{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T17:41:27Z","timestamp":1781199687231,"version":"3.54.1"},"reference-count":48,"publisher":"IEEE","license":[{"start":{"date-parts":[[2020,5,1]],"date-time":"2020-05-01T00:00:00Z","timestamp":1588291200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2020,5,1]],"date-time":"2020-05-01T00:00:00Z","timestamp":1588291200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2020,5,1]],"date-time":"2020-05-01T00:00:00Z","timestamp":1588291200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,5]]},"DOI":"10.1109\/icra40945.2020.9196723","type":"proceedings-article","created":{"date-parts":[[2020,9,15]],"date-time":"2020-09-15T21:25:46Z","timestamp":1600205146000},"page":"10788-10794","source":"Crossref","is-referenced-by-count":16,"title":["Multi-Task Learning for Single Image Depth Estimation and Segmentation Based on Unsupervised Network"],"prefix":"10.1109","author":[{"given":"Yawen","family":"Lu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michel","family":"Sarkis","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guoyu","family":"Lu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00216"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2010.5539939"},{"key":"ref33","first-page":"298","article-title":"Geometry meets semantics for semi-supervised monocular depth estimation","author":"ramirez","year":"2018","journal-title":"ACCV"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2004.102"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/3DV.2018.00045"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/IROS.2018.8593814"},{"key":"ref37","first-page":"107","article-title":"Normalized cuts and image segmentation","author":"shi","year":"2000","journal-title":"Departmental Papers (CIS)"},{"key":"ref36","first-page":"234","article-title":"U-net: Convolutional networks for biomedical image segmentation","author":"ronneberger","year":"2015","journal-title":"MICCAI"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.01252"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.440"},{"key":"ref10","first-page":"740","article-title":"Unsupervised cnn for single view depth estimation: Geometry to the rescue","author":"garg","year":"2016","journal-title":"ECCV"},{"key":"ref40","article-title":"W-net: A deep model for fully unsupervised image segmentation","author":"xia","year":"2017","journal-title":"arXiv preprint arXiv 1711 07064"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2012.6248074"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/IVS.2011.5940405"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.699"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA.2014.6907054"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1145\/3272127.3275083"},{"key":"ref17","first-page":"2017","article-title":"Spatial transformer networks","author":"jaderberg","year":"2015","journal-title":"NIPS"},{"key":"ref18","article-title":"Adam: A method for stochastic optimization","author":"kingma","year":"2014","journal-title":"arXiv preprint arXiv 1412 6980"},{"key":"ref19","first-page":"109","article-title":"Efficient inference in fully connected crfs with gaussian edge potentials","author":"kr\u00e4henb\u00fchl","year":"2011","journal-title":"NIPS"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00594"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2014.500"},{"key":"ref27","first-page":"281","article-title":"Some methods for classification and analysis of multivariate observations","volume":"1","author":"macqueen","year":"1967","journal-title":"Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability"},{"key":"ref3","first-page":"801","article-title":"Encoder-decoder with atrous separable convolution for semantic image segmentation","author":"chen","year":"2018","journal-title":"ECCV"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.350"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/3DV.2016.69"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/34.1000236"},{"key":"ref8","article-title":"Depth map prediction from a single image using a multi-scale deep network","author":"eigen","year":"2014","journal-title":"NIPS"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2013.231"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2016.2644615"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-014-0733-5"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2012.120"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.700"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-33718-5_26"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/42.906424"},{"key":"ref48","article-title":"The berhu penalty and the grouped effect","author":"zwald","year":"2012","journal-title":"arXiv preprint arXiv 1207 6868"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/3DV.2016.32"},{"key":"ref47","first-page":"36","article-title":"Df-net: Unsupervised joint learning of depth and flow using cross-task consistency","author":"zou","year":"2018","journal-title":"Proceedings of the European Conference on Computer Vision (ECCV)"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.238"},{"key":"ref42","first-page":"0","article-title":"Every pixel counts: Unsupervised geometry learning with holistic 3d motion understanding","author":"yang","year":"2018","journal-title":"ECCV"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7299152"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.25"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.3390\/s19081795"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00212"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00031"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2015.2505283"}],"event":{"name":"2020 IEEE International Conference on Robotics and Automation (ICRA)","location":"Paris, France","start":{"date-parts":[[2020,5,31]]},"end":{"date-parts":[[2020,8,31]]}},"container-title":["2020 IEEE International Conference on Robotics and Automation (ICRA)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9187508\/9196508\/09196723.pdf?arnumber=9196723","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,6,28]],"date-time":"2022-06-28T00:16:31Z","timestamp":1656375391000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9196723\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,5]]},"references-count":48,"URL":"https:\/\/doi.org\/10.1109\/icra40945.2020.9196723","relation":{},"subject":[],"published":{"date-parts":[[2020,5]]}}}