{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T04:08:43Z","timestamp":1750219723472,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":26,"publisher":"ACM","license":[{"start":{"date-parts":[[2023,10,29]],"date-time":"2023-10-29T00:00:00Z","timestamp":1698537600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62171114, 52222810"],"award-info":[{"award-number":["62171114, 52222810"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Fundamental Research Funds for the Central Universities","award":["DUT22RC(3)099"],"award-info":[{"award-number":["DUT22RC(3)099"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,10,29]]},"DOI":"10.1145\/3606042.3616453","type":"proceedings-article","created":{"date-parts":[[2023,10,20]],"date-time":"2023-10-20T10:09:04Z","timestamp":1697796544000},"page":"13-18","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Adapting Segment Anything Model for Shield Tunnel Water Leakage Segmentation"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5499-1274","authenticated-orcid":false,"given":"Shichang","family":"Liu","sequence":"first","affiliation":[{"name":"Sichuan University, Chengdu, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4745-8361","authenticated-orcid":false,"given":"Junxin","family":"Chen","sequence":"additional","affiliation":[{"name":"Dalian University of Technology, Dalian, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7761-1748","authenticated-orcid":false,"given":"Ben-Guo","family":"He","sequence":"additional","affiliation":[{"name":"Northeastern University, Shenyang, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-6258-902X","authenticated-orcid":false,"given":"Tao","family":"Chen","sequence":"additional","affiliation":[{"name":"Huaneng Luding Hydropower Co., Ltd.,, Chengdu, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0651-4278","authenticated-orcid":false,"given":"Gwanggil","family":"Jeon","sequence":"additional","affiliation":[{"name":"Incheon National University, Incheon, South Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1717-5785","authenticated-orcid":false,"given":"Wei","family":"Wang","sequence":"additional","affiliation":[{"name":"Shenzhen MSU-BIT University, Shenzhen, Guangdong, China"}]}],"member":"320","published-online":{"date-parts":[[2023,10,29]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Rethinking atrous convolution for semantic image segmentation. arXiv preprint arXiv:1706.05587","author":"Chen Liang-Chieh","year":"2017","unstructured":"Liang-Chieh Chen, George Papandreou, Florian Schroff, and Hartwig Adam. 2017. Rethinking atrous convolution for semantic image segmentation. arXiv preprint arXiv:1706.05587 (2017)."},{"key":"e_1_3_2_1_2_1","volume-title":"SAM fails to segment anything?--SAM-Adapter: Adapting SAM in underperformed scenes: camouflage, shadow, and more. arXiv preprint arXiv:2304.09148","author":"Chen Tianrun","year":"2023","unstructured":"Tianrun Chen, Lanyun Zhu, Chaotao Ding, Runlong Cao, Shangzhan Zhang, Yan Wang, Zejian Li, Lingyun Sun, Papa Mao, and Ying Zang. 2023. SAM fails to segment anything?--SAM-Adapter: Adapting SAM in underperformed scenes: camouflage, shadow, and more. arXiv preprint arXiv:2304.09148 (2023)."},{"key":"e_1_3_2_1_3_1","volume-title":"Vision transformer adapter for dense predictions. arXiv preprint arXiv:2205.08534","author":"Chen Zhe","year":"2022","unstructured":"Zhe Chen, Yuchen Duan, Wenhai Wang, Junjun He, Tong Lu, Jifeng Dai, and Yu Qiao. 2022. Vision transformer adapter for dense predictions. arXiv preprint arXiv:2205.08534 (2022)."},{"key":"e_1_3_2_1_4_1","unstructured":"Alexey Dosovitskiy Lucas Beyer Alexander Kolesnikov Dirk Weissenborn Xiaohua Zhai Thomas Unterthiner Mostafa Dehghani Matthias Minderer Georg Heigold Sylvain Gelly et al. 2020. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020)."},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_3_2_1_6_1","volume-title":"Gaussian error linear units (gelus). arXiv preprint arXiv:1606.08415","author":"Hendrycks Dan","year":"2016","unstructured":"Dan Hendrycks and Kevin Gimpel. 2016. Gaussian error linear units (gelus). arXiv preprint arXiv:1606.08415 (2016)."},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.tust.2018.04.002"},{"key":"e_1_3_2_1_8_1","volume-title":"2023 a. SAM struggles in concealed scenes--Empirical study on \"Segment Anything\". arXiv preprint arXiv:2304.06022","author":"Ji Ge-Peng","year":"2023","unstructured":"Ge-Peng Ji, Deng-Ping Fan, Peng Xu, Ming-Ming Cheng, Bowen Zhou, and Luc Van Gool. 2023 a. SAM struggles in concealed scenes--Empirical study on \"Segment Anything\". arXiv preprint arXiv:2304.06022 (2023)."},{"key":"e_1_3_2_1_9_1","volume-title":"2023 b. Segment anything is not always perfect: An investigation of sam on different real-world applications. arXiv preprint arXiv:2304.05750","author":"Ji Wei","year":"2023","unstructured":"Wei Ji, Jingjing Li, Qi Bi, Wenbo Li, and Li Cheng. 2023 b. Segment anything is not always perfect: An investigation of sam on different real-world applications. arXiv preprint arXiv:2304.05750 (2023)."},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"crossref","unstructured":"Alexander Kirillov Eric Mintun Nikhila Ravi Hanzi Mao Chloe Rolland Laura Gustafson Tete Xiao Spencer Whitehead Alexander C Berg Wan-Yen Lo et al. 2023. Segment anything. arXiv preprint arXiv:2304.02643 (2023).","DOI":"10.1109\/ICCV51070.2023.00371"},{"key":"e_1_3_2_1_11_1","volume-title":"International Conference on Maintenance Engineering. Springer, 81--90","author":"Li Chun","year":"2020","unstructured":"Chun Li, Weibiao Chen, Rongfeng Deng, and Qi Han. 2020a. Overview of tunnel detection technology. In International Conference on Maintenance Engineering. Springer, 81--90."},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1088\/1755-1315\/455\/1\/012154"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.01862"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"e_1_3_2_1_15_1","volume-title":"Segment anything in medical images. arXiv preprint arXiv:2304.12306","author":"Ma Jun","year":"2023","unstructured":"Jun Ma and Bo Wang. 2023. Segment anything in medical images. arXiv preprint arXiv:2304.12306 (2023)."},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"e_1_3_2_1_17_1","volume-title":"International Conference on Machine Learning. PMLR, 6105--6114","author":"Tan Mingxing","year":"2019","unstructured":"Mingxing Tan and Quoc Le. 2019. Efficientnet: Rethinking model scaling for convolutional neural networks. In International Conference on Machine Learning. PMLR, 6105--6114."},{"key":"e_1_3_2_1_18_1","volume-title":"Can sam segment anything? when sam meets camouflaged object detection. arXiv preprint arXiv:2304.04709","author":"Tang Lv","year":"2023","unstructured":"Lv Tang, Haoke Xiao, and Bo Li. 2023. Can sam segment anything? when sam meets camouflaged object detection. arXiv preprint arXiv:2304.04709 (2023)."},{"key":"e_1_3_2_1_19_1","volume-title":"Medical SAM Adapter: Adapting segment anything model for medical image segmentation. arXiv preprint arXiv:2304.12620","author":"Wu Junde","year":"2023","unstructured":"Junde Wu, Rao Fu, Huihui Fang, Yuanpei Liu, Zhaowei Wang, Yanwu Xu, Yueming Jin, and Tal Arbel. 2023. Medical SAM Adapter: Adapting segment anything model for medical image segmentation. arXiv preprint arXiv:2304.12620 (2023)."},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01228-1_26"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jvcir.2019.102708"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.measurement.2021.109316"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.tust.2020.103524"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1111\/mice.12367"},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.tust.2019.103156"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1111\/mice.12836"}],"event":{"name":"MM '23: The 31st ACM International Conference on Multimedia","sponsor":["SIGMM ACM Special Interest Group on Multimedia"],"location":"Ottawa ON Canada","acronym":"MM '23"},"container-title":["Proceedings of the 2023 Workshop on Advanced Multimedia Computing for Smart Manufacturing and Engineering"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3606042.3616453","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3606042.3616453","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T16:36:20Z","timestamp":1750178180000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3606042.3616453"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,29]]},"references-count":26,"alternative-id":["10.1145\/3606042.3616453","10.1145\/3606042"],"URL":"https:\/\/doi.org\/10.1145\/3606042.3616453","relation":{},"subject":[],"published":{"date-parts":[[2023,10,29]]},"assertion":[{"value":"2023-10-29","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}