{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,14]],"date-time":"2026-03-14T18:04:15Z","timestamp":1773511455659,"version":"3.50.1"},"reference-count":74,"publisher":"Association for Computing Machinery (ACM)","issue":"3","license":[{"start":{"date-parts":[[2024,4,9]],"date-time":"2024-04-09T00:00:00Z","timestamp":1712620800000},"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":"crossref","award":["62372091, 61872405, 61720106004"],"award-info":[{"award-number":["62372091, 61872405, 61720106004"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Sichuan Science and Technology Program of China","award":["2023NSFSC0462"],"award-info":[{"award-number":["2023NSFSC0462"]}]},{"name":"\u201c111\u201d","award":["B17008"],"award-info":[{"award-number":["B17008"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Graph."],"published-print":{"date-parts":[[2024,6,30]]},"abstract":"<jats:p>\n            Supervised homography estimation methods face a challenge due to the lack of adequate labeled training data. To address this issue, we propose\n            <jats:italic>DMHomo<\/jats:italic>\n            , a diffusion model-based framework for supervised homography learning. This framework generates image pairs with accurate labels, realistic image content, and realistic interval motion, ensuring that they satisfy adequate pairs. We utilize unlabeled image pairs with pseudo labels such as homography and dominant plane masks, computed from existing methods, to train a diffusion model that generates a supervised training dataset. To further enhance performance, we introduce a new probabilistic mask loss, which identifies outlier regions through supervised training, and an iterative mechanism to optimize the generative and homography models successively. Our experimental results demonstrate that DMHomo effectively overcomes the scarcity of qualified datasets in supervised homography learning and improves generalization to real-world scenes. The code and dataset are available at GitHub (\n            <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/github.com\/lhaippp\/DMHomo\">https:\/\/github.com\/lhaippp\/DMHomo<\/jats:ext-link>\n            ).\n          <\/jats:p>","DOI":"10.1145\/3652207","type":"journal-article","created":{"date-parts":[[2024,3,11]],"date-time":"2024-03-11T12:17:55Z","timestamp":1710159475000},"page":"1-16","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":16,"title":["DMHomo: Learning Homography with Diffusion Models"],"prefix":"10.1145","volume":"43","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3983-9287","authenticated-orcid":false,"given":"Haipeng","family":"Li","sequence":"first","affiliation":[{"name":"University of Electronic Science and Technology of China, Chengdu, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7087-6775","authenticated-orcid":false,"given":"Hai","family":"Jiang","sequence":"additional","affiliation":[{"name":"Sichuan University, Chengdu, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3494-8062","authenticated-orcid":false,"given":"Ao","family":"Luo","sequence":"additional","affiliation":[{"name":"Megvii Technology, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4506-6973","authenticated-orcid":false,"given":"Ping","family":"Tan","sequence":"additional","affiliation":[{"name":"The Hong Kong University of Science and Technology, Hongkong, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7398-6873","authenticated-orcid":false,"given":"Haoqiang","family":"Fan","sequence":"additional","affiliation":[{"name":"Megvii Technology, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4491-7967","authenticated-orcid":false,"given":"Bing","family":"Zeng","sequence":"additional","affiliation":[{"name":"University of Electronic Science and Technology of China, Chengdu, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8815-5335","authenticated-orcid":false,"given":"Shuaicheng","family":"Liu","sequence":"additional","affiliation":[{"name":"University of Electronic Science and Technology of China, Chengdu, China"}]}],"member":"320","published-online":{"date-parts":[[2024,4,9]]},"reference":[{"key":"e_1_3_2_2_1","first-page":"5173","volume-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","author":"Balntas Vassileios","unstructured":"Vassileios Balntas, Karel Lenc, Andrea Vedaldi, and Krystian Mikolajczyk. 2017. HPatches: A benchmark and evaluation of handcrafted and learned local descriptors. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 5173\u20135182."},{"key":"e_1_3_2_3_1","article-title":"Analytic-DPM: An analytic estimate of the optimal reverse variance in diffusion probabilistic models","author":"Bao Fan","year":"2022","unstructured":"Fan Bao, Chongxuan Li, Jun Zhu, and Bo Zhang. 2022. Analytic-DPM: An analytic estimate of the optimal reverse variance in diffusion probabilistic models. arXiv preprint arXiv:2201.06503 (2022).","journal-title":"arXiv preprint arXiv:2201.06503"},{"key":"e_1_3_2_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.01044"},{"key":"e_1_3_2_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00138"},{"key":"e_1_3_2_6_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-33783-3_44"},{"key":"e_1_3_2_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00192"},{"key":"e_1_3_2_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.402"},{"key":"e_1_3_2_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/3459665"},{"key":"e_1_3_2_10_1","article-title":"Deep image homography estimation","author":"DeTone Daniel","year":"2016","unstructured":"Daniel DeTone, Tomasz Malisiewicz, and Andrew Rabinovich. 2016. Deep image homography estimation. arXiv preprint arXiv:1606.03798 (2016).","journal-title":"arXiv preprint arXiv:1606.03798"},{"key":"e_1_3_2_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW.2018.00060"},{"key":"e_1_3_2_12_1","first-page":"8780","article-title":"Diffusion models beat GANs on image synthesis","volume":"34","author":"Dhariwal Prafulla","year":"2021","unstructured":"Prafulla Dhariwal and Alexander Nichol. 2021. Diffusion models beat GANs on image synthesis. Advances in Neural Information Processing Systems 34 (2021), 8780\u20138794.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00612"},{"key":"e_1_3_2_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.316"},{"key":"e_1_3_2_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/358669.358692"},{"key":"e_1_3_2_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00355"},{"key":"e_1_3_2_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2012.6248074"},{"key":"e_1_3_2_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00373"},{"key":"e_1_3_2_19_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-19800-7_17"},{"key":"e_1_3_2_20_1","doi-asserted-by":"publisher","DOI":"10.5555\/861369"},{"key":"e_1_3_2_21_1","first-page":"6840","article-title":"Denoising diffusion probabilistic models","volume":"33","author":"Ho Jonathan","year":"2020","unstructured":"Jonathan Ho, Ajay Jain, and Pieter Abbeel. 2020. Denoising diffusion probabilistic models. Advances in Neural Information Processing Systems 33 (2020), 6840\u20136851.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_22_1","article-title":"Classifier-free diffusion guidance","author":"Ho Jonathan","year":"2022","unstructured":"Jonathan Ho and Tim Salimans. 2022. Classifier-free diffusion guidance. arXiv preprint arXiv:2207.12598 (2022).","journal-title":"arXiv preprint arXiv:2207.12598"},{"key":"e_1_3_2_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01714"},{"key":"e_1_3_2_24_1","article-title":"Simple diffusion: End-to-end diffusion for high resolution images","author":"Hoogeboom Emiel","year":"2023","unstructured":"Emiel Hoogeboom, Jonathan Heek, and Tim Salimans. 2023. Simple diffusion: End-to-end diffusion for high resolution images. arXiv preprint arXiv:2301.11093 (2023).","journal-title":"arXiv preprint arXiv:2301.11093"},{"key":"e_1_3_2_25_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01234-2_40"},{"key":"e_1_3_2_26_1","article-title":"Semi-supervised deep large-baseline homography estimation with progressive equivalence constraint","author":"Jiang Hai","year":"2022","unstructured":"Hai Jiang, Haipeng Li, Yuhang Lu, Songchen Han, and Shuaicheng Liu. 2022. Semi-supervised deep large-baseline homography estimation with progressive equivalence constraint. arXiv preprint arXiv:2212.02763 (2022).","journal-title":"arXiv preprint arXiv:2212.02763"},{"key":"e_1_3_2_27_1","article-title":"Elucidating the design space of diffusion-based generative models","author":"Karras Tero","year":"2022","unstructured":"Tero Karras, Miika Aittala, Timo Aila, and Samuli Laine. 2022. Elucidating the design space of diffusion-based generative models. arXiv preprint arXiv:2206.00364 (2022).","journal-title":"arXiv preprint arXiv:2206.00364"},{"key":"e_1_3_2_28_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-65414-6_28"},{"key":"e_1_3_2_29_1","article-title":"Adam: A method for stochastic optimization","author":"Kingma Diederik P.","year":"2014","unstructured":"Diederik P. Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014).","journal-title":"arXiv preprint arXiv:1412.6980"},{"key":"e_1_3_2_30_1","article-title":"Variational dropout and the local reparameterization trick","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 28 (2015), 1\u20139.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_31_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00767"},{"key":"e_1_3_2_32_1","first-page":"12869","volume-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","author":"Li Haipeng","unstructured":"Haipeng Li, Kunming Luo, and Shuaicheng Liu. 2021. GyroFlow: Gyroscope-guided unsupervised optical flow learning. In Proceedings of the IEEE\/CVF International Conference on Computer Vision. 12869\u201312878."},{"key":"e_1_3_2_33_1","article-title":"GyroFlow+: Gyroscope-guided unsupervised deep homography and optical flow learning","author":"Li Haipeng","year":"2023","unstructured":"Haipeng Li, Kunming Luo, Bing Zeng, and Shuaicheng Liu. 2023. GyroFlow+: Gyroscope-guided unsupervised deep homography and optical flow learning. arXiv preprint arXiv:2301.10018 (2023).","journal-title":"arXiv preprint arXiv:2301.10018"},{"key":"e_1_3_2_34_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00218"},{"key":"e_1_3_2_35_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"e_1_3_2_36_1","article-title":"DeepOIS: Gyroscope-guided deep optical image stabilizer compensation","volume":"9","author":"Liu Shuaicheng","year":"2021","unstructured":"Shuaicheng Liu, Haipeng Li, Zhengning Wang, Jue Wang, Shuyuan Zhu, and Bing Zeng. 2021a. DeepOIS: Gyroscope-guided deep optical image stabilizer compensation. IEEE Transactions on Circuits and Systems for Video Technology. Published Online, August 9, 2021. DOI: 10.1109\/TCSVT.2021.3103281","journal-title":"IEEE Transactions on Circuits and Systems for Video Technology."},{"key":"e_1_3_2_37_1","article-title":"Unsupervised global and local homography estimation with motion basis learning","author":"Liu Shuaicheng","year":"2022","unstructured":"Shuaicheng Liu, Yuhang Lu, Hai Jiang, Nianjin Ye, Chuan Wang, and Bing Zeng. 2022. Unsupervised global and local homography estimation with motion basis learning. IEEE Transactions on Pattern Analysis and Machine Intelligence. Published Online, November 21, 2022.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence."},{"issue":"3","key":"e_1_3_2_38_1","first-page":"2849","article-title":"Content-aware unsupervised deep homography estimation and its extensions","volume":"45","author":"Liu Shuaicheng","year":"2023","unstructured":"Shuaicheng Liu, Nianjin Ye, Chuan Wang, Kunming Luo, Jue Wang, and Jian Sun. 2023b. Content-aware unsupervised deep homography estimation and its extensions. IEEE Transactions on Pattern Analysis and Machine Intelligence 45, 3 (2023), 2849\u20132863.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"e_1_3_2_39_1","doi-asserted-by":"publisher","DOI":"10.1145\/3450626.3459768"},{"key":"e_1_3_2_40_1","doi-asserted-by":"publisher","DOI":"10.1109\/WACV56688.2023.00037"},{"key":"e_1_3_2_41_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW53098.2021.00057"},{"key":"e_1_3_2_42_1","doi-asserted-by":"publisher","DOI":"10.1023\/B:VISI.0000029664.99615.94"},{"key":"e_1_3_2_43_1","article-title":"DPM-Solver: A fast ODE solver for diffusion probabilistic model sampling in around 10 steps","author":"Lu Cheng","year":"2022","unstructured":"Cheng Lu, Yuhao Zhou, Fan Bao, Jianfei Chen, Chongxuan Li, and Jun Zhu. 2022. DPM-Solver: A fast ODE solver for diffusion probabilistic model sampling in around 10 steps. arXiv preprint arXiv:2206.00927 (2022).","journal-title":"arXiv preprint arXiv:2206.00927"},{"key":"e_1_3_2_44_1","article-title":"Image restoration with mean-reverting stochastic differential equations","author":"Luo Ziwei","year":"2023","unstructured":"Ziwei Luo, Fredrik K. Gustafsson, Zheng Zhao, Jens Sj\u00f6lund, and Thomas B. Sch\u00f6n. 2023. Image restoration with mean-reverting stochastic differential equations. arXiv preprint arXiv:2301.11699 (2023).","journal-title":"arXiv preprint arXiv:2301.11699"},{"key":"e_1_3_2_45_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298925"},{"key":"e_1_3_2_46_1","doi-asserted-by":"publisher","DOI":"10.1145\/3503250"},{"key":"e_1_3_2_47_1","doi-asserted-by":"publisher","DOI":"10.1109\/TRO.2015.2463671"},{"key":"e_1_3_2_48_1","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2018.2809549"},{"key":"e_1_3_2_49_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00581"},{"key":"e_1_3_2_50_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01018"},{"key":"e_1_3_2_51_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.01215"},{"key":"e_1_3_2_52_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01042"},{"key":"e_1_3_2_53_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2011.6126544"},{"key":"e_1_3_2_54_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2022.3204461"},{"key":"e_1_3_2_55_1","article-title":"Progressive distillation for fast sampling of diffusion models","author":"Salimans Tim","year":"2022","unstructured":"Tim Salimans and Jonathan Ho. 2022. Progressive distillation for fast sampling of diffusion models. arXiv preprint arXiv:2202.00512 (2022).","journal-title":"arXiv preprint arXiv:2202.00512"},{"key":"e_1_3_2_56_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00499"},{"key":"e_1_3_2_57_1","volume-title":"Proceedings of the Vicomor Workshop at IROS","volume":"2012","author":"Saurer Olivier","year":"2012","unstructured":"Olivier Saurer, Friedrich Fraundorfer, and Marc Pollefeys. 2012. Homography based visual odometry with known vertical direction and weak Manhattan world assumption. In Proceedings of the Vicomor Workshop at IROS, Vol. 2012."},{"key":"e_1_3_2_58_1","first-page":"14890","volume-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","author":"Shao Ruizhi","unstructured":"Ruizhi Shao, Gaochang Wu, Yuemei Zhou, Ying Fu, Lu Fang, and Yebin Liu. 2021. LocalTrans: A multiscale local transformer network for cross-resolution homography estimation. In Proceedings of the IEEE\/CVF International Conference on Computer Vision. 14890\u201314899."},{"key":"e_1_3_2_59_1","first-page":"2256","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Sohl-Dickstein Jascha","year":"2015","unstructured":"Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli. 2015. Deep unsupervised learning using nonequilibrium thermodynamics. In Proceedings of the International Conference on Machine Learning. 2256\u20132265."},{"key":"e_1_3_2_60_1","article-title":"Denoising diffusion implicit models","author":"Song Jiaming","year":"2020","unstructured":"Jiaming Song, Chenlin Meng, and Stefano Ermon. 2020a. Denoising diffusion implicit models. arXiv preprint arXiv:2010.02502 (2020).","journal-title":"arXiv preprint arXiv:2010.02502"},{"key":"e_1_3_2_61_1","article-title":"Generative modeling by estimating gradients of the data distribution","volume":"32","author":"Song Yang","year":"2019","unstructured":"Yang Song and Stefano Ermon. 2019. Generative modeling by estimating gradients of the data distribution. Advances in Neural Information Processing Systems 32 (2019), 1\u201313.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_62_1","article-title":"Solving inverse problems in medical imaging with score-based generative models","author":"Song Yang","year":"2021","unstructured":"Yang Song, Liyue Shen, Lei Xing, and Stefano Ermon. 2021. Solving inverse problems in medical imaging with score-based generative models. arXiv preprint arXiv:2111.08005 (2021).","journal-title":"arXiv preprint arXiv:2111.08005"},{"key":"e_1_3_2_63_1","article-title":"Score-based generative modeling through stochastic differential equations","author":"Song Yang","year":"2020","unstructured":"Yang Song, Jascha Sohl-Dickstein, Diederik P. Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole. 2020b. Score-based generative modeling through stochastic differential equations. arXiv preprint arXiv:2011.13456 (2020).","journal-title":"arXiv preprint arXiv:2011.13456"},{"key":"e_1_3_2_64_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00881"},{"key":"e_1_3_2_65_1","article-title":"Human motion diffusion model","author":"Tevet Guy","year":"2022","unstructured":"Guy Tevet, Sigal Raab, Brian Gordon, Yonatan Shafir, Daniel Cohen-Or, and Amit H. Bermano. 2022. Human motion diffusion model. arXiv preprint arXiv:2209.14916 (2022).","journal-title":"arXiv preprint arXiv:2209.14916"},{"key":"e_1_3_2_66_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.01127"},{"key":"e_1_3_2_67_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00629"},{"key":"e_1_3_2_68_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00566"},{"key":"e_1_3_2_69_1","article-title":"Attention is all you need","volume":"30","author":"Vaswani Ashish","year":"2017","unstructured":"Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, \u0141ukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. Advances in Neural Information Processing Systems 30 (2017), 1\u201311.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_70_1","article-title":"Zero-shot image restoration using denoising diffusion null-space model","author":"Wang Yinhuai","year":"2022","unstructured":"Yinhuai Wang, Jiwen Yu, and Jian Zhang. 2022. Zero-shot image restoration using denoising diffusion null-space model. arXiv preprint arXiv:2212.00490 (2022).","journal-title":"arXiv preprint arXiv:2212.00490"},{"key":"e_1_3_2_71_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01216-8_8"},{"key":"e_1_3_2_72_1","first-page":"13117","volume-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","author":"Ye Nianjin","unstructured":"Nianjin Ye, Chuan Wang, Haoqiang Fan, and Shuaicheng Liu. 2021. Motion basis learning for unsupervised deep homography estimation with subspace projection. In Proceedings of the IEEE\/CVF International Conference on Computer Vision. 13117\u201313125."},{"key":"e_1_3_2_73_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46466-4_28"},{"key":"e_1_3_2_74_1","doi-asserted-by":"crossref","unstructured":"Jason J. Yu Adam W. Harley and Konstantinos G. Derpanis. 2016. Back to basics: Unsupervised learning of optical flow via brightness constancy and motion smoothness. In Computer Vision\u2014ECCV 2016 Workshops. Lecture Notes in Computer Science Vol. 9915. Springer 3\u201310.","DOI":"10.1007\/978-3-319-49409-8_1"},{"key":"e_1_3_2_75_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58452-8_38"}],"container-title":["ACM Transactions on Graphics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3652207","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3652207","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T22:49:15Z","timestamp":1750286955000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3652207"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,9]]},"references-count":74,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2024,6,30]]}},"alternative-id":["10.1145\/3652207"],"URL":"https:\/\/doi.org\/10.1145\/3652207","relation":{},"ISSN":["0730-0301","1557-7368"],"issn-type":[{"value":"0730-0301","type":"print"},{"value":"1557-7368","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,4,9]]},"assertion":[{"value":"2023-10-11","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-02-27","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-04-09","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}