{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T07:59:04Z","timestamp":1774511944175,"version":"3.50.1"},"reference-count":44,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"3","license":[{"start":{"date-parts":[[2025,3,1]],"date-time":"2025-03-01T00:00:00Z","timestamp":1740787200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2025,3,1]],"date-time":"2025-03-01T00:00:00Z","timestamp":1740787200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,3,1]],"date-time":"2025-03-01T00:00:00Z","timestamp":1740787200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2022YFC3302200"],"award-info":[{"award-number":["2022YFC3302200"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62471207"],"award-info":[{"award-number":["62471207"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62276146"],"award-info":[{"award-number":["62276146"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003392","name":"Natural Science Foundation of Fujian Province","doi-asserted-by":"publisher","award":["2024J02029"],"award-info":[{"award-number":["2024J02029"]}],"id":[{"id":"10.13039\/501100003392","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100013066","name":"Key Project of Colleges and Universities of Henan Province","doi-asserted-by":"publisher","award":["23A52002"],"award-info":[{"award-number":["23A52002"]}],"id":[{"id":"10.13039\/501100013066","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Circuits Syst. Video Technol."],"published-print":{"date-parts":[[2025,3]]},"DOI":"10.1109\/tcsvt.2024.3490597","type":"journal-article","created":{"date-parts":[[2024,11,4]],"date-time":"2024-11-04T18:40:24Z","timestamp":1730745624000},"page":"2603-2615","source":"Crossref","is-referenced-by-count":3,"title":["SIAVC: Semi-Supervised Framework for Industrial Accident Video Classification"],"prefix":"10.1109","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0952-9915","authenticated-orcid":false,"given":"Zuoyong","family":"Li","sequence":"first","affiliation":[{"name":"Fujian Provincial Key Laboratory of Information Processing and Intelligent Control, School of Computer and Big Data, Minjiang University, Fuzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-5105-3227","authenticated-orcid":false,"given":"Qinghua","family":"Lin","sequence":"additional","affiliation":[{"name":"School of Computer Science and Mathematics, Fujian University of Technology, Fuzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9428-7812","authenticated-orcid":false,"given":"Haoyi","family":"Fan","sequence":"additional","affiliation":[{"name":"School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7497-8883","authenticated-orcid":false,"given":"Tiesong","family":"Zhao","sequence":"additional","affiliation":[{"name":"Fujian Key Laboratory for Intelligent Processing and Wireless Transmission of Media Information, Fuzhou University, Fuzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5027-5286","authenticated-orcid":false,"given":"David","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Data Science, The Chinese University of Hong Kong (Shenzhen), Shenzhen, China"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2015.2392531"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2024.123409"},{"key":"ref3","first-page":"10078","article-title":"VideoMae: Masked autoencoders are data-efficient learners for self-supervised video pre-training","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"35","author":"Tong"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/ICCVW54120.2021.00355"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00320"},{"key":"ref6","first-page":"596","article-title":"FixMatch: Simplifying semi-supervised learning with consistency and confidence","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"33","author":"Sohn"},{"key":"ref7","article-title":"Revisiting deep semi-supervised learning: An empirical distribution alignment framework and its generalization bound","author":"Wang","year":"2022","journal-title":"arXiv:2203.06639"},{"issue":"2","key":"ref8","first-page":"896","article-title":"Pseudo-Label: The simple and efficient semi-supervised learning method for deep neural networks","volume-title":"Proc. ICMLW","volume":"3","author":"Lee"},{"key":"ref9","first-page":"1","article-title":"In defense of pseudo-labeling: An uncertainty-aware pseudo-label selection frame work for semi-supervised learning","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Rizve"},{"key":"ref10","first-page":"5050","article-title":"MixMatch: A holistic approach to semi-supervised learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"32","author":"Berthelot"},{"key":"ref11","first-page":"18408","article-title":"FlexMatch: Boosting semi-supervised learning with curriculum pseudo labeling","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Zhang"},{"key":"ref12","article-title":"AutoAugment: Learning augmentation policies from data","author":"Cubuk","year":"2018","journal-title":"arXiv:1805.09501"},{"key":"ref13","article-title":"ReMixMatch: Semi-supervised learning with distribution alignment and augmentation anchoring","author":"Berthelot","year":"2019","journal-title":"arXiv:1911.09785"},{"key":"ref14","article-title":"VideoMix: Rethinking data augmentation for video classification","author":"Yun","year":"2020","journal-title":"arXiv:2012.03457"},{"key":"ref15","first-page":"1","article-title":"FreeMatch: Self-adaptive thresholding for semi-supervised learning","volume-title":"Proc. Int. Conf. Learn. Represent. (ICLR)","author":"Wang"},{"key":"ref16","article-title":"SoftMatch: Addressing the quantity-quality trade-off in semi-supervised learning","author":"Chen","year":"2023","journal-title":"arXiv:2301.10921"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01407"},{"key":"ref18","article-title":"SelfMatch: Combining contrastive self-supervision and consistency for semi-supervised learning","author":"Kim","year":"2021","journal-title":"arXiv:2101.06480"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.01455"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/TSMC.1979.4310076"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/TNSE.2021.3139671"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2023.3278310"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2022.3204753"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00565"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2017.2786999"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01063"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/WACV48630.2021.00115"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00476"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i3.25386"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2023.3262754"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.1706.03762"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW50498.2020.00359"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.01455"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00155"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-11758-4_52"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2011.2157190"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/SBR-LARS-R.2017.8215312"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1049\/ipr2.12258"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2014.2339592"},{"key":"ref40","article-title":"FireNet: A specialized lightweight fire & smoke detection model for real-time IoT applications","author":"Jadon","year":"2019","journal-title":"arXiv:1905.11922"},{"key":"ref41","article-title":"SGDR: Stochastic gradient descent with warm restarts","author":"Loshchilov","year":"2016","journal-title":"arXiv:1608.03983"},{"key":"ref42","first-page":"1139","article-title":"On the importance of initialization and momentum in deep learning","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Sutskever"},{"key":"ref43","first-page":"1","article-title":"Temporal ensembling for semi-supervised learning","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Laine"},{"key":"ref44","first-page":"1195","article-title":"Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"30","author":"Tarvainen"}],"container-title":["IEEE Transactions on Circuits and Systems for Video Technology"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/76\/10916540\/10741535.pdf?arnumber=10741535","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,7]],"date-time":"2025-03-07T18:52:36Z","timestamp":1741373556000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10741535\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3]]},"references-count":44,"journal-issue":{"issue":"3"},"URL":"https:\/\/doi.org\/10.1109\/tcsvt.2024.3490597","relation":{},"ISSN":["1051-8215","1558-2205"],"issn-type":[{"value":"1051-8215","type":"print"},{"value":"1558-2205","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,3]]}}}