{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T09:43:07Z","timestamp":1780047787308,"version":"3.53.1"},"reference-count":83,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":["U1713213"],"award-info":[{"award-number":["U1713213"]}],"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":["61772508"],"award-info":[{"award-number":["61772508"]}],"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":["U1913202"],"award-info":[{"award-number":["U1913202"]}],"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":["U1813205"],"award-info":[{"award-number":["U1813205"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Shenzhen Technology Project","award":["JCYJ20180507182610734"],"award-info":[{"award-number":["JCYJ20180507182610734"]}]},{"name":"Shenzhen Technology Project","award":["JSGG20191129094012321"],"award-info":[{"award-number":["JSGG20191129094012321"]}]},{"DOI":"10.13039\/501100012658","name":"3D Digital Media Technology Engineering Laboratory","doi-asserted-by":"publisher","award":["[2017]476"],"award-info":[{"award-number":["[2017]476"]}],"id":[{"id":"10.13039\/501100012658","id-type":"DOI","asserted-by":"publisher"}]},{"name":"CAS Key Technology Talent Program"},{"DOI":"10.13039\/501100013289","name":"Shenzhen Institutes of Advanced Technology Innovation Program for Excellent Young Researchers","doi-asserted-by":"publisher","award":["E1G032"],"award-info":[{"award-number":["E1G032"]}],"id":[{"id":"10.13039\/501100013289","id-type":"DOI","asserted-by":"publisher"}]},{"name":"ARC","award":["DP-180103424"],"award-info":[{"award-number":["DP-180103424"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Multimedia"],"published-print":{"date-parts":[[2023]]},"DOI":"10.1109\/tmm.2021.3123813","type":"journal-article","created":{"date-parts":[[2021,10,29]],"date-time":"2021-10-29T19:21:29Z","timestamp":1635535289000},"page":"191-202","source":"Crossref","is-referenced-by-count":22,"title":["Mixer-Based Semantic Spread for Few-Shot Learning"],"prefix":"10.1109","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3131-3275","authenticated-orcid":false,"given":"Jun","family":"Cheng","sequence":"first","affiliation":[{"name":"CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1490-2235","authenticated-orcid":false,"given":"Fusheng","family":"Hao","sequence":"additional","affiliation":[{"name":"CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5584-2385","authenticated-orcid":false,"given":"Fengxiang","family":"He","sequence":"additional","affiliation":[{"name":"JD Explore Academy, JD.com, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8128-2788","authenticated-orcid":false,"given":"Liu","family":"Liu","sequence":"additional","affiliation":[{"name":"UBTECH Sydney Artificial Intelligence Centre, School of Computer Science, Faculty of Engineering, The University of Sydney, Darlington, NSW, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6358-1840","authenticated-orcid":false,"given":"Qieshi","family":"Zhang","sequence":"additional","affiliation":[{"name":"CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.5555\/2999134.2999257"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2018.2855081"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.322"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2018.2890360"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00813"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2018.2869277"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00131"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2006.79"},{"key":"ref9","first-page":"3630","article-title":"Matching networks for one shot learning","volume-title":"Adv. Neural Inf. Process. Syst.","author":"Oriol","year":"2016"},{"key":"ref10","first-page":"719","article-title":"Tadam: Task dependent adaptive metric for improved few-shot learning","volume-title":"Adv. Neural Inf. Process. Syst.","author":"Oreshkin","year":"2018"},{"key":"ref11","first-page":"4077","article-title":"Prototypical networks for few-shot learning","volume-title":"Adv. Neural Inf. Process. Syst.","author":"Jake","year":"2017"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00743"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33018642"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2020\/409"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2020.3039329"},{"key":"ref16","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":"ref17","article-title":"Meta-SGD: Learning to learn quickly for few-shot learning","author":"Li","year":"2017"},{"key":"ref18","article-title":"A simple neural attentive meta-learner","volume-title":"Int. Conf. Learn. Representations","author":"Mishra","year":"2018"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00855"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.14711\/thesis-991012636368103412"},{"key":"ref21","first-page":"2371","article-title":"Generative adversarial nets","volume-title":"Adv. Neural Inf. Process. Syst.","author":"Goodfellow","year":"2014"},{"key":"ref22","article-title":"Generative adversarial residual pairwise networks for one shot learning","author":"Mehrotra","year":"2017"},{"key":"ref23","article-title":"Few-shot learning on graphs via super-classes based on graph spectral measures","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Chauhan","year":"2020"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00011"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.2979745"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2020.3001510"},{"key":"ref27","article-title":"Deep comparison: relation columns for few-shot learning","author":"Zhang","year":"2018"},{"key":"ref28","first-page":"10457","article-title":"Local propagation for few-shot learning","volume-title":"Proc. Int. Conf. Pattern Recognit.","author":"Yann","year":"2021"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01222"},{"key":"ref30","article-title":"Meta-learning with latent embedding optimization","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Rusu","year":"2019"},{"key":"ref31","article-title":"Optimization as a model for few-shot learning","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Ravi","year":"2017"},{"key":"ref32","article-title":"Meta-learning for semi-supervised few-shot classification","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Ren","year":"2018"},{"issue":"CNS-TR-2011-001","key":"ref33","article-title":"The caltech-ucsd birds-200-2011 dataset","volume-title":"Computation Neural Syst. Tech. Rep.","author":"Wah","year":"2011"},{"key":"ref34","article-title":"On first-order meta-learning algorithms","author":"Nichol","year":"2018"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.01091"},{"key":"ref36","article-title":"Meta-learning with differentiable closed-form solvers","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Bertinetto","year":"2019"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01238"},{"key":"ref38","article-title":"Siamese neural networks for one-shot image recognition","volume-title":"ICML Deep Learning Workshop,","volume":"2","author":"Koch","year":"2015"},{"key":"ref39","first-page":"3664","article-title":"Rapid adaptation with conditionally shifted neurons","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Munkhdalai","year":"2018"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00429"},{"key":"ref41","article-title":"Discriminative k-shot learning using probabilistic models","author":"Bauer","year":"2017"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01259"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58548-8_26"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58568-6_16"},{"key":"ref45","first-page":"232","article-title":"Infinite mixture prototypes for few-shot learning","volume-title":"Int. Conf. Mach. Learn.","author":"Allen","year":"2019"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00755"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00459"},{"key":"ref48","first-page":"4136","article-title":"Adaptive subspaces for few-shot learning","volume-title":"Proc. IEEE Conf. Comput. Vis. Pattern Recognit.","author":"Christian","year":"2020"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58592-1_8"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2906665"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.492"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00948"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.328"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00760"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58571-6_38"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01285"},{"key":"ref57","first-page":"2218","article-title":"Charting the right manifold: Manifold mixup for few-shot learning","volume-title":"Proc. IEEE Winter Conf. Appl. Comput. Vis.","author":"Puneet","year":"2020"},{"key":"ref58","first-page":"121","article-title":"Embedding propagation: Smoother manifold for few-shot classification","volume-title":"Proc. Eur. Conf. Comput. Vis.","author":"Pau","year":"2020"},{"key":"ref59","article-title":"Few-shot learning with graph neural networks","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Victor","year":"2018"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00010"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01340"},{"key":"ref62","article-title":"Transductive propagation network for few-shot learning","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Liu","year":"2019"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref64","article-title":"Few-shot learning with metric-agnostic conditional embeddings","author":"Hilliard","year":"2018"},{"key":"ref65","article-title":"Adam: A method for stochastic optimization","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Kingma","year":"2015"},{"key":"ref66","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00009"},{"key":"ref68","article-title":"A closer look at few-shot classification","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Chen","year":"2019"},{"key":"ref69","article-title":"Recasting gradient-based meta-learning as hierarchical bayes","volume-title":"Int. Conf. Learn. Representations","author":"Grant","year":"2018"},{"key":"ref70","first-page":"9537","article-title":"Probabilistic model-agnostic meta-learning","volume-title":"Adv. Neural Inf. Process. Syst.","author":"Finn","year":"2018"},{"key":"ref71","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.2984710"},{"key":"ref72","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33019079"},{"key":"ref73","article-title":"Meta-learning probabilistic inference for prediction","volume-title":"Int. Conf. Learn. Representations","author":"Gordon","year":"2019"},{"key":"ref74","article-title":"Incremental few-shot learning with attention attractor networks","author":"Ren","year":"2018"},{"key":"ref75","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00049"},{"key":"ref76","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2021.107935"},{"key":"ref77","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00883"},{"key":"ref78","first-page":"2554","article-title":"Meta networks","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Munkhdalai","year":"2017"},{"key":"ref79","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58517-4_24"},{"key":"ref80","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2020.2993952"},{"key":"ref81","first-page":"2445","article-title":"Information maximization for few-shot learning","volume-title":"Adv. Neural Inf. Process. Syst.","author":"Malik","year":"2020"},{"key":"ref82","article-title":"Empirical Bayes transductive meta-learning with synthetic gradients","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Hu","year":"2020"},{"key":"ref83","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58452-8_43"},{"key":"ref84","first-page":"2734","article-title":"Interventional few-shot learning","volume-title":"Adv. Neural Inf. Process. Syst.","author":"Yue","year":"2020"}],"container-title":["IEEE Transactions on Multimedia"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6046\/10016790\/09594692.pdf?arnumber=9594692","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,12]],"date-time":"2024-01-12T02:14:54Z","timestamp":1705025694000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9594692\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"references-count":83,"URL":"https:\/\/doi.org\/10.1109\/tmm.2021.3123813","relation":{},"ISSN":["1520-9210","1941-0077"],"issn-type":[{"value":"1520-9210","type":"print"},{"value":"1941-0077","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]}}}