{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,8]],"date-time":"2026-02-08T16:15:34Z","timestamp":1770567334929,"version":"3.49.0"},"publisher-location":"New York, NY, USA","reference-count":76,"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":[{"name":"Fund for Foreign Scholars in University Research and Teaching Program?s 111 Project","award":["B07048"],"award-info":[{"award-number":["B07048"]}]},{"name":"Key Research and Development Program of Shannxi","award":["2021ZDLGY01-06, 2022ZDLGY01-12, 2023YBGY244, 2023QCYLL28, 2024GX-ZDCYL-02-08, 2024GX-ZDCYL-02-17"],"award-info":[{"award-number":["2021ZDLGY01-06, 2022ZDLGY01-12, 2023YBGY244, 2023QCYLL28, 2024GX-ZDCYL-02-08, 2024GX-ZDCYL-02-17"]}]},{"name":"Joint Funds of the National Natural Science Foundation of China","award":["U22B2054"],"award-info":[{"award-number":["U22B2054"]}]},{"name":"Program for Cheung Kong Scholars and Innovative Research Team in University","award":["IRT 15R53"],"award-info":[{"award-number":["IRT 15R53"]}]},{"DOI":"10.13039\/https:\/\/doi.org\/10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["2022M722496"],"award-info":[{"award-number":["2022M722496"]}],"id":[{"id":"10.13039\/https:\/\/doi.org\/10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Key Scientific Technological Innovation Research Project by Ministry of Education"},{"DOI":"10.13039\/https:\/\/doi.org\/10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2021ZD0110400, 2021ZD0110404"],"award-info":[{"award-number":["2021ZD0110400, 2021ZD0110404"]}],"id":[{"id":"10.13039\/https:\/\/doi.org\/10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/https:\/\/doi.org\/10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62271377, 62201407"],"award-info":[{"award-number":["62271377, 62201407"]}],"id":[{"id":"10.13039\/https:\/\/doi.org\/10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"State Key Program and the Foundation for Innovative Research Groups of the National Natural Science Foundation of China","award":["61836009"],"award-info":[{"award-number":["61836009"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,10,28]]},"DOI":"10.1145\/3664647.3680567","type":"proceedings-article","created":{"date-parts":[[2024,10,26]],"date-time":"2024-10-26T06:59:41Z","timestamp":1729925981000},"page":"5967-5976","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["Generalized Source-Free Domain-adaptive Segmentation via Reliable Knowledge Propagation"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-0846-7987","authenticated-orcid":false,"given":"Qi","family":"Zang","sequence":"first","affiliation":[{"name":"Xidian University, Xi'an, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4940-1211","authenticated-orcid":false,"given":"Shuang","family":"Wang","sequence":"additional","affiliation":[{"name":"Xidian University, Xi'an, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9880-8822","authenticated-orcid":false,"given":"Dong","family":"Zhao","sequence":"additional","affiliation":[{"name":"Xidian University, Xi'an, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-0564-9384","authenticated-orcid":false,"given":"Yang","family":"Hu","sequence":"additional","affiliation":[{"name":"Xidian University, Xi'an, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6943-4657","authenticated-orcid":false,"given":"Dou","family":"Quan","sequence":"additional","affiliation":[{"name":"Xidian University, Xi'an, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8746-4566","authenticated-orcid":false,"given":"Jinlong","family":"Li","sequence":"additional","affiliation":[{"name":"University of Trento, Trento, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6597-7248","authenticated-orcid":false,"given":"Nicu","family":"Sebe","sequence":"additional","affiliation":[{"name":"University of Trento, Trento, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8202-0544","authenticated-orcid":false,"given":"Zhun","family":"Zhong","sequence":"additional","affiliation":[{"name":"Hefei University of Technology, Hefei, China"}]}],"member":"320","published-online":{"date-parts":[[2024,10,28]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al.","author":"Achiam Josh","year":"2023","unstructured":"Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al. 2023. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023)."},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2019.2892405"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00816"},{"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","first-page":"15105","article-title":"Deliberated domain bridging for domain adaptive semantic segmentation","volume":"35","author":"Chen Lin","year":"2022","unstructured":"Lin Chen, Zhixiang Wei, Xin Jin, Huaian Chen, Miao Zheng, Kai Chen, and Yi Jin. 2022. Deliberated domain bridging for domain adaptive semantic segmentation. Advances in Neural Information Processing Systems, Vol. 35 (2022), 15105--15118.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/3581783.3611708"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00264"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.350"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.00207"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00707"},{"key":"e_1_3_2_1_11_1","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 9613--9623","author":"Francois","unstructured":"Francois Fleuret et al. 2021. Uncertainty reduction for model adaptation in semantic segmentation. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 9613--9623."},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/3474085.3475186"},{"key":"e_1_3_2_1_13_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_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00294"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2015.2389824"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00969"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00682"},{"key":"e_1_3_2_1_19_1","first-page":"3635","article-title":"Model adaptation: Historical contrastive learning for unsupervised domain adaptation without source data","volume":"34","author":"Huang Jiaxing","year":"2021","unstructured":"Jiaxing Huang, Dayan Guan, Aoran Xiao, and Shijian Lu. 2021. Model adaptation: Historical contrastive learning for unsupervised domain adaptation without source data. Advances in Neural Information Processing Systems, Vol. 34 (2021), 3635--3649.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_20_1","unstructured":"Max Jaderberg Karen Simonyan Andrew Zisserman et al. 2015. Spatial transformer networks. Advances in neural information processing systems Vol. 28 (2015)."},{"key":"e_1_3_2_1_21_1","first-page":"17173","article-title":"Variational model perturbation for source-free domain adaptation","volume":"35","author":"Jing Mengmeng","year":"2022","unstructured":"Mengmeng Jing, Xiantong Zhen, Jingjing Li, and Cees Snoek. 2022. Variational model perturbation for source-free domain adaptation. Advances in Neural Information Processing Systems, Vol. 35 (2022), 17173--17187.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1611835114"},{"key":"e_1_3_2_1_23_1","volume-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV). 7046--7056","author":"Kundu Jogendra Nath","unstructured":"Jogendra Nath Kundu, Akshay Kulkarni, Amit Singh, Varun Jampani, and R. Venkatesh Babu. 2021. Generalize Then Adapt: Source-Free Domain Adaptive Semantic Segmentation. In Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV). 7046--7056."},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00696"},{"key":"e_1_3_2_1_25_1","volume-title":"Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. 4544--4553","author":"Kundu Jogendra Nath","year":"2020","unstructured":"Jogendra Nath Kundu, Naveen Venkat, R Venkatesh Babu, et al. 2020. Universal source-free domain adaptation. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. 4544--4553."},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1145\/3581783.3612521"},{"key":"e_1_3_2_1_27_1","volume-title":"Adversarial Supervision Makes Layout-to-Image Diffusion Models Thrive. arXiv preprint arXiv:2401.08815","author":"Li Yumeng","year":"2024","unstructured":"Yumeng Li, Margret Keuper, Dan Zhang, and Anna Khoreva. 2024. Adversarial Supervision Makes Layout-to-Image Diffusion Models Thrive. arXiv preprint arXiv:2401.08815 (2024)."},{"key":"e_1_3_2_1_28_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_29_1","first-page":"8602","article-title":"Source data-absent unsupervised domain adaptation through hypothesis transfer and labeling transfer","volume":"44","author":"Liang Jian","year":"2021","unstructured":"Jian Liang, Dapeng Hu, Yunbo Wang, Ran He, and Jiashi Feng. 2021. Source data-absent unsupervised domain adaptation through hypothesis transfer and labeling transfer. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 44, 11 (2021), 8602--8617.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00127"},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-023-01863-1"},{"key":"e_1_3_2_1_33_1","volume-title":"Levine (Eds.)","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. In Advances in Neural Information Processing Systems,, A. Oh, T. Neumann, A. Globerson, K. Saenko, M. Hardt, and S. Levine (Eds.), Vol. 36. Curran Associates, Inc., 6690--6702. https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2023\/file\/157c30da6a988e1cbef2095f7b9521db-Paper-Conference.pdf"},{"key":"e_1_3_2_1_34_1","volume-title":"Advances in Neural Information Processing Systems","volume":"36","author":"Nguyen Quang","year":"2024","unstructured":"Quang Nguyen, Truong Vu, Anh Tran, and Khoi Nguyen. 2024. Dataset diffusion: Diffusion-based synthetic data generation for pixel-level semantic segmentation. Advances in Neural Information Processing Systems, Vol. 36 (2024)."},{"key":"e_1_3_2_1_35_1","volume-title":"International conference on machine learning. PMLR, 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. PMLR, 16888--16905."},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01269"},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00262"},{"key":"e_1_3_2_1_38_1","volume-title":"Proceedings, Part II 14","author":"Richter Stephan R","year":"2016","unstructured":"Stephan R Richter, Vibhav Vineet, Stefan Roth, and Vladlen Koltun. 2016. Playing for data: Ground truth from computer games. In Computer Vision--ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11--14, 2016, Proceedings, Part II 14. Springer, 102--118."},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01042"},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.352"},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.01059"},{"key":"e_1_3_2_1_42_1","first-page":"25278","article-title":"Laion-5b: An open large-scale dataset for training next generation image-text models","volume":"35","author":"Schuhmann Christoph","year":"2022","unstructured":"Christoph Schuhmann, Romain Beaumont, Richard Vencu, Cade Gordon, Ross Wightman, Mehdi Cherti, Theo Coombes, Aarush Katta, Clayton Mullis, Mitchell Wortsman, et al. 2022. Laion-5b: An open large-scale dataset for training next generation image-text models. Advances in Neural Information Processing Systems, Vol. 35 (2022), 25278--25294.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_43_1","volume-title":"So Kweon, and Sungha Choi.","author":"Song Junha","year":"2023","unstructured":"Junha Song, Jungsoo Lee, In So Kweon, and Sungha Choi. 2023. Ecotta: Memory-efficient continual test-time adaptation via self-distilled regularization. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 11920--11929."},{"key":"e_1_3_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1109\/WACV48630.2021.00142"},{"key":"e_1_3_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00262"},{"key":"e_1_3_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00706"},{"key":"e_1_3_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2023.3291876"},{"key":"e_1_3_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2022.3222634"},{"key":"e_1_3_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.02307"},{"key":"e_1_3_2_1_50_1","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.2308.03003"},{"key":"e_1_3_2_1_51_1","volume-title":"Mike Zheng Shou, and Chunhua Shen","author":"Wu Weijia","year":"2024","unstructured":"Weijia Wu, Yuzhong Zhao, Hao Chen, Yuchao Gu, Rui Zhao, Yefei He, Hong Zhou, Mike Zheng Shou, and Chunhua Shen. 2024. Datasetdm: Synthesizing data with perception annotations using diffusion models. Advances in Neural Information Processing Systems, Vol. 36 (2024)."},{"key":"e_1_3_2_1_52_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.00117"},{"key":"e_1_3_2_1_53_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_54_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.01370"},{"key":"e_1_3_2_1_55_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.00699"},{"key":"e_1_3_2_1_56_1","volume-title":"Freemask: Synthetic images with dense annotations make stronger segmentation models. arXiv preprint arXiv:2310.15160","author":"Yang Lihe","year":"2023","unstructured":"Lihe Yang, Xiaogang Xu, Bingyi Kang, Yinghuan Shi, and Hengshuang Zhao. 2023. Freemask: Synthetic images with dense annotations make stronger segmentation models. arXiv preprint arXiv:2310.15160 (2023)."},{"key":"e_1_3_2_1_57_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2024.110273"},{"key":"e_1_3_2_1_58_1","volume-title":"Transcending Fusion: A Multi-Scale Alignment Method for Remote Sensing Image-Text Retrieval. arXiv preprint arXiv:2405.18959","author":"Yang Rui","year":"2024","unstructured":"Rui Yang, Shuang Wang, Yingping Han, Yuanheng Li, Dong Zhao, Dou Quan, Yanhe Guo, and Licheng Jiao. 2024. Transcending Fusion: A Multi-Scale Alignment Method for Remote Sensing Image-Text Retrieval. arXiv preprint arXiv:2405.18959 (2024)."},{"key":"e_1_3_2_1_59_1","doi-asserted-by":"publisher","DOI":"10.1109\/JSTARS.2022.3194076"},{"key":"e_1_3_2_1_60_1","doi-asserted-by":"publisher","DOI":"10.1145\/3581783.3612207"},{"key":"e_1_3_2_1_61_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00885"},{"key":"e_1_3_2_1_62_1","doi-asserted-by":"publisher","DOI":"10.1145\/3474085.3475384"},{"key":"e_1_3_2_1_63_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.01991"},{"key":"e_1_3_2_1_64_1","doi-asserted-by":"publisher","DOI":"10.1145\/3474085.3475482"},{"key":"e_1_3_2_1_65_1","doi-asserted-by":"publisher","DOI":"10.1145\/3581783.3612071"},{"key":"e_1_3_2_1_66_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00612"},{"key":"e_1_3_2_1_67_1","doi-asserted-by":"publisher","DOI":"10.1109\/IGARSS46834.2022.9884217"},{"key":"e_1_3_2_1_68_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.00355"},{"key":"e_1_3_2_1_69_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01223"},{"key":"e_1_3_2_1_70_1","first-page":"5137","article-title":"Divide and contrast: Source-free domain adaptation via adaptive contrastive learning","volume":"35","author":"Zhang Ziyi","year":"2022","unstructured":"Ziyi Zhang, Weikai Chen, Hui Cheng, Zhen Li, Siyuan Li, Liang Lin, and Guanbin Li. 2022. Divide and contrast: Source-free domain adaptation via adaptive contrastive learning. Advances in Neural Information Processing Systems, Vol. 35 (2022), 5137--5149.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_71_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52733.2024.02210"},{"key":"e_1_3_2_1_72_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.01129"},{"key":"e_1_3_2_1_73_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.01758"},{"key":"e_1_3_2_1_74_1","volume-title":"Semantic Connectivity-Driven Pseudo-labeling for Cross-domain Segmentation. arXiv preprint arXiv:2312.06331","author":"Zhao Dong","year":"2023","unstructured":"Dong Zhao, Ruizhi Yang, Shuang Wang, Qi Zang, Yang Hu, Licheng Jiao, Nicu Sebe, and Zhun Zhong. 2023. Semantic Connectivity-Driven Pseudo-labeling for Cross-domain Segmentation. arXiv preprint arXiv:2312.06331 (2023)."},{"key":"e_1_3_2_1_75_1","unstructured":"Dong Zhao Qi Zang Zining Wang Dou Quan and Shuang Wang. 2022. SwinLS: Adapting Swin Transformer to Landslide Detection.. In CDCEO@ IJCAI. 91--95."},{"key":"e_1_3_2_1_76_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-19815-1_31"}],"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\/3664647.3680567","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3664647.3680567","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T00:03:45Z","timestamp":1750291425000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3664647.3680567"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,28]]},"references-count":76,"alternative-id":["10.1145\/3664647.3680567","10.1145\/3664647"],"URL":"https:\/\/doi.org\/10.1145\/3664647.3680567","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"}}]}}