{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T16:46:50Z","timestamp":1777567610612,"version":"3.51.4"},"publisher-location":"New York, NY, USA","reference-count":52,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,10,28]],"date-time":"2024-10-28T00:00:00Z","timestamp":1730073600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/https:\/\/doi.org\/10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61972091"],"award-info":[{"award-number":["61972091"]}],"id":[{"id":"10.13039\/https:\/\/doi.org\/10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Natural Science Foundation of Guangdong Province of China","award":["2022A1515010101, 2021A1515012639"],"award-info":[{"award-number":["2022A1515010101, 2021A1515012639"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,10,28]]},"DOI":"10.1145\/3664647.3680910","type":"proceedings-article","created":{"date-parts":[[2024,10,26]],"date-time":"2024-10-26T06:59:49Z","timestamp":1729925989000},"page":"10085-10094","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":6,"title":["From Question to Exploration: Can Classic Test-Time Adaptation Strategies Be Effectively Applied in Semantic Segmentation?"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3955-9701","authenticated-orcid":false,"given":"Chang'an","family":"Yi","sequence":"first","affiliation":[{"name":"School of Electronic and Information Engineering, Foshan University, Foshan, Guangdong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5493-2549","authenticated-orcid":false,"given":"Haotian","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Software, Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan, Shandong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2125-1074","authenticated-orcid":false,"given":"Yifan","family":"Zhang","sequence":"additional","affiliation":[{"name":"Skywork AI, Singapore, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1891-6186","authenticated-orcid":false,"given":"Yonghui","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Software, Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan, Shandong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-0886-5906","authenticated-orcid":false,"given":"Yan","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Electronic and Information Engineering, Foshan University, Foshan, Guangdong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8262-8883","authenticated-orcid":false,"given":"Lizhen","family":"Cui","sequence":"additional","affiliation":[{"name":"School of Software, Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan, Shandong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2024,10,28]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Jamie Ryan Kiros, and Geoffrey E Hinton","author":"Ba Jimmy Lei","year":"2016","unstructured":"Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E Hinton. 2016. Layer normalization. arXiv preprint arXiv:1607.06450 (2016)."},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.01517"},{"key":"e_1_3_2_1_3_1","first-page":"1","article-title":"The comparison and evaluation of forecasters","volume":"32","author":"DeGroot Morris H","year":"1983","unstructured":"Morris H DeGroot and Stephen E Fienberg. 1983. The comparison and evaluation of forecasters. Journal of the Royal Statistical Society: Series D (The Statistician), Vol. 32, 1--2 (1983), 12--22.","journal-title":"Journal of the Royal Statistical Society: Series D (The Statistician)"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i6.25922"},{"key":"e_1_3_2_1_5_1","volume-title":"Visual prompt tuning for test-time domain adaptation. arXiv preprint arXiv:2210.04831","author":"Gao Yunhe","year":"2022","unstructured":"Yunhe Gao, Xingjian Shi, Yi Zhu, Hao Wang, Zhiqiang Tang, Xiong Zhou, Mu Li, and Dimitris N Metaxas. 2022. Visual prompt tuning for test-time domain adaptation. arXiv preprint arXiv:2210.04831 (2022)."},{"key":"e_1_3_2_1_6_1","first-page":"27253","article-title":"NOTE: Robust continual test-time adaptation against temporal correlation","volume":"35","author":"Gong Taesik","year":"2022","unstructured":"Taesik Gong, Jongheon Jeong, Taewon Kim, Yewon Kim, Jinwoo Shin, and Sung-Ju Lee. 2022. NOTE: Robust continual test-time adaptation against temporal correlation. Advances in Neural Information Processing Systems, Vol. 35 (2022), 27253--27266.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_7_1","volume-title":"International Conference On Learning Representations. 1--11","author":"Hendrycks Dan","year":"2019","unstructured":"Dan Hendrycks and Thomas Dietterich. 2019. Benchmarking neural network robustness to common corruptions and perturbations. In International Conference On Learning Representations. 1--11."},{"key":"e_1_3_2_1_8_1","volume-title":"Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531","author":"Hinton Geoffrey","year":"2015","unstructured":"Geoffrey Hinton, Oriol Vinyals, and Jeff Dean. 2015. Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531 (2015)."},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00969"},{"key":"e_1_3_2_1_10_1","volume-title":"LoRA: Low-Rank Adaptation of Large Language Models. In International Conference on Learning Representations. 1--16","author":"Hu Edward J","year":"2022","unstructured":"Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. 2022. LoRA: Low-Rank Adaptation of Large Language Models. In International Conference on Learning Representations. 1--16."},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00821"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.00292"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-19827-4_41"},{"key":"e_1_3_2_1_14_1","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 23090--23099","author":"Khurana Ansh","year":"2023","unstructured":"Ansh Khurana, Sujoy Paul, Piyush Rai, Soma Biswas, and Gaurav Aggarwal. 2023. SITA: Single Image Test-time Adaptation. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 23090--23099."},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.00371"},{"key":"e_1_3_2_1_16_1","volume-title":"International Conference on Machine Learning. 5637--5664","author":"Koh Pang Wei","year":"2021","unstructured":"Pang Wei Koh, Shiori Sagawa, Henrik Marklund, Sang Michael Xie, Marvin Zhang, Akshay Balsubramani, Weihua Hu, Michihiro Yasunaga, Richard Lanas Phillips, Irena Gao, et al. 2021. Wilds: A benchmark of in-the-wild distribution shifts. In International Conference on Machine Learning. 5637--5664."},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i2.25228"},{"key":"e_1_3_2_1_18_1","volume-title":"A comprehensive survey on test-time adaptation under distribution shifts. arXiv preprint arXiv:2303.15361","author":"Liang Jian","year":"2023","unstructured":"Jian Liang, Ran He, and Tieniu Tan. 2023. A comprehensive survey on test-time adaptation under distribution shifts. arXiv preprint arXiv:2303.15361 (2023)."},{"key":"e_1_3_2_1_19_1","volume-title":"International Conference on Machine Learning. 6028--6039","author":"Liang Jian","year":"2020","unstructured":"Jian Liang, Dapeng Hu, and Jiashi Feng. 2020. Do we really need to access the source data? source hypothesis transfer for unsupervised domain adaptation. In International Conference on Machine Learning. 6028--6039."},{"key":"e_1_3_2_1_20_1","volume-title":"European Conference on Computer Vision.","author":"Lin Hongbin","year":"2024","unstructured":"Hongbin Lin, Yifan Zhang, Shuaicheng Niu, Shuguang Cui, and Zhen Li. 2024. Fully Test-Time Adaptation for Monocular 3D Object Detection. In European Conference on Computer Vision."},{"key":"e_1_3_2_1_21_1","first-page":"1","article-title":"Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing","volume":"55","author":"Liu Pengfei","year":"2023","unstructured":"Pengfei Liu, Weizhe Yuan, Jinlan Fu, Zhengbao Jiang, Hiroaki Hayashi, and Graham Neubig. 2023. Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing. Comput. Surveys, Vol. 55, 9 (2023), 1--35.","journal-title":"Comput. Surveys"},{"key":"e_1_3_2_1_22_1","first-page":"21808","article-title":"TTT: When does self-supervised test-time training fail or thrive","volume":"34","author":"Liu Yuejiang","year":"2021","unstructured":"Yuejiang Liu, Parth Kothari, Bastien Van Delft, Baptiste Bellot-Gurlet, Taylor Mordan, and Alexandre Alahi. 2021. TTT: When does self-supervised test-time training fail or thrive? Advances in Neural Information Processing Systems, Vol. 34 (2021), 21808--21820.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00695"},{"key":"e_1_3_2_1_24_1","volume-title":"Conference on Uncertainty in Artificial Intelligence. 1308--1317","author":"Lyzhov Alexander","year":"2020","unstructured":"Alexander Lyzhov, Yuliya Molchanova, Arsenii Ashukha, Dmitry Molchanov, and Dmitry Vetrov. 2020. Greedy policy search: A simple baseline for learnable test-time augmentation. In Conference on Uncertainty in Artificial Intelligence. 1308--1317."},{"key":"e_1_3_2_1_25_1","volume-title":"Advances in Neural Information Processing Systems","volume":"36","author":"Ma Xinhong","year":"2023","unstructured":"Xinhong Ma, Yiming Wang, Hao Liu, Tianyu Guo, and Yunhe Wang. 2023. When Visual Prompt Tuning Meets Source-Free Domain Adaptive Semantic Segmentation. Advances in Neural Information Processing Systems, Vol. 36 (2023)."},{"key":"e_1_3_2_1_26_1","volume-title":"Workshop of International Conference on Machine Learning. 1--17","author":"Nado Zachary","year":"2020","unstructured":"Zachary Nado, Shreyas Padhy, D Sculley, Alexander D'Amour, Balaji Lakshminarayanan, and Jasper Snoek. 2020. Evaluating prediction-time batch normalization for robustness under covariate shift. In Workshop of International Conference on Machine Learning. 1--17."},{"key":"e_1_3_2_1_27_1","volume-title":"International conference on machine learning. 16888--16905","author":"Niu Shuaicheng","year":"2022","unstructured":"Shuaicheng Niu, Jiaxiang Wu, Yifan Zhang, Yaofo Chen, Shijian Zheng, Peilin Zhao, and Mingkui Tan. 2022. Efficient test-time model adaptation without forgetting. In International conference on machine learning. 16888--16905."},{"key":"e_1_3_2_1_28_1","volume-title":"International conference on machine learning. 1--12","author":"Niu Shuaicheng","year":"2023","unstructured":"Shuaicheng Niu, Jiaxiang Wu, Yifan Zhang, Zhiquan Wen, Yaofo Chen, Peilin Zhao, and Mingkui Tan. 2023. Towards stable test-time adaptation in dynamic wild world. In International conference on machine learning. 1--12."},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2021\/402"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-018-1072-8"},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.01059"},{"key":"e_1_3_2_1_32_1","volume-title":"Improving robustness against common corruptions by covariate shift adaptation. Advances in neural information processing systems","author":"Schneider Steffen","year":"2020","unstructured":"Steffen Schneider, Evgenia Rusak, Luisa Eck, Oliver Bringmann, Wieland Brendel, and Matthias Bethge. 2020. Improving robustness against common corruptions by covariate shift adaptation. Advances in neural information processing systems, Vol. 33 (2020), 11539--11551."},{"key":"e_1_3_2_1_33_1","first-page":"17543","article-title":"Revisiting realistic test-time training: Sequential inference and adaptation by anchored clustering","volume":"35","author":"Su Yongyi","year":"2022","unstructured":"Yongyi Su, Xun Xu, and Kui Jia. 2022. Revisiting realistic test-time training: Sequential inference and adaptation by anchored clustering. In Advances in Neural Information Processing Systems, Vol. 35. 17543--17555.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_34_1","volume-title":"International conference on machine learning. 9229--9248","author":"Sun Yu","year":"2020","unstructured":"Yu Sun, Xiaolong Wang, Zhuang Liu, John Miller, Alexei Efros, and Moritz Hardt. 2020. Test-time training with self-supervision for generalization under distribution shifts. In International conference on machine learning. 9229--9248."},{"key":"e_1_3_2_1_35_1","volume-title":"Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results. Advances in neural information processing systems","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)."},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.01948"},{"key":"e_1_3_2_1_37_1","volume-title":"International Conference On Learning Representations. 1--12","author":"Wang Dequan","year":"2021","unstructured":"Dequan Wang, Evan Shelhamer, Shaoteng Liu, Bruno Olshausen, and Trevor Darrell. 2021. Tent: Fully test-time adaptation by entropy minimization. In International Conference On Learning Representations. 1--12."},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00240"},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00706"},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01071"},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01261-8_1"},{"key":"e_1_3_2_1_42_1","first-page":"12077","article-title":"SegFormer: Simple and efficient design for semantic segmentation with transformers","volume":"34","author":"Xie Enze","year":"2021","unstructured":"Enze Xie, Wenhai Wang, Zhiding Yu, Anima Anandkumar, Jose M Alvarez, and Ping Luo. 2021. SegFormer: Simple and efficient design for semantic segmentation with transformers. Advances in Neural Information Processing Systems, Vol. 34 (2021), 12077--12090.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v38i15.29569"},{"key":"e_1_3_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-43898-1_49"},{"key":"e_1_3_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.01528"},{"key":"e_1_3_2_1_46_1","unstructured":"Yifan Zhang Bryan Hooi Lanqing Hong and Jiashi Feng. 2022. Self-supervised aggregation of diverse experts for test-agnostic long-tailed recognition. In Advances in Neural Information Processing Systems. 34077--34090."},{"key":"e_1_3_2_1_47_1","volume-title":"Deep long-tailed learning: A survey","author":"Zhang Yifan","year":"2023","unstructured":"Yifan Zhang, Bingyi Kang, Bryan Hooi, Shuicheng Yan, and Jiashi Feng. 2023. Deep long-tailed learning: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence (2023)."},{"key":"e_1_3_2_1_48_1","volume-title":"AdaNPC: Exploring Non-Parametric Classifier for Test-Time Adaptation. In International Conference on Machine Learning. 41647--41676","author":"Zhang Yifan","year":"2023","unstructured":"Yifan Zhang, Xue Wang, Kexin Jin, Kun Yuan, Zhang Zhang, Liang Wang, Rong Jin, and Tieniu Tan. 2023. AdaNPC: Exploring Non-Parametric Classifier for Test-Time Adaptation. In International Conference on Machine Learning. 41647--41676."},{"key":"e_1_3_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2020.3006377"},{"key":"e_1_3_2_1_50_1","unstructured":"Yifan Zhang Daquan Zhou Bryan Hooi Kai Wang and Jiashi Feng. 2023. Expanding small-scale datasets with guided imagination. In Advances in Neural Information Processing Systems."},{"key":"e_1_3_2_1_51_1","volume-title":"DELTA: DEGRADATION-FREE FULLY TEST-TIME ADAPTATION. In The Eleventh International Conference on Learning Representations.","author":"Zhao Bowen","year":"2023","unstructured":"Bowen Zhao, Chen Chen, and Shu-Tao Xia. 2023. DELTA: DEGRADATION-FREE FULLY TEST-TIME ADAPTATION. In The Eleventh International Conference on Learning Representations."},{"key":"e_1_3_2_1_52_1","volume-title":"International Conference on Machine Learning. 27378--27394","author":"Zhou Daquan","year":"2022","unstructured":"Daquan Zhou, Zhiding Yu, Enze Xie, Chaowei Xiao, Animashree Anandkumar, Jiashi Feng, and Jose M Alvarez. 2022. Understanding the robustness in vision transformers. In International Conference on Machine Learning. 27378--27394."}],"event":{"name":"MM '24: The 32nd ACM International Conference on Multimedia","location":"Melbourne VIC Australia","acronym":"MM '24","sponsor":["SIGMM ACM Special Interest Group on Multimedia"]},"container-title":["Proceedings of the 32nd ACM International Conference on Multimedia"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3664647.3680910","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3664647.3680910","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T01:17:33Z","timestamp":1750295853000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3664647.3680910"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,28]]},"references-count":52,"alternative-id":["10.1145\/3664647.3680910","10.1145\/3664647"],"URL":"https:\/\/doi.org\/10.1145\/3664647.3680910","relation":{},"subject":[],"published":{"date-parts":[[2024,10,28]]},"assertion":[{"value":"2024-10-28","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}