{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,12]],"date-time":"2025-12-12T13:08:52Z","timestamp":1765544932092,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":49,"publisher":"ACM","license":[{"start":{"date-parts":[[2023,10,21]],"date-time":"2023-10-21T00:00:00Z","timestamp":1697846400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"Jiangsu Science and Technology Programme","award":["BE2020006-4"],"award-info":[{"award-number":["BE2020006-4"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,10,21]]},"DOI":"10.1145\/3583780.3614872","type":"proceedings-article","created":{"date-parts":[[2023,10,21]],"date-time":"2023-10-21T07:45:26Z","timestamp":1697874326000},"page":"2990-2998","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["Explore Epistemic Uncertainty in Domain Adaptive Semantic Segmentation"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4623-0365","authenticated-orcid":false,"given":"Kai","family":"Yao","sequence":"first","affiliation":[{"name":"University of Liverpool &amp; Xi'an Jiaotong-Liverpool University, Liverpool, United Kingdom"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1750-1501","authenticated-orcid":false,"given":"Zixian","family":"Su","sequence":"additional","affiliation":[{"name":"University of Liverpool &amp; Xi'an Jiaotong-Liverpool University, Liverpool, United Kingdom"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8600-2570","authenticated-orcid":false,"given":"Xi","family":"Yang","sequence":"additional","affiliation":[{"name":"Xi'an Jiaotong-Liverpool University, Suzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4654-3081","authenticated-orcid":false,"given":"Jie","family":"Sun","sequence":"additional","affiliation":[{"name":"Xi'an Jiaotong-Liverpool University, Suzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3034-9639","authenticated-orcid":false,"given":"Kaizhu","family":"Huang","sequence":"additional","affiliation":[{"name":"Duke Kunshan University, Kunshan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2023,10,21]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Uncertainty in the variational information bottleneck. arXiv preprint arXiv:1807.00906","author":"Alemi Alexander A","year":"2018","unstructured":"Alexander A Alemi , Ian Fischer , and Joshua V Dillon . 2018. Uncertainty in the variational information bottleneck. arXiv preprint arXiv:1807.00906 ( 2018 ). Alexander A Alemi, Ian Fischer, and Joshua V Dillon. 2018. Uncertainty in the variational information bottleneck. arXiv preprint arXiv:1807.00906 (2018)."},{"volume-title":"Information theory","author":"Ash Robert B","key":"e_1_3_2_1_2_1","unstructured":"Robert B Ash . 2012. Information theory . Courier Corporation . Robert B Ash. 2012. Information theory. Courier Corporation."},{"key":"e_1_3_2_1_3_1","first-page":"3422","article-title":"Homm: Higher-order moment matching for unsupervised domain adaptation","volume":"34","author":"Chen Chao","year":"2020","unstructured":"Chao Chen , Zhihang Fu , Zhihong Chen , Sheng Jin , Zhaowei Cheng , Xinyu Jin , and Xian-Sheng Hua . 2020 . Homm: Higher-order moment matching for unsupervised domain adaptation . In Association for the Advancement of Artificial Intelligence , Vol. 34. 3422 -- 3429 . Chao Chen, Zhihang Fu, Zhihong Chen, Sheng Jin, Zhaowei Cheng, Xinyu Jin, and Xian-Sheng Hua. 2020. Homm: Higher-order moment matching for unsupervised domain adaptation. In Association for the Advancement of Artificial Intelligence, Vol. 34. 3422--3429.","journal-title":"Association for the Advancement of Artificial Intelligence"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2017.2699184"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00352"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.350"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"e_1_3_2_1_8_1","volume-title":"Aleatory or epistemic? Does it matter? Structural safety","author":"Kiureghian Armen Der","year":"2009","unstructured":"Armen Der Kiureghian and Ove Ditlevsen . 2009. Aleatory or epistemic? Does it matter? Structural safety , Vol. 31 , 2 ( 2009 ), 105--112. Armen Der Kiureghian and Ove Ditlevsen. 2009. Aleatory or epistemic? Does it matter? Structural safety, Vol. 31, 2 (2009), 105--112."},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-19775-8_15"},{"key":"e_1_3_2_1_10_1","volume-title":"International Conference on Machine Learning. 1050--1059","author":"Gal Yarin","year":"2016","unstructured":"Yarin Gal and Zoubin Ghahramani . 2016 . Dropout as a bayesian approximation: Representing model uncertainty in deep learning . In International Conference on Machine Learning. 1050--1059 . Yarin Gal and Zoubin Ghahramani. 2016. Dropout as a bayesian approximation: Representing model uncertainty in deep learning. In International Conference on Machine Learning. 1050--1059."},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00392"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_3_2_1_13_1","volume-title":"International Conference on Learning Representations.","author":"Hendrycks Dan","year":"2017","unstructured":"Dan Hendrycks and Kevin Gimpel . 2017 . A baseline for detecting misclassified and out-of-distribution examples in neural networks . In International Conference on Learning Representations. Dan Hendrycks and Kevin Gimpel. 2017. A baseline for detecting misclassified and out-of-distribution examples in neural networks. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_14_1","volume-title":"International Conference on Machine Learning. 1989--1998","author":"Hoffman Judy","year":"2018","unstructured":"Judy Hoffman , Eric Tzeng , Taesung Park , Jun-Yan Zhu , Phillip Isola , Kate Saenko , Alexei Efros , and Trevor Darrell . 2018 . Cycada: Cycle-consistent adversarial domain adaptation . In International Conference on Machine Learning. 1989--1998 . Judy Hoffman, Eric Tzeng, Taesung Park, Jun-Yan Zhu, Phillip Isola, Kate Saenko, Alexei Efros, and Trevor Darrell. 2018. Cycada: Cycle-consistent adversarial domain adaptation. In International Conference on Machine Learning. 1989--1998."},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00969"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00694"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-19830-4_3"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00503"},{"key":"e_1_3_2_1_19_1","volume-title":"Advances in Neural Information Processing Systems","volume":"30","author":"Kendall Alex","year":"2017","unstructured":"Alex Kendall and Yarin Gal . 2017 . What uncertainties do we need in bayesian deep learning for computer vision ? Advances in Neural Information Processing Systems , Vol. 30 (2017). Alex Kendall and Yarin Gal. 2017. What uncertainties do we need in bayesian deep learning for computer vision? Advances in Neural Information Processing Systems, Vol. 30 (2017)."},{"key":"e_1_3_2_1_20_1","volume-title":"Advances in Neural Information Processing Systems","volume":"28","author":"Kingma Durk P","year":"2015","unstructured":"Durk P Kingma , Tim Salimans , and Max Welling . 2015 . Variational dropout and the local reparameterization trick . Advances in Neural Information Processing Systems , Vol. 28 (2015). Durk P Kingma, Tim Salimans, and Max Welling. 2015. Variational dropout and the local reparameterization trick. Advances in Neural Information Processing Systems, Vol. 28 (2015)."},{"key":"e_1_3_2_1_21_1","volume-title":"DecoupleNet: Decoupled Network for Domain Adaptive Semantic Segmentation. In European Conference on Computer Vision. https:\/\/doi.org\/10","author":"Lai Xin","year":"2022","unstructured":"Xin Lai , Zhuotao Tian , Xiaogang Xu , Yingcong Chen , Shu Liu , Hengshuang Zhao , Liwei Wang , and Jiaya Jia . 2022 . DecoupleNet: Decoupled Network for Domain Adaptive Semantic Segmentation. In European Conference on Computer Vision. https:\/\/doi.org\/10 .1007\/978--3-031--19827--4_22 10.1007\/978--3-031--19827--4_22 Xin Lai, Zhuotao Tian, Xiaogang Xu, Yingcong Chen, Shu Liu, Hengshuang Zhao, Liwei Wang, and Jiaya Jia. 2022. DecoupleNet: Decoupled Network for Domain Adaptive Semantic Segmentation. In European Conference on Computer Vision. https:\/\/doi.org\/10.1007\/978--3-031--19827--4_22"},{"key":"e_1_3_2_1_22_1","volume-title":"Advances in Neural Information Processing Systems","volume":"30","author":"Lakshminarayanan Balaji","year":"2017","unstructured":"Balaji Lakshminarayanan , Alexander Pritzel , and Charles Blundell . 2017 . Simple and scalable predictive uncertainty estimation using deep ensembles . Advances in Neural Information Processing Systems , Vol. 30 (2017). Balaji Lakshminarayanan, Alexander Pritzel, and Charles Blundell. 2017. Simple and scalable predictive uncertainty estimation using deep ensembles. Advances in Neural Information Processing Systems, Vol. 30 (2017)."},{"key":"e_1_3_2_1_23_1","first-page":"7498","article-title":"Simple and principled uncertainty estimation with deterministic deep learning via distance awareness","volume":"33","author":"Liu Jeremiah","year":"2020","unstructured":"Jeremiah Liu , Zi Lin , Shreyas Padhy , Dustin Tran , Tania Bedrax Weiss , and Balaji Lakshminarayanan . 2020 . Simple and principled uncertainty estimation with deterministic deep learning via distance awareness . Advances in Neural Information Processing Systems , Vol. 33 (2020), 7498 -- 7512 . Jeremiah Liu, Zi Lin, Shreyas Padhy, Dustin Tran, Tania Bedrax Weiss, and Balaji Lakshminarayanan. 2020. Simple and principled uncertainty estimation with deterministic deep learning via distance awareness. Advances in Neural Information Processing Systems, Vol. 33 (2020), 7498--7512.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_24_1","volume-title":"Advances in Neural Information Processing Systems","volume":"31","author":"Long Mingsheng","year":"2018","unstructured":"Mingsheng Long , Zhangjie Cao , Jianmin Wang , and Michael I Jordan . 2018 . Conditional adversarial domain adaptation . Advances in Neural Information Processing Systems , Vol. 31 (2018). Mingsheng Long, Zhangjie Cao, Jianmin Wang, and Michael I Jordan. 2018. Conditional adversarial domain adaptation. Advances in Neural Information Processing Systems, Vol. 31 (2018)."},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2013.274"},{"key":"e_1_3_2_1_26_1","volume-title":"International Conference on Learning Representations.","author":"Loshchilov Ilya","year":"2018","unstructured":"Ilya Loshchilov and Frank Hutter . 2018 . Decoupled weight decay regularization . In International Conference on Learning Representations. Ilya Loshchilov and Frank Hutter. 2018. Decoupled weight decay regularization. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN52387.2021.9533899"},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58574-7_25"},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1109\/WACV48630.2021.00141"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00149"},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00302"},{"key":"e_1_3_2_1_32_1","volume-title":"International Conference on Machine Learning.","author":"Postels Janis","year":"2022","unstructured":"Janis Postels , Mattia Segu , Tao Sun , Luc Van Gool , Fisher Yu , and Federico Tombari . 2022 . On the practicality of deterministic epistemic uncertainty . In International Conference on Machine Learning. Janis Postels, Mattia Segu, Tao Sun, Luc Van Gool, Fisher Yu, and Federico Tombari. 2022. On the practicality of deterministic epistemic uncertainty. In International Conference on Machine Learning."},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46475-6_7"},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.352"},{"key":"e_1_3_2_1_36_1","volume-title":"Advances in Neural Information Processing Systems","volume":"30","author":"Tarvainen Antti","year":"2017","unstructured":"Antti Tarvainen and Harri Valpola . 2017 . Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results . Advances in Neural Information Processing Systems , Vol. 30 (2017). Antti Tarvainen and Harri Valpola. 2017. Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results. Advances in Neural Information Processing Systems, Vol. 30 (2017)."},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1109\/WACV48630.2021.00142"},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00780"},{"key":"e_1_3_2_1_39_1","volume-title":"On feature collapse and deep kernel learning for single forward pass uncertainty. arXiv preprint arXiv:2102.11409","author":"van Amersfoort Joost","year":"2021","unstructured":"Joost van Amersfoort , Lewis Smith , Andrew Jesson , Oscar Key , and Yarin Gal . 2021. On feature collapse and deep kernel learning for single forward pass uncertainty. arXiv preprint arXiv:2102.11409 ( 2021 ). Joost van Amersfoort, Lewis Smith, Andrew Jesson, Oscar Key, and Yarin Gal. 2021. On feature collapse and deep kernel learning for single forward pass uncertainty. arXiv preprint arXiv:2102.11409 (2021)."},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00840"},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.107"},{"key":"e_1_3_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01023"},{"key":"e_1_3_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00414"},{"key":"e_1_3_2_1_44_1","first-page":"10754","article-title":"Dast: Unsupervised domain adaptation in semantic segmentation based on discriminator attention and self-training","volume":"35","author":"Yu Fei","year":"2021","unstructured":"Fei Yu , Mo Zhang , Hexin Dong , Sheng Hu , Bin Dong , and Li Zhang . 2021 . Dast: Unsupervised domain adaptation in semantic segmentation based on discriminator attention and self-training . In Association for the Advancement of Artificial Intelligence , Vol. 35. 10754 -- 10762 . Fei Yu, Mo Zhang, Hexin Dong, Sheng Hu, Bin Dong, and Li Zhang. 2021. Dast: Unsupervised domain adaptation in semantic segmentation based on discriminator attention and self-training. In Association for the Advancement of Artificial Intelligence, Vol. 35. 10754--10762.","journal-title":"Association for the Advancement of Artificial Intelligence"},{"key":"e_1_3_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01223"},{"key":"e_1_3_2_1_46_1","volume-title":"International Conference on Machine Learning. 7404--7413","author":"Zhang Yuchen","year":"2019","unstructured":"Yuchen Zhang , Tianle Liu , Mingsheng Long , and Michael Jordan . 2019 a. Bridging theory and algorithm for domain adaptation . In International Conference on Machine Learning. 7404--7413 . Yuchen Zhang, Tianle Liu, Mingsheng Long, and Michael Jordan. 2019a. Bridging theory and algorithm for domain adaptation. In International Conference on Machine Learning. 7404--7413."},{"key":"e_1_3_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00517"},{"key":"e_1_3_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-020-01395-y"},{"key":"e_1_3_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01219-9_18"}],"event":{"name":"CIKM '23: The 32nd ACM International Conference on Information and Knowledge Management","sponsor":["SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web","SIGIR ACM Special Interest Group on Information Retrieval"],"location":"Birmingham United Kingdom","acronym":"CIKM '23"},"container-title":["Proceedings of the 32nd ACM International Conference on Information and Knowledge Management"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3583780.3614872","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3583780.3614872","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T16:36:57Z","timestamp":1750178217000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3583780.3614872"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,21]]},"references-count":49,"alternative-id":["10.1145\/3583780.3614872","10.1145\/3583780"],"URL":"https:\/\/doi.org\/10.1145\/3583780.3614872","relation":{},"subject":[],"published":{"date-parts":[[2023,10,21]]},"assertion":[{"value":"2023-10-21","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}