{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T16:15:05Z","timestamp":1781021705773,"version":"3.54.1"},"reference-count":82,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"12","license":[{"start":{"date-parts":[[2024,12,1]],"date-time":"2024-12-01T00:00:00Z","timestamp":1733011200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2024,12,1]],"date-time":"2024-12-01T00:00:00Z","timestamp":1733011200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,12,1]],"date-time":"2024-12-01T00:00:00Z","timestamp":1733011200000},"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":["62121001"],"award-info":[{"award-number":["62121001"]}],"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":["U22B2014"],"award-info":[{"award-number":["U22B2014"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Youth Talent Promotion Project of China Association for Science and Technology","award":["2020QNRC001"],"award-info":[{"award-number":["2020QNRC001"]}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["QTZX23048"],"award-info":[{"award-number":["QTZX23048"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"Innovation Fund of Xidian University","doi-asserted-by":"publisher","award":["20109235456"],"award-info":[{"award-number":["20109235456"]}],"id":[{"id":"10.13039\/501100001809","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":[[2024,12]]},"DOI":"10.1109\/tcsvt.2024.3437182","type":"journal-article","created":{"date-parts":[[2024,8,1]],"date-time":"2024-08-01T18:36:02Z","timestamp":1722537362000},"page":"12837-12848","source":"Crossref","is-referenced-by-count":9,"title":["Markov-PQ: Joint Pruning-Quantization via Learnable Markov Chain"],"prefix":"10.1109","volume":"34","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0234-6270","authenticated-orcid":false,"given":"Yunsong","family":"Li","sequence":"first","affiliation":[{"name":"State Key Laboratory of Integrated Services Networks, Xidian University, Xi&#x2019;an, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6455-047X","authenticated-orcid":false,"given":"Xin","family":"Zhang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Integrated Services Networks, Xidian University, Xi&#x2019;an, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8310-024X","authenticated-orcid":false,"given":"Weiying","family":"Xie","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Integrated Services Networks, Xidian University, Xi&#x2019;an, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiaqing","family":"Zhang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Integrated Services Networks, Xidian University, Xi&#x2019;an, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2351-4461","authenticated-orcid":false,"given":"Leyuan","family":"Fang","sequence":"additional","affiliation":[{"name":"College of Electrical and Information Engineering, Hunan University, Changsha, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4734-9798","authenticated-orcid":false,"given":"Jiawei","family":"Du","sequence":"additional","affiliation":[{"name":"Centre for Frontier AI Research (CFAR), Agency for Science, Technology and Research (A*STAR), Fusionopolis, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","first-page":"1","article-title":"Revisiting efficient object detection backbones from zero-shot neural architecture search","volume-title":"Proc. Int. Conf. Mach. Learn. (ICML)","author":"Sun"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2022.3202213"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2022.3209160"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00978"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00096"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW53098.2021.00141"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/ARMS.1988.196463"},{"key":"ref8","first-page":"1","article-title":"Learning both weights and connections for efficient neural network","volume-title":"Proc. Adv. Neural Inf. Process. Syst. (NeurIPS)","author":"Han"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00447"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00958"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/tnnls.2023.3244994"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2023.3248659"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2023.3277689"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2022.3156588"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i4.16462"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01210"},{"key":"ref17","first-page":"9295","article-title":"SDQ: Stochastic differentiable quantization with mixed precision","volume-title":"Proc. Int. Conf. Mach. Learn. (ICML)","author":"Huang"},{"key":"ref18","first-page":"1","article-title":"Pruning vs quantization: Which is better?","volume-title":"Proc. Adv. Neural Inf. Process. Syst. (NeurIPS)","author":"Kuzmin"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2022.3216389"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2023.3279281"},{"key":"ref21","first-page":"1","article-title":"Deep compression: Compressing deep neural networks with pruning, trained quantization and Huffman coding","volume-title":"Proc. Int. Conf. Learn. Represent. (ICLR)","author":"Han"},{"key":"ref22","article-title":"Design automation for efficient deep learning computing","author":"Han","year":"2019","journal-title":"arXiv:1904.10616"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.21437\/Interspeech.2021-248"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00225"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00215"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58526-6_16"},{"key":"ref27","first-page":"1","article-title":"Bayesian bits: Unifying quantization and pruning","volume-title":"Proc. Adv. Neural Inf. Process. Syst. (NeurIPS)","volume":"33","author":"van Baalen"},{"key":"ref28","article-title":"Single-path bit sharing for automatic loss-aware model compression","author":"Liu","year":"2021","journal-title":"arXiv:2101.04935"},{"key":"ref29","article-title":"Automated model compression by jointly applied pruning and quantization","author":"Tang","year":"2020","journal-title":"arXiv:2011.06231"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00160"},{"key":"ref31","first-page":"1","article-title":"Network pruning via transformable architecture search","volume-title":"Proc. Adv. Neural Inf. Process. Syst. (NeurIPS)","author":"Dong"},{"key":"ref32","article-title":"Pruning filters for efficient ConvNets","author":"Li","year":"2016","journal-title":"arXiv:1608.08710"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2021.3124284"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/tnnls.2023.3306512"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2023.3288986"},{"key":"ref36","first-page":"1","article-title":"Trained ternary quantization","volume-title":"Proc. Int. Conf. Learn. Represent. (ICLR)","author":"Zhu"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00638"},{"key":"ref38","article-title":"DoReFa-Net: Training low bitwidth convolutional neural networks with low bitwidth gradients","author":"Zhou","year":"2016","journal-title":"arXiv:1606.06160"},{"key":"ref39","first-page":"1","article-title":"Searching for low-bit weights in quantized neural networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst. (NeurIPS)","author":"Yang"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00495"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00524"},{"key":"ref42","first-page":"11875","article-title":"HAWQ-V3: Dyadic neural network quantization","volume-title":"Proc. Int. Conf. Mach. Learn","author":"Yao"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/TETC.2021.3050770"},{"key":"ref44","article-title":"Joint pruning & quantization for extremely sparse neural networks","author":"Yu","year":"2020","journal-title":"arXiv:2010.01892"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00821"},{"key":"ref46","article-title":"Categorical reparameterization with Gumbel\u2013Softmax","author":"Jang","year":"2016","journal-title":"arXiv:1611.01144"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2022.3156047"},{"key":"ref48","first-page":"1","article-title":"Provable filter pruning for efficient neural networks","volume-title":"Proc. Int. Conf. Learn. Represent. (ICLR)","author":"Liebenwein"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00289"},{"key":"ref50","article-title":"Complexity-driven CNN compression for resource-constrained edge AI","author":"Zawish","year":"2022","journal-title":"arXiv:2208.12816"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00519"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1109\/tnnls.2023.3246263"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2021.3084206"},{"key":"ref54","first-page":"1","article-title":"CHIP: CHannel independence-based pruning for compact neural networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst. (NeurIPS)","author":"Sui"},{"key":"ref55","first-page":"10717","article-title":"Accelerate CNNs from three dimensions: A comprehensive pruning framework","volume-title":"Proc. Int. Conf. Mach. Learn. (ICML)","author":"Wang"},{"key":"ref56","article-title":"Mixed precision quantization of ConvNets via differentiable neural architecture search","author":"Wu","year":"2018","journal-title":"arXiv:1812.00090"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2023.3236336"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2022.3165123"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00161"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2022.3149332"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01237-3_23"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00522"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00748"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.00769"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"ref66","article-title":"Learning multiple layers of features from tiny images","author":"Krizhevsky","year":"2009"},{"key":"ref67","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref68","article-title":"Very deep convolutional networks for large-scale image recognition","author":"Simonyan","year":"2014","journal-title":"arXiv:1409.1556"},{"key":"ref69","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref70","first-page":"1","article-title":"ImageNet classification with deep convolutional neural networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst. (NeurIPS)","author":"Krizhevsky"},{"key":"ref71","article-title":"PACT: Parameterized clipping activation for quantized neural networks","author":"Choi","year":"2018","journal-title":"arXiv:1805.06085"},{"key":"ref72","first-page":"1","article-title":"Ternary weight networks","volume-title":"Proc. Int. Conf. Learn. Represent. (ICLR)","author":"Li"},{"key":"ref73","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00474"},{"key":"ref74","first-page":"10347","article-title":"Training data-efficient image transformers & distillation through attention","volume-title":"Proc. Int. Conf. Learn. Represent. (ICLR)","author":"Touvron"},{"key":"ref75","first-page":"1","article-title":"Discrimination-aware channel pruning for deep neural networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst. (NeruaIPS)","volume":"31","author":"Zhuang"},{"key":"ref76","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00204"},{"key":"ref77","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00520"},{"key":"ref78","first-page":"9010","article-title":"SAViT: Structure-aware vision transformer pruning via collaborative optimization","volume-title":"Proc. Adv. Neural Inf. Process. Syst. (NeruaIPS)","volume":"35","author":"Zheng"},{"key":"ref79","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i3.20222"},{"key":"ref80","first-page":"28092","article-title":"Post-training quantization for vision transformer","volume-title":"Proc. Adv. Neural Inf. Process. Syst. (NeruaIPS)","volume":"34","author":"Liu"},{"key":"ref81","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.01565"},{"key":"ref82","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2022.3229313"}],"container-title":["IEEE Transactions on Circuits and Systems for Video Technology"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/76\/10811772\/10620340.pdf?arnumber=10620340","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,15]],"date-time":"2025-01-15T20:20:37Z","timestamp":1736972437000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10620340\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12]]},"references-count":82,"journal-issue":{"issue":"12"},"URL":"https:\/\/doi.org\/10.1109\/tcsvt.2024.3437182","relation":{},"ISSN":["1051-8215","1558-2205"],"issn-type":[{"value":"1051-8215","type":"print"},{"value":"1558-2205","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12]]}}}