Continual BatchNorm Adaptation (CBNA) for Semantic Segmentation
Environment perception in autonomous driving vehicles often heavily relies on deep neural networks (DNNs), which are subject to domain shifts, leading to a significantly decreased performance during DNN deployment. Usually, this problem is addressed by unsupervised domain adaptation (UDA) approaches...
Uloženo v:
| Vydáno v: | IEEE transactions on intelligent transportation systems Ročník 23; číslo 11; s. 20899 - 20911 |
|---|---|
| Hlavní autoři: | , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
New York
IEEE
01.11.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Témata: | |
| ISSN: | 1524-9050, 1558-0016 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | Environment perception in autonomous driving vehicles often heavily relies on deep neural networks (DNNs), which are subject to domain shifts, leading to a significantly decreased performance during DNN deployment. Usually, this problem is addressed by unsupervised domain adaptation (UDA) approaches trained either simultaneously on source and target domain datasets or even source-free only on target data in an offline fashion. In this work, we further expand a source-free UDA approach to a continual and therefore online-capable UDA on a single-image basis for semantic segmentation. Accordingly, our method only requires the pre-trained model from the supplier (trained in the source domain) and the current (unlabeled target domain) camera image. Our method Continual BatchNorm Adaptation (CBNA) modifies the source domain statistics in the batch normalization layers, using target domain images in an unsupervised fashion, which yields consistent performance improvements during inference. Thereby, in contrast to existing works, our approach can be applied to improve a DNN continuously on a single-image basis during deployment without access to source data, without algorithmic delay, and nearly without computational overhead. We show the consistent effectiveness of our method across a wide variety of source/target domain settings for semantic segmentation. Code is available at https://github.com/ifnspaml/CBNA |
|---|---|
| AbstractList | Environment perception in autonomous driving vehicles often heavily relies on deep neural networks (DNNs), which are subject to domain shifts, leading to a significantly decreased performance during DNN deployment. Usually, this problem is addressed by unsupervised domain adaptation (UDA) approaches trained either simultaneously on source and target domain datasets or even source-free only on target data in an offline fashion. In this work, we further expand a source-free UDA approach to a continual and therefore online-capable UDA on a single-image basis for semantic segmentation. Accordingly, our method only requires the pre-trained model from the supplier (trained in the source domain) and the current (unlabeled target domain) camera image. Our method Continual BatchNorm Adaptation (CBNA) modifies the source domain statistics in the batch normalization layers, using target domain images in an unsupervised fashion, which yields consistent performance improvements during inference. Thereby, in contrast to existing works, our approach can be applied to improve a DNN continuously on a single-image basis during deployment without access to source data, without algorithmic delay, and nearly without computational overhead. We show the consistent effectiveness of our method across a wide variety of source/target domain settings for semantic segmentation. Code is available at https://github.com/ifnspaml/CBNA |
| Author | Klingner, Marvin Fingscheidt, Tim Ayache, Mouadh |
| Author_xml | – sequence: 1 givenname: Marvin orcidid: 0000-0001-7675-750X surname: Klingner fullname: Klingner, Marvin email: m.klingner@tu-bs.de organization: Institute of Communications Technology, Technische Universität Braunschweig, Braunschweig, Germany – sequence: 2 givenname: Mouadh surname: Ayache fullname: Ayache, Mouadh email: m.ayache@tu-bs.de organization: Institute of Communications Technology, Technische Universität Braunschweig, Braunschweig, Germany – sequence: 3 givenname: Tim orcidid: 0000-0002-8895-5041 surname: Fingscheidt fullname: Fingscheidt, Tim email: t.fingscheidt@tu-bs.de organization: Institute of Communications Technology, Technische Universität Braunschweig, Braunschweig, Germany |
| BookMark | eNp9kD9PwzAQxS1UJNrCB0AskVhgSLF9thNPqI34U6kqQ8tsOYkDqRq7OOnAt8dRKgYGpnt3-r2705ugkXXWIHRN8IwQLB-2y-1mRjGlMyASUwFnaEw4T2OMiRj1mrJYYo4v0KRtd2HKOCFj9Jg529X2qPfRQnfF59r5JpqX-tDprnY2ussW6_l9VDkfbUyjA1sE8dEYOwCX6LzS-9ZcneoUvT8_bbPXePX2sszmq7gAEF2cSwIUGKalEWWeJ4SXAGkFRpuca6FLWUpqTE5laAoqU8Z4LmiSFkJwTQGm6HbYe_Du62jaTu3c0dtwUtEEGKQJYBaoZKAK79rWm0oV9fBn53W9VwSrPi3Vp6X6tNQpreAkf5wHXzfaf__ruRk8tTHmlw-_Q8pT-AHIInX6 |
| CODEN | ITISFG |
| CitedBy_id | crossref_primary_10_1038_s41598_025_05648_z crossref_primary_10_1016_j_microc_2025_113384 crossref_primary_10_1016_j_suscom_2024_100984 crossref_primary_10_1109_TPAMI_2024_3446949 crossref_primary_10_1109_ACCESS_2023_3277785 crossref_primary_10_1016_j_eswa_2023_122120 |
| Cites_doi | 10.1007/s11263-015-0816-y 10.1109/CVPR42600.2020.00637 10.1007/978-3-030-58555-6_42 10.1109/TPAMI.2017.2699184 10.1007/978-3-030-58574-7_25 10.1109/ICCV.2019.00153 10.1109/CVPR.2018.00352 10.1007/978-3-030-01225-0_29 10.1109/ICCV48922.2021.00696 10.1109/ITSC48978.2021.9564566 10.1109/ACCESS.2019.2949697 10.1109/CVPR.2019.00963 10.1109/ICCV.2015.169 10.1007/978-3-030-58598-3_44 10.1109/ICRA.2018.8460982 10.1109/CVPR.2019.00710 10.1109/CVPR.2016.350 10.1109/ICCV.2019.00393 10.1109/CVPR.2019.00258 10.1016/j.patcog.2021.108292 10.1177/0278364913491297 10.1109/CVPR.2016.352 10.1109/CVPR.2017.700 10.1007/978-3-030-58542-6_18 10.1109/CVPR46437.2021.00824 10.1186/s40537-016-0043-6 10.1007/978-3-030-58583-9_29 10.1109/CVPR42600.2020.01265 10.1109/ICCVW54120.2021.00202 10.1109/ICCV.2019.00107 10.1109/CVPR.2015.7298925 10.1007/978-3-030-01219-9_18 10.1109/ICCV.2019.00746 10.1007/s11263-014-0733-5 10.1007/978-3-319-46475-6_7 10.1109/CVPR42600.2020.00966 10.1016/j.patcog.2018.03.005 10.1109/CVPR.2015.7298965 10.1109/CVPR.2016.90 10.1109/WACV48630.2021.00052 10.1109/CVPR.2019.00262 10.1109/CVPR46437.2021.00127 10.1109/CVPR42600.2020.00382 10.1109/ICCV.2017.534 10.1109/CVPR42600.2020.01299 10.1109/ICCV.2019.00219 10.1109/WACVW54805.2022.00027 10.1109/CVPR46437.2021.01141 10.1109/CVPR.2018.00780 10.1109/CVPRW.2019.00181 10.1007/978-3-030-58542-6_5 10.1109/CVPR42600.2020.00414 10.1109/ICCV.2019.00693 10.1109/ICCV.2019.00218 10.1109/ICCV48922.2021.01005 10.1109/WACV48630.2021.00066 |
| ContentType | Journal Article |
| Copyright | Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2022 |
| Copyright_xml | – notice: Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2022 |
| DBID | 97E ESBDL RIA RIE AAYXX CITATION 7SC 7SP 8FD FR3 JQ2 KR7 L7M L~C L~D |
| DOI | 10.1109/TITS.2022.3190263 |
| DatabaseName | IEEE Xplore (IEEE) IEEE Xplore Open Access Journals IEEE All-Society Periodicals Package (ASPP) 1998–Present IEEE/IET Electronic Library (IEL) (UW System Shared) CrossRef Computer and Information Systems Abstracts Electronics & Communications Abstracts Technology Research Database Engineering Research Database ProQuest Computer Science Collection Civil Engineering Abstracts Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional |
| DatabaseTitle | CrossRef Civil Engineering Abstracts Technology Research Database Computer and Information Systems Abstracts – Academic Electronics & Communications Abstracts ProQuest Computer Science Collection Computer and Information Systems Abstracts Engineering Research Database Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Professional |
| DatabaseTitleList | Civil Engineering Abstracts |
| Database_xml | – sequence: 1 dbid: RIE name: IEEE/IET Electronic Library (IEL) (UW System Shared) url: https://ieeexplore.ieee.org/ sourceTypes: Publisher |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering |
| EISSN | 1558-0016 |
| EndPage | 20911 |
| ExternalDocumentID | 10_1109_TITS_2022_3190263 9843858 |
| Genre | orig-research |
| GroupedDBID | -~X 0R~ 29I 4.4 5GY 5VS 6IK 97E AAJGR AARMG AASAJ AAWTH ABAZT ABQJQ ABVLG ACGFO ACGFS ACIWK ACNCT AENEX AETIX AGQYO AGSQL AHBIQ AIBXA AKJIK AKQYR ALMA_UNASSIGNED_HOLDINGS ATWAV BEFXN BFFAM BGNUA BKEBE BPEOZ CS3 DU5 EBS EJD ESBDL HZ~ H~9 IFIPE IPLJI JAVBF LAI M43 O9- OCL P2P PQQKQ RIA RIE RNS ZY4 AAYXX CITATION 7SC 7SP 8FD FR3 JQ2 KR7 L7M L~C L~D |
| ID | FETCH-LOGICAL-c336t-b91323402de6dbb715d338f3eaeb5a6ad9d92eeb29a6ac298445b6278c665a233 |
| IEDL.DBID | RIE |
| ISICitedReferencesCount | 10 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000833052800001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1524-9050 |
| IngestDate | Sun Nov 30 04:55:23 EST 2025 Sat Nov 29 06:35:01 EST 2025 Tue Nov 18 21:52:55 EST 2025 Wed Aug 27 02:18:56 EDT 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 11 |
| Language | English |
| License | https://creativecommons.org/licenses/by/4.0/legalcode |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c336t-b91323402de6dbb715d338f3eaeb5a6ad9d92eeb29a6ac298445b6278c665a233 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| ORCID | 0000-0002-8895-5041 0000-0001-7675-750X |
| OpenAccessLink | https://ieeexplore.ieee.org/document/9843858 |
| PQID | 2734387304 |
| PQPubID | 75735 |
| PageCount | 13 |
| ParticipantIDs | ieee_primary_9843858 crossref_primary_10_1109_TITS_2022_3190263 crossref_citationtrail_10_1109_TITS_2022_3190263 proquest_journals_2734387304 |
| PublicationCentury | 2000 |
| PublicationDate | 2022-11-01 |
| PublicationDateYYYYMMDD | 2022-11-01 |
| PublicationDate_xml | – month: 11 year: 2022 text: 2022-11-01 day: 01 |
| PublicationDecade | 2020 |
| PublicationPlace | New York |
| PublicationPlace_xml | – name: New York |
| PublicationTitle | IEEE transactions on intelligent transportation systems |
| PublicationTitleAbbrev | TITS |
| PublicationYear | 2022 |
| Publisher | IEEE The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher_xml | – name: IEEE – name: The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| References | ref13 ref56 ref12 ref59 ref58 ref14 ref53 ref55 ref10 ref17 ref16 fleuret (ref54) 2021 ref19 ref18 li (ref49) 2020 kingma (ref65) 2015 ref51 ref50 ronneberger (ref72) 2015 ref46 ref45 ref48 ref42 ref41 ref44 ref43 ref8 hoffman (ref34) 2016 ref7 ref9 ref4 ref6 paszke (ref64) 2016 ref5 ref40 ref35 liang (ref52) 2020 ref37 ref36 ref31 ref30 ref33 ref32 stan (ref47) 2020 yu (ref61) 2018 ref2 ref1 ref38 dou (ref26) 2019 eigen (ref3) 2014 ref71 ref70 ref73 ref68 ref24 ref23 ioffe (ref57) 2015 ref69 ref25 ref20 ref66 lee (ref67) 2019 ref21 ref28 ref27 li (ref22) 2017 ref29 ganin (ref11) 2015 zhang (ref15) 2022; 122 ref60 simonyan (ref63) 2015 ref62 hoffman (ref39) 2018 |
| References_xml | – ident: ref71 doi: 10.1007/s11263-015-0816-y – ident: ref51 doi: 10.1109/CVPR42600.2020.00637 – ident: ref35 doi: 10.1007/978-3-030-58555-6_42 – ident: ref2 doi: 10.1109/TPAMI.2017.2699184 – ident: ref42 doi: 10.1007/978-3-030-58574-7_25 – start-page: 2366 year: 2014 ident: ref3 article-title: Depth map prediction from a single image using a multi-scale deep network publication-title: Proc Adv Neural Inf Process Syst – start-page: 448 year: 2015 ident: ref57 article-title: Batch normalization: Accelerating deep network training by reducing internal covariate shift publication-title: Proc Int Conf Mach Learn – ident: ref27 doi: 10.1109/ICCV.2019.00153 – ident: ref8 doi: 10.1109/CVPR.2018.00352 – start-page: 1 year: 2015 ident: ref63 article-title: Very deep convolutional networks for large-scale image recognition publication-title: Proc ICLR – ident: ref25 doi: 10.1007/978-3-030-01225-0_29 – ident: ref55 doi: 10.1109/ICCV48922.2021.00696 – ident: ref48 doi: 10.1109/ITSC48978.2021.9564566 – start-page: 6447 year: 2019 ident: ref26 article-title: Domain generalization via model-agnostic learning of semantic features publication-title: Proc NeurIPS – year: 2018 ident: ref61 article-title: BDD100 K: A diverse driving video database with scalable annotation tooling publication-title: arXiv 1805 04687 – start-page: 1 year: 2017 ident: ref22 article-title: Revisiting batch normalization for practical domain adaptation publication-title: Proc ICLR – ident: ref37 doi: 10.1109/ACCESS.2019.2949697 – start-page: 1 year: 2015 ident: ref65 article-title: Adam: A method for stochastic optimization publication-title: Proc ICLR – ident: ref6 doi: 10.1109/CVPR.2019.00963 – ident: ref7 doi: 10.1109/ICCV.2015.169 – ident: ref32 doi: 10.1007/978-3-030-58598-3_44 – start-page: 1180 year: 2015 ident: ref11 article-title: Unsupervised domain adaptation by backpropagation publication-title: Proc ICML – ident: ref46 doi: 10.1109/ICRA.2018.8460982 – ident: ref17 doi: 10.1109/CVPR.2019.00710 – start-page: 1989 year: 2018 ident: ref39 article-title: CyCADA: Cycle-consistent adversarial domain adaptation publication-title: Proc ICML – ident: ref10 doi: 10.1109/CVPR.2016.350 – year: 2016 ident: ref34 article-title: FCNs in the wild: Pixel-level adversarial and constraint-based adaptation publication-title: arXiv 1612 02649 – ident: ref73 doi: 10.1109/ICCV.2019.00393 – ident: ref38 doi: 10.1109/CVPR.2019.00258 – volume: 122 year: 2022 ident: ref15 article-title: Generalizable semantic segmentation via model-agnostic learning and target-specific normalization publication-title: Pattern Recognit doi: 10.1016/j.patcog.2021.108292 – ident: ref9 doi: 10.1177/0278364913491297 – ident: ref59 doi: 10.1109/CVPR.2016.352 – ident: ref4 doi: 10.1109/CVPR.2017.700 – ident: ref43 doi: 10.1007/978-3-030-58542-6_18 – ident: ref20 doi: 10.1109/CVPR46437.2021.00824 – ident: ref13 doi: 10.1186/s40537-016-0043-6 – ident: ref70 doi: 10.1007/978-3-030-58583-9_29 – ident: ref18 doi: 10.1109/CVPR42600.2020.01265 – ident: ref21 doi: 10.1109/ICCVW54120.2021.00202 – start-page: 9613 year: 2021 ident: ref54 article-title: Uncertainty reduction for model adaptation in semantic segmentation publication-title: Proc IEEE/CVF Conf Comput Vis Pattern Recognit (CVPR) – ident: ref33 doi: 10.1109/ICCV.2019.00107 – ident: ref60 doi: 10.1109/CVPR.2015.7298925 – start-page: 6028 year: 2020 ident: ref52 article-title: Do we really need to access the source data? Source hypothesis transfer for unsupervised domain adaptation publication-title: Proc ICML – ident: ref44 doi: 10.1007/978-3-030-01219-9_18 – ident: ref68 doi: 10.1109/ICCV.2019.00746 – ident: ref66 doi: 10.1007/s11263-014-0733-5 – ident: ref58 doi: 10.1007/978-3-319-46475-6_7 – ident: ref53 doi: 10.1109/CVPR42600.2020.00966 – year: 2020 ident: ref49 article-title: A free lunch for unsupervised domain adaptive object detection without source data publication-title: arXiv 2012 05400 – start-page: 1 year: 2019 ident: ref67 article-title: SPIGAN: Privileged adversarial learning from simulation publication-title: Proc ICLR – ident: ref23 doi: 10.1016/j.patcog.2018.03.005 – ident: ref1 doi: 10.1109/CVPR.2015.7298965 – ident: ref5 doi: 10.1109/CVPR.2016.90 – ident: ref50 doi: 10.1109/WACV48630.2021.00052 – year: 2020 ident: ref47 article-title: Unsupervised model adaptation for continual semantic segmentation publication-title: arXiv 2009 12518 – ident: ref36 doi: 10.1109/CVPR.2019.00262 – ident: ref56 doi: 10.1109/CVPR46437.2021.00127 – year: 2016 ident: ref64 article-title: ENet: A deep neural network architecture for real-time semantic segmentation publication-title: ArXiv 1606 02147 – ident: ref16 doi: 10.1109/CVPR42600.2020.00382 – ident: ref62 doi: 10.1109/ICCV.2017.534 – ident: ref69 doi: 10.1109/CVPR42600.2020.01299 – ident: ref14 doi: 10.1109/ICCV.2019.00219 – start-page: 234 year: 2015 ident: ref72 article-title: U-Net: Convolutional networks for biomedical image segmentation publication-title: Proc Int Conf Med Image Comput Comput -Assist Intervent – ident: ref19 doi: 10.1109/WACVW54805.2022.00027 – ident: ref29 doi: 10.1109/CVPR46437.2021.01141 – ident: ref12 doi: 10.1109/CVPR.2018.00780 – ident: ref30 doi: 10.1109/CVPRW.2019.00181 – ident: ref28 doi: 10.1007/978-3-030-58542-6_5 – ident: ref40 doi: 10.1109/CVPR42600.2020.00414 – ident: ref41 doi: 10.1109/ICCV.2019.00693 – ident: ref31 doi: 10.1109/ICCV.2019.00218 – ident: ref24 doi: 10.1109/ICCV48922.2021.01005 – ident: ref45 doi: 10.1109/WACV48630.2021.00066 |
| SSID | ssj0014511 |
| Score | 2.466021 |
| Snippet | Environment perception in autonomous driving vehicles often heavily relies on deep neural networks (DNNs), which are subject to domain shifts, leading to a... |
| SourceID | proquest crossref ieee |
| SourceType | Aggregation Database Enrichment Source Index Database Publisher |
| StartPage | 20899 |
| SubjectTerms | Adaptation Adaptation models Artificial neural networks batch normalization Data models deep learning Delays Domain adaptation Domains Image segmentation Neural networks Semantic segmentation Semantics Task analysis unsupervised learning |
| Title | Continual BatchNorm Adaptation (CBNA) for Semantic Segmentation |
| URI | https://ieeexplore.ieee.org/document/9843858 https://www.proquest.com/docview/2734387304 |
| Volume | 23 |
| WOSCitedRecordID | wos000833052800001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVIEE databaseName: IEEE/IET Electronic Library (IEL) (UW System Shared) customDbUrl: eissn: 1558-0016 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0014511 issn: 1524-9050 databaseCode: RIE dateStart: 20000101 isFulltext: true titleUrlDefault: https://ieeexplore.ieee.org/ providerName: IEEE |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1bS8MwFD5swwd98DbF6ZQ--KBiXJv0lieZw6EgRdiEvZU0yXTgLuzi7_ck7YqiCL6lkJTwpT35vp6eLwDnXiAE7ro-Ea5mxA89RWJfRQSDZSwjOWRBZi3zn6IkiQcD_lyB67IWRmttfz7TN6Zpc_lqKlfmU1mLx77JY1WhGkVRXqtVZgyMz5b1RqU-4W6wzmB6Lm_1H_s9VIKUokDlqDnYtz3IHqryIxLb7aW787-J7cJ2QSOddr7ue1DRk33Y-mIuWIdbYzw1Mo6jzh3G27cE2anTVmKWJ9-di85d0r50kLQ6PT1GgEcSG6_johhpcgAv3ft-54EUxyUQyVi4JBlHZclQDyodqiyLvECh_hwyLXQWiFAorjjVqKQ5XkiKc_aDLKRRLMMwEJSxQ6hNphN9BI6kNJOe5FrL2KcKWTjyNMEUcklkLC5vgLsGMJWFl7g50uI9tZrC5anBPDWYpwXmDbgqh8xyI42_OtcNyGXHAt8GNNerlBav2iI1_jwsxkDlH_8-6gQ2zb3zAsIm1JbzlT6FDfmxHC3mZ_Yp-gQn68I7 |
| linkProvider | IEEE |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1dS8MwFL3MKagPfovTqX3wQcW6Nkk_8iRTFIezCJvgW0iTTAc6ZZv-fm_SbiiK4FsKCQ0n7c05vb0nAAdhJCXuusyXgaE-i0Ptp0wnPgbLVCWqR6PcWea3kyxLHx74XQVOprUwxhj385k5tU2Xy9ev6t1-KmvwlNk81gzMRoyRsKjWmuYMrNOWc0clzOdBNMlhhgFvdFvdDmpBQlCiclQd9Nsu5I5V-RGL3QZztfy_qa3AUkkkvWax8qtQMYM1WPxiL7gOZ9Z6qm89R71zjLhPGfJTr6nlW5F-9w4vzrPmkYe01euYF4S4r7Dx-FKWIw024P7qsntx7ZcHJviK0njs5xy1JUVFqE2s8zwJI40KtEeNNHkkY6m55sSgluZ4oQjOmUV5TJJUxXEkCaWbUB28DswWeIqQXIWKG6NSRjTycGRqkmpkk8hZAl6DYAKgUKWbuD3U4lk4VRFwYTEXFnNRYl6D4-mQt8JK46_O6xbkaccS3xrUJ6skypdtJKxDD00xVLHt30ftw_x197Yt2q3sZgcW7H2KcsI6VMfDd7MLc-pj3B8N99wT9Qlwe8WC |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Continual+BatchNorm+Adaptation+%28CBNA%29+for+Semantic+Segmentation&rft.jtitle=IEEE+transactions+on+intelligent+transportation+systems&rft.au=Klingner%2C+Marvin&rft.au=Ayache%2C+Mouadh&rft.au=Fingscheidt%2C+Tim&rft.date=2022-11-01&rft.pub=The+Institute+of+Electrical+and+Electronics+Engineers%2C+Inc.+%28IEEE%29&rft.issn=1524-9050&rft.eissn=1558-0016&rft.volume=23&rft.issue=11&rft.spage=20899&rft_id=info:doi/10.1109%2FTITS.2022.3190263&rft.externalDBID=NO_FULL_TEXT |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1524-9050&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1524-9050&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1524-9050&client=summon |