-count":48,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-33676-9_3","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"25 October 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"DAGM GCPR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"German Conference on Pattern Recognition","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Dortmund","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":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10 September 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 September 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"41","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"dagm2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/gcpr2019.tu-dortmund.de\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"91","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"43","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"47% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}