{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T22:47:42Z","timestamp":1780354062946,"version":"3.54.1"},"reference-count":51,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"12","license":[{"start":{"date-parts":[[2023,12,1]],"date-time":"2023-12-01T00:00:00Z","timestamp":1701388800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2023,12,1]],"date-time":"2023-12-01T00:00:00Z","timestamp":1701388800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,12,1]],"date-time":"2023-12-01T00:00:00Z","timestamp":1701388800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62076179"],"award-info":[{"award-number":["62076179"]}],"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":["61925602"],"award-info":[{"award-number":["61925602"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Haihe Laboratory of Information Technology Application Innovation","award":["22HHXCJC00002"],"award-info":[{"award-number":["22HHXCJC00002"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Circuits Syst. Video Technol."],"published-print":{"date-parts":[[2023,12]]},"DOI":"10.1109\/tcsvt.2023.3277689","type":"journal-article","created":{"date-parts":[[2023,5,18]],"date-time":"2023-05-18T17:28:03Z","timestamp":1684430883000},"page":"7197-7211","source":"Crossref","is-referenced-by-count":20,"title":["Skeleton Neural Networks via Low-Rank Guided Filter Pruning"],"prefix":"10.1109","volume":"33","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8555-5387","authenticated-orcid":false,"given":"Liu","family":"Yang","sequence":"first","affiliation":[{"name":"Tianjin Key Laboratory of Machine Learning, Ministry of Education, the Engineering Research Center of City Intelligence and Digital Governance, and the College of Intelligence and Computing, Tianjin University, Tianjin, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-6402-3299","authenticated-orcid":false,"given":"Shiqiao","family":"Gu","sequence":"additional","affiliation":[{"name":"SenseTime Research, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6490-6555","authenticated-orcid":false,"given":"Chenyang","family":"Shen","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology, Division of Medical Physics and Engineering, University of Texas Southwestern Medical Center, Dallas, TX, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6540-946X","authenticated-orcid":false,"given":"Xile","family":"Zhao","sequence":"additional","affiliation":[{"name":"Research Center for Image and Vision Computing, School of Mathematical Sciences, University of Electronic Science and Technology of China, Chengdu, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7765-8095","authenticated-orcid":false,"given":"Qinghua","family":"Hu","sequence":"additional","affiliation":[{"name":"Tianjin Key Laboratory of Machine Learning, Ministry of Education, the Engineering Research Center of City Intelligence and Digital Governance, and the College of Intelligence and Computing, Tianjin University, Tianjin, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.683"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.713"},{"key":"ref3","article-title":"YOLOv3: An incremental improvement","author":"Redmon","year":"2018","journal-title":"arXiv:1804.02767"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref6","article-title":"8-bit approximations for parallelism in deep learning","author":"Dettmers","year":"2015","journal-title":"arXiv:1511.04561"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00881"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/TCOMM.2020.2977280"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2021.3064293"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/tnnls.2021.3069886"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2022.3216389"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00145"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.205"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2019.2936742"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2021.3086590"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2021.3056895"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2021.3090902"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00092"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00306"},{"key":"ref20","first-page":"576","article-title":"Compression of deep convolutional neural networks for fast and low power mobile applications","author":"Kim","year":"2015","journal-title":"Comput. Sci."},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2015.2502579"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/BIGCOMP.2017.7881725"},{"key":"ref23","first-page":"856","article-title":"Compression-aware training of deep networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Alvarez"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2020\/136"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/tcsvt.2022.3216101"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2022.3156588"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2022.3175762"},{"key":"ref28","article-title":"Pruning filters for efficient ConvNets","author":"Li","year":"2016","journal-title":"arXiv:1608.08710"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.541"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.155"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.01152"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00958"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/WACV.2019.00129"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00160"},{"key":"ref35","article-title":"Network pruning that matters: A case study on retraining variants","author":"Le","year":"2021","journal-title":"arXiv:2105.03193"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.298"},{"key":"ref37","first-page":"2074","article-title":"Learning structured sparsity in deep neural networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Wen"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2018\/309"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2019.2933477"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00447"},{"key":"ref41","first-page":"1","article-title":"Learning both weights and connections for efficient neural network","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Han"},{"key":"ref42","article-title":"Deep compression: Compressing deep neural networks with pruning, trained quantization and Huffman coding","author":"Han","year":"2015","journal-title":"arXiv:1510.00149"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58580-8_35"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00915"},{"key":"ref45","first-page":"5122","article-title":"Operation-aware soft channel pruning using differentiable masks","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Kang"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/tnnls.2022.3149332"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1201\/9781315140919"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/cvpr.2008.4587747"},{"key":"ref49","first-page":"1","article-title":"Automatic differentiation in PyTorch","volume-title":"Proc. 31st Conf. Neural Inf. Process. Syst.","author":"Paszke"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46493-0_38"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.5244\/C.30.87"}],"container-title":["IEEE Transactions on Circuits and Systems for Video Technology"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/76\/10348119\/10129179.pdf?arnumber=10129179","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,12,20]],"date-time":"2023-12-20T01:23:37Z","timestamp":1703035417000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10129179\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12]]},"references-count":51,"journal-issue":{"issue":"12"},"URL":"https:\/\/doi.org\/10.1109\/tcsvt.2023.3277689","relation":{},"ISSN":["1051-8215","1558-2205"],"issn-type":[{"value":"1051-8215","type":"print"},{"value":"1558-2205","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,12]]}}}