{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,11]],"date-time":"2026-04-11T06:59:29Z","timestamp":1775890769047,"version":"3.50.1"},"reference-count":84,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"1","license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":["IEEE Trans. Pattern Anal. Mach. Intell."],"published-print":{"date-parts":[[2024,1]]},"DOI":"10.1109\/tpami.2023.3320613","type":"journal-article","created":{"date-parts":[[2023,9,28]],"date-time":"2023-09-28T17:45:50Z","timestamp":1695923150000},"page":"220-235","source":"Crossref","is-referenced-by-count":34,"title":["Domain Adaptive and Generalizable Network Architectures and Training Strategies for Semantic Image Segmentation"],"prefix":"10.1109","volume":"46","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7391-0676","authenticated-orcid":false,"given":"Lukas","family":"Hoyer","sequence":"first","affiliation":[{"name":"ETH Zurich, Z&#x00FC;rich, Switzerland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5440-9678","authenticated-orcid":false,"given":"Dengxin","family":"Dai","sequence":"additional","affiliation":[{"name":"Huawei Zurich Research Center, Z&#x00FC;rich, Switzerland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3445-5711","authenticated-orcid":false,"given":"Luc","family":"Van Gool","sequence":"additional","affiliation":[{"name":"ETH Zurich, Z&#x00FC;rich, Switzerland"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.350"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.01059"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.352"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46475-6_7"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2017.2699184"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00780"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58539-6_11"},{"key":"ref8","article-title":"Hierarchical multi-scale attention for semantic segmentation","author":"Tao","year":"2020"},{"key":"ref9","first-page":"1","article-title":"An image is worth 16x16 words: Transformers for image recognition at scale","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Dosovitskiy"},{"key":"ref10","first-page":"12077","article-title":"SegFormer: Simple and efficient design for semantic segmentation with transformers","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Xie"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.01007"},{"key":"ref12","article-title":"Accurate, large minibatch SGD: Training ImageNet in 1 hour","author":"Goyal","year":"2017"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/WACV48630.2021.00142"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01513"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00840"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58555-6_42"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/WACV48630.2021.00056"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00969"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-20056-4_22"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01223"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00747"},{"key":"ref24","first-page":"6462","article-title":"Grid saliency for context explanations of semantic segmentation","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Hoyer"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00813"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00326"},{"key":"ref27","first-page":"5998","article-title":"Attention is all you need","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Vaswani"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00767"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.396"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-00767-6_22"},{"key":"ref31","first-page":"1","article-title":"Benchmarking neural network robustness to common corruptions and perturbations","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Hendrycks"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00823"},{"key":"ref33","first-page":"23296","article-title":"Intriguing properties of vision transformers","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Naseer"},{"key":"ref34","first-page":"1","article-title":"ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Geirhos"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-020-01383-2"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01225-0_29"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01141"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00219"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2021.3096334"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-19815-1_31"},{"key":"ref41","first-page":"338","article-title":"Adversarial style augmentation for domain generalized urban-scene segmentation","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Zhong"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-19815-1_31"},{"key":"ref43","first-page":"1989","article-title":"CyCADA: Cycle-consistent adversarial domain adaptation","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Hoffman"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-021-01496-2"},{"key":"ref45","article-title":"FCNs in the wild: Pixel-level adversarial and constraint-based adaptation","author":"Hoffman","year":"2016"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00262"},{"key":"ref47","first-page":"2672","article-title":"Generative adversarial nets","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Goodfellow"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01219-9_18"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-019-01182-4"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/ITSC.2018.8569387"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.01128"},{"key":"ref52","first-page":"514","article-title":"Context-aware mixup for domain adaptive semantic segmentation","volume-title":"Proc. Winter Conf. Appl. Comput. Vis.","author":"Zhou"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/cvpr46437.2021.01098"},{"key":"ref54","first-page":"7032","article-title":"Learning to model the tail","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Wang"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00844"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2017.2773081"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01098"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00823"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58568-6_38"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58542-6_18"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1109\/WACV45572.2020.9093626"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.167"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00608"},{"key":"ref64","first-page":"1195","article-title":"Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Tarvainen"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.1109\/WACV48630.2021.00141"},{"key":"ref66","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00681"},{"key":"ref67","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref68","first-page":"1","article-title":"On the variance of the adaptive learning rate and beyond","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Liu"},{"key":"ref69","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-19827-4_22"},{"key":"ref70","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00747"},{"key":"ref71","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2020.3045882"},{"key":"ref72","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01551"},{"key":"ref73","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00271"},{"key":"ref74","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.534"},{"key":"ref75","article-title":"MMSegmentation: OpenMMLab semantic segmentation toolbox and benchmark","author":"Contributors","year":"2020"},{"key":"ref76","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00682"},{"key":"ref77","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00262"},{"key":"ref78","article-title":"VisDA 2022 challenge: Domain adaptation for industrial waste sorting","author":"Bashkirova","year":"2023"},{"key":"ref79","first-page":"19234","article-title":"EDAPS: Enhanced domain-adaptive panoptic segmentation","volume-title":"Proc. IEEE Int. Conf. Comput. Vis.","author":"Saha"},{"key":"ref80","doi-asserted-by":"publisher","DOI":"10.1109\/WACV56688.2023.00619"},{"key":"ref81","article-title":"QuadFormer: Quadruple transformer for unsupervised domain adaptation in power line segmentation of aerial images","author":"Rao","year":"2022"},{"key":"ref82","article-title":"Interlaced sparse self-attention for semantic segmentation","author":"Huang","year":"2019"},{"key":"ref83","doi-asserted-by":"publisher","DOI":"10.1109\/cvprw56347.2022.00309"},{"key":"ref84","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01228-1_26"}],"container-title":["IEEE Transactions on Pattern Analysis and Machine Intelligence"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/34\/10345401\/10266755.pdf?arnumber=10266755","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,12,20]],"date-time":"2023-12-20T01:22:29Z","timestamp":1703035349000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10266755\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1]]},"references-count":84,"journal-issue":{"issue":"1"},"URL":"https:\/\/doi.org\/10.1109\/tpami.2023.3320613","relation":{},"ISSN":["0162-8828","2160-9292","1939-3539"],"issn-type":[{"value":"0162-8828","type":"print"},{"value":"2160-9292","type":"electronic"},{"value":"1939-3539","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1]]}}}