{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T19:17:53Z","timestamp":1780341473185,"version":"3.54.1"},"reference-count":51,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"DOI":"10.13039\/501100001659","name":"Deutsche Forschungsgemeinschaft (DFG) through German Research Foundation","doi-asserted-by":"publisher","award":["438106095"],"award-info":[{"award-number":["438106095"]}],"id":[{"id":"10.13039\/501100001659","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001659","name":"Germany\u2019s Excellence Strategy\u2013EXC-2064\/1","doi-asserted-by":"publisher","award":["390727645"],"award-info":[{"award-number":["390727645"]}],"id":[{"id":"10.13039\/501100001659","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2022]]},"DOI":"10.1109\/access.2022.3195211","type":"journal-article","created":{"date-parts":[[2022,8,5]],"date-time":"2022-08-05T00:26:09Z","timestamp":1659659169000},"page":"81192-81202","source":"Crossref","is-referenced-by-count":9,"title":["Real Time Landmark Detection for Within- and Cross Subject Tracking With Minimal Human Supervision"],"prefix":"10.1109","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6096-6101","authenticated-orcid":false,"given":"Marcel","family":"Frueh","sequence":"first","affiliation":[{"name":"Department of Radiology, Medical Image and Data Analysis (MIDAS.lab), University Hospital T&#x00FC;bingen, T&#x00FC;bingen, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andreas","family":"Schilling","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Institute for Visual Computing, University of T&#x00FC;bingen, T&#x00FC;bingen, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6928-4967","authenticated-orcid":false,"given":"Sergios","family":"Gatidis","sequence":"additional","affiliation":[{"name":"Department of Radiology, Medical Image and Data Analysis (MIDAS.lab), University Hospital T&#x00FC;bingen, T&#x00FC;bingen, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0353-4898","authenticated-orcid":false,"given":"Thomas","family":"Kuestner","sequence":"additional","affiliation":[{"name":"Department of Radiology, Medical Image and Data Analysis (MIDAS.lab), University Hospital T&#x00FC;bingen, T&#x00FC;bingen, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1016\/j.zemedi.2018.11.002"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1002\/mp.13264"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2015.2469286"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.417"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2013.75"},{"key":"ref6","article-title":"Self-supervised learning from 100 million medical images","author":"Ghesu","year":"2022","journal-title":"arXiv:2201.01283"},{"key":"ref7","first-page":"1","article-title":"VICREG: Variance-invariance-covariance regularization for self-supervised learning","volume-title":"Proc. ICLR","author":"Bardes"},{"key":"ref8","first-page":"1597","article-title":"A simple framework for contrastive learning of visual representations","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Chen"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/tmi.2022.3169003"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00267"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2021.3070138"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2020.107260"},{"key":"ref13","article-title":"Facial landmark detection using a Gabor filter representation and a genetic search algorithm","volume-title":"Proc. ASCI Conf.","author":"Lim"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2014.241"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.5220\/0003863705470556"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-10599-4_7"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46726-9_27"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-32251-9_29"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00088"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1134\/S1054661820030165"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00529"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-48881-3_56"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00508"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00803"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00864"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW50498.2020.00387"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00285"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1145\/3394171.3413547"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01995"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00167"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2018.01.080"},{"key":"ref32","first-page":"19545","article-title":"Space-time correspondence as a contrastive random walk","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"33","author":"Jabri"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2022.107085"},{"key":"ref34","first-page":"13695","article-title":"Self-supervised multi-object tracking with cross-input consistency","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"34","author":"Bastani"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01294"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00676"},{"key":"ref37","article-title":"PatchNet\u2013short-range template matching for efficient video processing","author":"Mao","year":"2021","journal-title":"arXiv:2103.07371"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1016\/j.isprsjprs.2021.09.012"},{"key":"ref39","article-title":"Very deep convolutional networks for large-scale image recognition","author":"Simonyan","year":"2014","journal-title":"arXiv:1409.1556"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-015-0816-y"},{"key":"ref41","volume-title":"Imgaug","author":"Jung","year":"2020"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/ICPR.2010.675"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.2967\/jnumed.112.105296"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.3301590"},{"key":"ref45","first-page":"1","article-title":"Adam: A method for stochastic optimization","volume-title":"Proc. 3rd Int. Conf. Learn. Represent.","author":"Kingma"},{"key":"ref46","first-page":"1","article-title":"Pytorch: An imperative style, high-performance deep learning library","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"32","author":"Paszke"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01167"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-00937-3_7"},{"key":"ref51","first-page":"12546","article-title":"Contrastive learning of global and local features for medical image segmentation with limited annotations","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"33","author":"Chaitanya"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/9668973\/09845397.pdf?arnumber=9845397","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,2]],"date-time":"2024-03-02T03:56:47Z","timestamp":1709351807000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9845397\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"references-count":51,"URL":"https:\/\/doi.org\/10.1109\/access.2022.3195211","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]}}}