{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T04:19:58Z","timestamp":1780460398979,"version":"3.54.1"},"reference-count":37,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,1,12]],"date-time":"2026-01-12T00:00:00Z","timestamp":1768176000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2026,1,12]],"date-time":"2026-01-12T00:00:00Z","timestamp":1768176000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62006071"],"award-info":[{"award-number":["62006071"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Science and Technology Research Project of Henan Province","award":["232103810086"],"award-info":[{"award-number":["232103810086"]}]},{"name":"Henan University of Technology Grain Information Processing Center","award":["KFJJ2023010"],"award-info":[{"award-number":["KFJJ2023010"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Hum-Cent Intell Syst"],"DOI":"10.1007\/s44230-025-00129-y","type":"journal-article","created":{"date-parts":[[2026,1,12]],"date-time":"2026-01-12T14:47:54Z","timestamp":1768229274000},"page":"17-34","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["LE-HRNet: A Lightweight High-Resolution Network for Efficient Human Pose Estimation"],"prefix":"10.1007","volume":"6","author":[{"given":"Weiya","family":"Shi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinyu","family":"Dong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Changjiang","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Saiyang","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yue","family":"Guo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,1,12]]},"reference":[{"key":"129_CR1","doi-asserted-by":"crossref","unstructured":"Liu H, Wen X, Ye X, Zhang W. Progressive dual-branch transformer-based diffusion model: a novel approach for robust 2D human pose estimation. The Visual Computer (prepublish), p. 1\u201316, 2025.","DOI":"10.1007\/s00371-025-04066-6"},{"key":"129_CR2","doi-asserted-by":"crossref","unstructured":"Toshev A, Szegedy C. DeepPose: human pose estimation via deep neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, p. 1653\u201360, 2014.","DOI":"10.1109\/CVPR.2014.214"},{"key":"129_CR3","doi-asserted-by":"crossref","unstructured":"Cao Z, Simon T, Wei S-E, Sheikh Y. Realtime multi-person 2D pose estimation using part affinity fields. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, p. 7291\u20139, 2017.","DOI":"10.1109\/CVPR.2017.143"},{"key":"129_CR4","unstructured":"Newell A, Huang Z, Deng J. Associative embedding: end-to-end learning for joint detection and grouping. In: Advances in neural information processing systems, vol. 30. 2017."},{"key":"129_CR5","doi-asserted-by":"crossref","unstructured":"Nie X, Feng J, Zhang J, Yan S. Single-stage multi-person pose machines. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, p. 6951\u201360, 2019.","DOI":"10.1109\/ICCV.2019.00705"},{"key":"129_CR6","doi-asserted-by":"crossref","unstructured":"Chen Y, Wang Z, Peng Y, Zhang Z, Yu G, Sun J. Cascaded pyramid network for multi-person pose estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, p. 7103\u201312, 2018.","DOI":"10.1109\/CVPR.2018.00742"},{"key":"129_CR7","doi-asserted-by":"crossref","unstructured":"Li J, Wang C, Zhu H, Mao Y, Fang H-S, Lu C. CrowdPose: efficient crowded scenes pose estimation and a new benchmark. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, p. 10863\u201372, 2019.","DOI":"10.1109\/CVPR.2019.01112"},{"key":"129_CR8","doi-asserted-by":"crossref","unstructured":"Xiao B, Wu H, Wei Y. Simple baselines for human pose estimation and tracking. In: Proceedings of the European Conference on Computer Vision (ECCV), p. 466\u201381, 2018.","DOI":"10.1007\/978-3-030-01231-1_29"},{"key":"129_CR9","doi-asserted-by":"crossref","unstructured":"Wang Y, Li M, Cai H, Chen W-M, Han S. Lite pose: efficient architecture design for 2D human pose estimation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, p. 13126\u201336, 2022.","DOI":"10.1109\/CVPR52688.2022.01278"},{"key":"129_CR10","doi-asserted-by":"crossref","unstructured":"Sun K, Xiao B, Liu D, Wang J. Deep high-resolution representation learning for human pose estimation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, p. 5693\u2013703, 2019.","DOI":"10.1109\/CVPR.2019.00584"},{"key":"129_CR11","doi-asserted-by":"crossref","unstructured":"Yu C, Xiao B, Gao C, Yuan L, Zhang L, Sang N, Wang J. Lite-HRNet: a lightweight high-resolution network. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, p. 10440\u201350, 2021.","DOI":"10.1109\/CVPR46437.2021.01030"},{"key":"129_CR12","doi-asserted-by":"crossref","unstructured":"Cheng B, Xiao B, Wang J, Shi H, Huang TS, Zhang L. HigherHRNet: scale-aware representation learning for bottom-up human pose estimation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, p. 5386\u201395, 2020.","DOI":"10.1109\/CVPR42600.2020.00543"},{"key":"129_CR13","doi-asserted-by":"crossref","unstructured":"Neff C, Sheth A, Furgurson S, Tabkhi H. EfficientHRNet: efficient scaling for lightweight high-resolution multi-person pose estimation. 2020. arXiv:2007.08090.","DOI":"10.1007\/s11554-021-01132-9"},{"key":"129_CR14","unstructured":"Zhang Z, Tang J, Wu G. Simple and lightweight human pose estimation. 2019. arXiv:1911.10346."},{"key":"129_CR15","unstructured":"Jiang T, Lu P, Zhang L, Ma N, Han R, Lyu C, Li Y, Chen K. RTMPose: real-time multi-person pose estimation based on MMPose. 2023. arXiv:2303.07399."},{"issue":"1","key":"129_CR16","first-page":"110","volume":"29","author":"W Bao","year":"2020","unstructured":"Bao W, Yang Y, Liang D, Zhu M. Multi-residual module stacked hourglass networks for human pose estimation. J Beijing Inst Tech. 2020;29(1):110\u20139.","journal-title":"J Beijing Inst Tech"},{"key":"129_CR17","doi-asserted-by":"crossref","unstructured":"Zhang X, Zhou X, Lin M, Sun J. ShuffleNet: an extremely efficient convolutional neural network for mobile devices. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, p. 6848\u201356, 2018.","DOI":"10.1109\/CVPR.2018.00716"},{"key":"129_CR18","unstructured":"Howard AG, Zhu M, Chen B, Kalenichenko D, Wang W, Weyand T, Andreetto M, Adam H. MobileNets: efficient convolutional neural networks for mobile vision applications. 2017. arXiv:1704.04861."},{"key":"129_CR19","unstructured":"Howard AG, Zhu M, Chen B, Kalenichenko D, Wang W, Weyand T, Andreetto M, Adam H. MobileNets: efficient convolutional neural networks for mobile vision applications. 2017. arXiv:1704.04861."},{"key":"129_CR20","doi-asserted-by":"crossref","unstructured":"Sandler M, Howard A, Zhu M, Zhmoginov A, Chen L-C. MobileNetV2: inverted residuals and linear bottlenecks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, p. 4510\u201320, 2018.","DOI":"10.1109\/CVPR.2018.00474"},{"key":"129_CR21","doi-asserted-by":"crossref","unstructured":"Howard A, Sandler M, Chu G, Chen L-C, Chen B, Tan M, Wang W, Zhu Y, Pang R, Vasudevan V. Searching for MobileNetV3. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, p. 1314\u201324, 2019.","DOI":"10.1109\/ICCV.2019.00140"},{"key":"129_CR22","doi-asserted-by":"crossref","unstructured":"Ma N, Zhang X, Zheng H-T, Sun J. ShuffleNet V2: practical guidelines for efficient CNN architecture design. In: Proceedings of the European Conference on Computer Vision (ECCV), p. 116\u201331, 2018.","DOI":"10.1007\/978-3-030-01264-9_8"},{"key":"129_CR23","unstructured":"Iandola FN, Han S, Moskewicz MW, Ashraf K, Dally WJ, Keutzer K. SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and $$<$$ 0.5 mb model size. 2016. arXiv:1602.07360."},{"key":"129_CR24","unstructured":"Tan M, Le Q. EfficientNet: rethinking model scaling for convolutional neural networks. In: International Conference on Machine Learning. PMLR; 2019. p. 6105\u201314."},{"key":"129_CR25","unstructured":"Dosovitskiy A. An image is worth 16x16 words: transformers for image recognition at scale. 2020. arXiv:2010.11929."},{"key":"129_CR26","doi-asserted-by":"crossref","unstructured":"Liu Z, Lin Y, Cao Y, Hu H, Wei Y, Zhang Z, Lin S, Guo B. Swin transformer: hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, p. 10012\u201322, 2021.","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"129_CR27","doi-asserted-by":"crossref","unstructured":"Wang J, Chen K, Xu R, Liu Z, Loy CC, Lin D. CARAFE: content-aware reassembly of features. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, p. 3007\u201316, 2019.","DOI":"10.1109\/ICCV.2019.00310"},{"issue":"1","key":"129_CR28","doi-asserted-by":"publisher","first-page":"151","DOI":"10.1007\/s11263-024-02191-8","volume":"133","author":"H Lu","year":"2025","unstructured":"Lu H, Liu W, Fu H, Cao Z. FADE: a task-agnostic upsampling operator for encoder-decoder architectures. Int J Comput Vis. 2025;133(1):151\u201372.","journal-title":"Int J Comput Vis"},{"key":"129_CR29","first-page":"20889","volume":"35","author":"H Lu","year":"2022","unstructured":"Lu H, Liu W, Ye Z, Fu H, Liu Y, Cao Z. SAPA: similarity-aware point affiliation for feature upsampling. Adv Neural Inform Process Sys. 2022;35:20889\u2013901.","journal-title":"Adv Neural Inform Process Sys"},{"key":"129_CR30","doi-asserted-by":"crossref","unstructured":"Liu W, Lu H, Fu H, Cao Z. Learning to upsample by learning to sample. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, p. 6027\u201337, 2023.","DOI":"10.1109\/ICCV51070.2023.00554"},{"key":"129_CR31","doi-asserted-by":"crossref","unstructured":"Misra D, Nalamada T, Arasanipalai AU, Hou Q. Rotate to attend: convolutional triplet attention module. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, p. 3139\u201348, 2021.","DOI":"10.1109\/WACV48630.2021.00318"},{"key":"129_CR32","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2023.106442","volume":"123","author":"D Wan","year":"2023","unstructured":"Wan D, Lu R, Shen S, Xu T, Lang X, Ren Z. Mixed local channel attention for object detection. Eng Appl Artif Intell. 2023;123:106442.","journal-title":"Eng Appl Artif Intell"},{"key":"129_CR33","doi-asserted-by":"crossref","unstructured":"Andriluka M, Pishchulin L, Gehler P, Schiele B. 2D human pose estimation: new benchmark and state of the art analysis. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, p. 3686\u201393, 2014.","DOI":"10.1109\/CVPR.2014.471"},{"key":"129_CR34","doi-asserted-by":"crossref","unstructured":"Lin T-Y, Maire M, Belongie S, Hays J, Perona P, Ramanan D, Doll\u00e1r P, Zitnick CL. Microsoft COCO: common objects in context. In: European Conference on Computer Vision. Springer; 2014. p. 740\u201355.","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"129_CR35","unstructured":"Xu H, Yan S, Zheng W, Innov SY. Lightweight human pose estimation with enhanced knowledge review. In: BMVC, 2024."},{"issue":"2","key":"129_CR36","doi-asserted-by":"publisher","DOI":"10.3390\/s24020396","volume":"24","author":"R Li","year":"2024","unstructured":"Li R, Yan A, Yang S, He D, Zeng X, Liu H. Human pose estimation based on efficient and lightweight high-resolution network (EL-HRNet). Sensors. 2024;24(2):396. https:\/\/doi.org\/10.3390\/s24020396.","journal-title":"Sensors"},{"key":"129_CR37","doi-asserted-by":"publisher","unstructured":"Zhang F, Zhu X, Dai H, Ye M, Zhu C. Distribution-aware coordinate representation for human pose estimation. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, p. 7091\u2013100, 2020. https:\/\/doi.org\/10.1109\/CVPR42600.2020.00712.","DOI":"10.1109\/CVPR42600.2020.00712"}],"container-title":["Human-Centric Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s44230-025-00129-y","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44230-025-00129-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44230-025-00129-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T08:50:17Z","timestamp":1775551817000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s44230-025-00129-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,12]]},"references-count":37,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,3]]}},"alternative-id":["129"],"URL":"https:\/\/doi.org\/10.1007\/s44230-025-00129-y","relation":{},"ISSN":["2667-1336"],"issn-type":[{"value":"2667-1336","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,12]]},"assertion":[{"value":"12 September 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 November 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 December 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 January 2026","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no conflict of interest. None of the authors have registered as editors or reviewers in the editing system.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This study utilizes only publicly available benchmark datasets (COCO and MPII). These datasets have been de-identified and contain no personally identifiable information. As the research involves secondary analysis of existing, anonymized data, no separate ethical approval was required.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"All authors have provided their consent for the publication of this manuscript.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for participation and publication"}}]}}