{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,24]],"date-time":"2026-01-24T13:45:51Z","timestamp":1769262351778,"version":"3.49.0"},"reference-count":63,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"1","license":[{"start":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T00:00:00Z","timestamp":1769904000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T00:00:00Z","timestamp":1769904000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T00:00:00Z","timestamp":1769904000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100012476","name":"Fundamental Research Funds for Central Universities of the Central South University","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100012476","id-type":"DOI","asserted-by":"publisher"}]},{"name":"High Performance Computing Center of Central South University"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Emerg. Top. Comput. Intell."],"published-print":{"date-parts":[[2026,2]]},"DOI":"10.1109\/tetci.2025.3592937","type":"journal-article","created":{"date-parts":[[2025,8,25]],"date-time":"2025-08-25T20:49:06Z","timestamp":1756154946000},"page":"468-479","source":"Crossref","is-referenced-by-count":0,"title":["Global and Local Attention-Based Multiscale Prototypical Network for Few-Shot Learning"],"prefix":"10.1109","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-1896-6457","authenticated-orcid":false,"given":"Qianhao","family":"Yu","sequence":"first","affiliation":[{"name":"School of Automation, Central South University, Changsha, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7670-3958","authenticated-orcid":false,"given":"Yong","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Automation, Central South University, Changsha, China"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/TETCI.2018.2866254"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-015-0816-y"},{"key":"ref3","article-title":"Learning from very few samples: A survey","author":"Lu","year":"2020"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TETCI.2019.2948151"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.5555\/3294996.3295163"},{"key":"ref6","first-page":"3637","article-title":"Matching networks for one shot learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Vinyals","year":"2016"},{"key":"ref7","first-page":"1126","article-title":"Model-agnostic meta-learning for fast adaptation of deep networks","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Finn","year":"2017"},{"key":"ref8","article-title":"Optimization as a model for few-shot learning","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Ravi","year":"2017"},{"key":"ref9","article-title":"Meta-SGD:Learning to learn quickly for few-shot learning","author":"Li","year":"2017"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00049"},{"key":"ref11","article-title":"Meta-learning with latent embedding optimization","author":"Rusu","year":"2018"},{"key":"ref12","article-title":"A simple neural attentive meta-learner","author":"Mishra","year":"2017"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2020.04.040"},{"key":"ref14","first-page":"719","article-title":"Tadam: Task dependent adaptive metric for improved few-shot learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Oreshkin","year":"2018"},{"key":"ref15","first-page":"2927","article-title":"Gradient-based meta-learning with learned layerwise metric and subspace","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Lee","year":"2018"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2020.2995754"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2023.3238804"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/TETCI.2022.3232816"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00131"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.3390\/technologies11020040"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1186\/s40537-016-0043-6"},{"key":"ref23","first-page":"3664","article-title":"Rapid adaptation with conditionally shifted neurons","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Munkhdalai","year":"2018"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2019.2957187"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.3011526"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2020.106609"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00009"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2023.3248798"},{"key":"ref29","article-title":"An image is worth 16x16 words: Transformers for image recognition at scale","author":"Dosovitskiy","year":"2020"},{"key":"ref30","article-title":"A universal representation transformer layer for few-shot image classification","author":"Liu","year":"2020"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00883"},{"key":"ref32","first-page":"21981","article-title":"Crosstransformers: Spatially-aware few-shot transfer","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Doersch","year":"2020"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-20044-1_19"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2021.107935"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2022.3170727"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00855"},{"key":"ref37","first-page":"10852","article-title":"XtarNet: Learning to extract task-adaptive representation for incremental few-shot learning","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Yoon","year":"2020"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/TETCI.2018.2868061"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2021.3083650"},{"key":"ref40","first-page":"23","article-title":"Task similarity aware meta learning: Theory-inspired improvement on maml","volume-title":"Proc. Uncertainty Artif. Intell.","author":"Zhou","year":"2021"},{"key":"ref41","first-page":"20755","article-title":"Meta-learning with adaptive hyperparameters","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Baik","year":"2020"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"ref43","article-title":"Transductive propagation network for few-shot learning","author":"Liu","year":"2018"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01345"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2022.108979"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1162\/neco_a_01423"},{"key":"ref47","article-title":"Meta-learning for semi-supervised few-shot classification","author":"Ren","year":"2018"},{"key":"ref48","article-title":"The caltech-Ucsd birds-200-2011 dataset","author":"Wah","year":"2011"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01222"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2021.3132912"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2021\/313"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58558-7_2"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/WACV56688.2023.00541"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00534"},{"key":"ref55","first-page":"7115","article-title":"TapNet: Neural network augmented with task-adaptive projection for few-shot learning","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Yoon","year":"2019"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.01091"},{"key":"ref57","article-title":"Multi-head self-attention via vision transformer for zero-shot learning","author":"Alamri","year":"2021"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i1.19909"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2022.109270"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2019.2947780"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.1807.06521"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00326"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00986"}],"container-title":["IEEE Transactions on Emerging Topics in Computational Intelligence"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/7433297\/11361307\/11137389.pdf?arnumber=11137389","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,23]],"date-time":"2026-01-23T21:01:27Z","timestamp":1769202087000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11137389\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2]]},"references-count":63,"journal-issue":{"issue":"1"},"URL":"https:\/\/doi.org\/10.1109\/tetci.2025.3592937","relation":{},"ISSN":["2471-285X"],"issn-type":[{"value":"2471-285X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2]]}}}